In the grand narrative of human history, there have been pivotal moments that reshaped our societies and propelled us into new eras of progress. From the Agricultural Revolution to the Industrial Revolution, these milestones have marked significant shifts in the way we live, work, and interact with the world around us. Today, we stand on the precipice of another transformative moment: the AI Revolution.

As businesses grapple with artificial intelligence’s implications and potential to change industries, the question arises: What role do we, as tech specialists, play in this monumental shift?

Timspark initiated a conversation with Carl Eidsgaard, Head of AI Advisory & Solutions, to cover the most intriguing AI questions. From Carl’s perspective, the answer lies in understanding the phases of AI adoption and transformation and navigating their challenges and opportunities.

But there’s more to unpack, so keep exploring the full interview below.

About Carl Eidsgaard, AI consultant and Explorer

Hanna Strashynskaya (CMO at Timspark): Could you share a bit about yourself and how you got into AI?

Carl Eidsgaard: My name is Carl Eidsgaard. Originally from Norway, I’ve called Amsterdam home for the past seven years, working primarily in data analytics and AI projects with companies like Oracle and Microsoft. However, my fascination with AI spans over a decade, sparked by Ray Kurzweil’s groundbreaking book, ‘The Singularity is Near,’ which I encountered during my time in business school around 2012-2013.

Kurzweil’s vision of AI’s potential societal impact prompted me to pivot from a finance career to pursue opportunities in technology. I consider myself an AI explorer, rather than an AI consultant, navigating this new frontier with a blend of technical insight and a passion for demystifying AI for others. Despite over a decade of immersion in this field, I’m continually humbled by its rapid advancements and the profound implications it holds for society.

Carl: As for the technical side of things, the past 15 years have witnessed extensive research laying the groundwork for today’s AI landscape, a realm I’ve passionately explored for the past decade. Notable milestones, such as the creation of AlphaGo by Google DeepMind in 2013, marked significant leaps in AI’s capabilities, particularly in navigating complex scenarios like the game of Go. Unlike previous victories, such as IBM’s chess triumph in the 1980s, AlphaGo’s success showcased the power of machine learning and its ability to surpass human expertise.

Since then, the pace of advancement has exceeded my expectations. From the unveiling of ChatGPT in late 2022 to the present day, progress has been nothing short of astonishing. Witnessing this evolution every week invokes a spectrum of emotions, from grappling with existential questions to harboring boundless optimism for the future.

I see an evident potential for this to transform society and human civilization as a whole.

The impact of AI adoption on business

Hanna: What’s your plan for leveraging AI to shape our society and future? And how do you see yourself contributing to AI adoption and transformation in businesses?

Carl: That’s a fascinating question! Considering the rapid advancement of AI and its potential to render roles like mine obsolete in the future, I entered AI consulting fully aware of this eventuality. However, before reaching that point, it’s crucial to understand the various phases of AI adoption.

Currently, we’re in the informational phase, where businesses and individuals are exploring the capabilities of AI and its potential applications. The possibilities are vast, ranging from enhancing productivity to upgrading problem-solving approaches. This leads us to the next phase: adoption. Technologies like ChatGPT represent a significant leap, effectively serving as vast repositories of human knowledge accessible at our fingertips. Integrating such systems into our workflow can augment our capabilities, improving work quality and expanding our understanding of the world.

While the prospect of AI eventually autonomously handling tasks we currently do is on the horizon, there are significant stages of adoption and adaptation to navigate before that becomes a reality.

Hanna: How do you usually evaluate a company’s readiness for adopting AI?

Carl: AI adoption indeed varies greatly from one business to another, influenced by a myriad of factors, including cultural predispositions, existing infrastructure, and budgetary considerations. However, while each journey is unique, there are common traits indicative of readiness and progress in AI integration.

Firstly, I assess a company’s future-forward mindset. Are they cognizant of the seismic shift AI represents, not just for their industry but for society as a whole? Recognizing AI’s existential implications is crucial for embracing its potential and navigating its impact effectively.

Secondly, adaptability is key. Given the rapid pace of AI advancement, organizations must be willing to evolve and pivot as new technologies emerge. Finally, C-level buy-in is instrumental here; a top-down approach to AI adoption significantly enhances the likelihood of success by fostering a culture of innovation and agility throughout the organization.

Hanna: Starting from the bottom and working up can be an option, too, but it’s usually a longer route, correct?

Carl: Yes, it is. And things tend to become easier overall. For instance, securing a budget for a ChatGPT subscription is typically much smoother when done at the C-suite level for a global organization compared to being an executive with a single business domain, right?

Having buy-in from the C-suite definitely streamlines the process.

Common metrics for AI ROI evaluation

Hanna: As an AI explorer and consultant, how do you assist businesses in defining and measuring the return on investment for AI implementations?

Carl: When considering return on investment and objectives in AI integration, it closely mirrors the framework of a typical digital transformation journey. Aligning with established practices, organizations focus on setting clear goals, defining key performance indicators, establishing baseline performance metrics, and monitoring progress both in the short and long term.

However, there are notable distinctions in implementing AI compared to traditional analytics or ERP/CRM systems. Firstly, AI can automate much of this process through the utilization of large language models (LLMs) trained specifically for the organization’s context. This contextual analytics fabric enables a more tailored and efficient approach to data analysis and decision-making.

Secondly, the AI revolution unfolds both incrementally and in waves, necessitating adaptation to its evolving landscape. This concept of an ‘intelligence explosion’ implies exponential growth, which humans may struggle to navigate effectively. Planning for both incremental advancements and transformative waves is essential for maximizing the benefits of AI adoption while mitigating potential disruptions, such as job displacement.

Most common challenges of AI adoption

Hanna: What are the common challenges that companies encounter when integrating AI into their current systems and processes?

Carl: AI adoption manifests uniquely in each organization, yet certain domains commonly shape this process. Firstly, there’s the technical landscape and legacy systems, an area particularly pertinent in enterprise settings where outdated infrastructure still persists. Legacy systems can present hurdles to automation, potentially impeding the integration of AI-driven workflows.

Secondly, data quality and management emerge as critical considerations, especially for companies developing their own AI models. A foundation of clean, reliable data is essential for effective AI implementation.

Moreover, fostering AI literacy within the organization is paramount. Without a fundamental understanding of AI principles, companies risk underutilizing its potential. Investing in AI education or consulting expertise can bridge this gap.

Ethical and regulatory concerns loom large in the AI landscape, too. Prioritizing ethical AI practices not only safeguards against potential harm but also enhances trust and reputation. While regulatory frameworks are gradually emerging, they often lag behind technological advancements. Therefore, organizations must proactively address ethical considerations and compliance, as waiting for regulatory mandates may lead to obsolescence.

Hanna: Let’s delve into the ethical challenges that businesses may face during AI implementation. Could you provide some real-world examples from your experience?

Carl: When embarking on AI implementation, considerations vary depending on the specific context. For organizations developing their own models, such as ad or contextual analytics frameworks, several key factors come into play.

First, addressing biases and ensuring fairness in datasets is the first thing to do. This involves mitigating biases within datasets, perhaps by extending them with synthetic data to create a more representative sample. Additionally, taking advantage of system prompts in large language models can help refine interactions and mitigate biases in user interactions.

Bias in large language models isn’t as dire as it seems initially. These models are typically trained on baseline datasets, which are readily available. During this training, the model learns a wide range of parameters, ensuring a balanced understanding. However, challenges arise when incorporating specific datasets, as inherent biases within them may skew results.

Again, one effective method to mitigate this is through synthetic data, which mimics real data points but ensures diversity. For instance, in a recruitment-based system biased towards hiring white males in their 50s, diversifying the dataset before training can prevent the model from perpetuating this bias.

Privacy and ethics are equally significant concerns, particularly in the absence of firm regulations. Adhering to the principle of treating others as you would like to be treated yourself serves as a guiding ethos. Ensuring robust security measures, such as segregating AI systems into dedicated environments, safeguards against potential vulnerabilities.

Transparency and explainability are essential aspects of AI development. Thorough documentation of processes and solutions facilitates comprehension and enables seamless knowledge transfer among team members. Organizational education efforts ensure that stakeholders understand the workings of AI systems and can contribute effectively to their deployment and utilization.

Hanna: To broaden our perspective, let’s touch on the common concern about AI replacing jobs. How do we address this ethical challenge?

Carl: Let’s take a step back and address the notion of job obsolescence or worker displacement. Currently, we’re not at a stage where this is imminent; there’s still time for transition. However, in the timeline of AI development, we’ve already reached one significant milestone: the implementation of AI with cognitive capabilities, such as ChatGPT, which excels in generating text and is improving in areas like video generation. 

The next milestone is artificial general intelligence, or AGI, where models will match the capabilities of the best human operators. Beyond that lies artificial superintelligence, or ASI, surpassing human abilities across all domains.

This timeline provides context for potential job displacement, but it’s important to note that we’re not there yet. Before reaching that point, society must adapt to AI. Quality assurance by human operators will be crucial in ensuring these models perform as intended. 

While this transformation is significant, it’s essential not to fear being replaced. If managed effectively, this process could lead to a society far superior to what we can currently envision, with possibilities such as more time spent with loved ones.

Hanna: What’s your message to companies and employees worried about being replaced by AI?

Carl: Imagine having the freedom to pursue your interests without the constraint of needing to work for a living. Furthermore, consider the broader implications for humanity as a whole. Embracing technologies like AI could be pivotal in our journey toward becoming an interplanetary species.

Reflect on what truly brings you happiness and use that as a guide moving forward. Personalized AI models, such as a customized GPT, could be designed solely to ensure your well-being financially, socially, and politically. They might even handle tasks like voting on your behalf. Picture a world where everyone could live comfortably, free from financial worries. You could travel, explore new experiences, and rely on your personal AI to educate you whenever needed.

While this future might seem daunting to some, humans possess remarkable adaptability. Consider whether traditional notions of job satisfaction still hold in a world where personal AI can handle many tasks. The potential for such advancements is monumental, reshaping how we live and work in ways we may struggle to imagine today.

AI compliance with industry standards and regulations

Hanna: How can AI implementations be ensured to comply with industry standards and regulations?

Carl: Currently, we lack forward-thinking regulations specific to AI, but this is likely to change as political and regulatory bodies recognize the urgency. In the meantime, it’s crucial to prepare for impending regulations, which can be summarized in one word: ethics. Simply put, don’t engage in practices you wouldn’t want AI systems to perform on you.

Additionally, existing regulations like GDPR need to be considered and ensured to be compatible with AI implementations. This means maintaining compliance in how these systems are used and how data for them is stored, though the specifics may vary depending on the application.

On top of that, AI compliance involves a proactive approach. It means:

• Integrating regulations from the outset and fostering a culture of ethical development;
• Staying informed about evolving standards and incorporating them into AI design;
• Adhering to ethical guidelines and ensuring transparency and fairness;
• Implementing rigorous data governance and conducting regular audits to identify compliance gaps;
• Collaborating with regulators and industry bodies.

By doing all this, businesses can innovate sustainably and responsibly in the AI landscape.

Join the AI revolution to change your business

Summing up, as we stand on the cusp of the AI revolution, our role as facilitators of transformation is clear. By guiding businesses through the phases of AI adoption, assessing readiness, and navigating challenges, we at Timspark can help bring artificial intelligence projects to unexpected heights. It’s time to shape the future of business for generations to come.

Technical debt (also known as design or code debt) – is it really that bad, and is it worth the effort? Although the word “debt” has a negative connotation, business people understand that debt, when properly managed, can even be beneficial. The main thing is to assess it and build the right strategy. The same goes for technical debt: first, you need to identify it, evaluate its impact on the software being developed, and schedule sessions to eliminate it gradually. The good news is that there are automated tools you can use for measuring and reducing technical debt. The bad news is that teams often ignore the issues they find, causing them to get out of control. 

Technical debt itself isn’t that problematic in the short term — your software may work fine. But in the long run, having that kind of debt can be a ticking time bomb for a business. So, how can you manage and reduce technical debt?

Decoding technical debt – an inside look

What is technical debt and why does it occur?

Essentially, technical debt is necessary changes in the code itself and the software architecture as a whole, postponed until later. The problem is that this “later” may not come, which may increase the cost of maintaining a poorly designed system. How do you know that the source code is bad? Not only is it software in which previously undetected bugs keep popping up, but also an application whose functionality expansion often leads to a complete rewrite of previously completed parts.

There are multiple reasons why technical debt occurs. Let’s have a look at the main ones:

• This was a clear business decision to get the product to market as quickly as possible. However, it’s crucial for the decision maker to bear in mind that the next version of such a product may undergo a complete revamp from scratch.
• Development processes are not mature, team members are unfamiliar with coding standards to adhere to, or there is no technical decision maker on the team. The latter may lead to very poor architectural design and implementation.
• The team simply does not adhere to the company’s coding standards, and the technical manager is either absent or serves only as a nominal leader with no real influence over other team members.

Third-party libraries used in the project have been significantly modified or the project environment has undergone crucial updates. This type of technical debt is called environmental debt.

How to measure technical debt?

The most popular method for measuring technical debt is SQALE (Software Quality Assessment Based on Life Cycle Expectations). Your codebase will be rated on an alphabetical scale from A to E, with A representing the highest quality rating.

To help you decide which problems to fix and when you can use the SQALE pyramid illustrating the distribution of code debt based on its impact on application stability. The lower the level, the shorter the term causing problems due to technical debt. For example, if you have testability or reliability issues, you may expect functionality to be implemented incorrectly. Whereas higher levels show tech debt issues that may affect you in the future, such as during the maintenance phase.

For example, to ensure the reliability of your code, it’s essential, at the very least, to address all issues related to testability and reliability. If your goal is to cut down on future maintenance costs, you also need to handle problems related to changeability, efficiency, security, and maintainability.

Security issues as a crucial part of tech debt

Given the high focus on cybersecurity these days, it makes sense to address security issues as soon as they are discovered. The OWASP Top 10 document is adopted by a global community as a software security standard. This document represents the most important security risks for both web and mobile applications. It should be noted that the OWASP Top 10 is updated over time.  On the one hand, modern development frameworks cover known problems, leaving little room for developers to write insecure code., And, on the other hand, cybercriminals come up with new ways to hack systems. Thus, to ensure that your development follows the latest industry standards, remember to regularly check that the OWASP ruleset implemented in your project is up to date.

OWASP Mobile Top 10 changes in 2016-2024 are presented in the table below:

Dealing with technical debt

To keep your project moving in the right direction and reduce technical debt, consider the following steps:

1. Implement coding standards across the organization, not just a specific project. The most effective way is to create your own guidelines based on best practices accepted by the global development community.
2. Choose a suitable development methodology and plan releases. Nowadays, most projects are developed using Agile frameworks (such as SCRUM, Kanban, Lean, SAFe, etc.). However, some types of projects may require a more traditional Waterfall.
3. Automate the development process to help your team stay on track. That is, you will need a set of tools that seamlessly integrate into one ecosystem, ensuring transparency of the progress and quality of the entire project.
4. Involve DevOps to set up a Continuous Integration / Continuous Delivery (CI/CD) process, including the automated code review step. Adopting internal coding standards doesn’t guarantee that developers will regularly follow them.
5. Plan refactoring sprints in advance. Ideally, short-term technical debt issues should be resolved before the change set is committed to a version control system (such as GitHub). While long-term issues usually require more effort to resolve and more thorough regression testing after such fixes. Therefore, you will need additional iterations to refactor.

Without getting too deep into release planning management, let’s use the example of three-month releases that deliver functionality incrementally every two-week sprint. With these assumptions in mind, the illustrated planning below may be helpful:

With six sprints per release, it’s important to remember the feature freeze phase. Usually the last sprint is used to stabilize the software, meaning no new feature is implemented and the team focuses on fixing bugs. Some low-risk technical debt issues can also be resolved during the last sprint. However, the technical leader should evaluate whether these fixes are risky or not.

Warning: refactoring right before release is quite risky.

If your software requires more significant changes or issues with a high risk of technical debt in the backlog, it makes sense to schedule an additional refactoring sprint at the beginning of the next release development. This approach will give your team the necessary time to implement changes and run regression tests to ensure that no functionality has been affected.

Reducing technical debt with the help of DevOps

What DevOps activities are most important for reducing technical debt? First of all, a DevOps specialist is the very person who is responsible for setting up the CI/CD process. Without continuous integration, you won’t be able to ensure the stability of your software. In addition, a DevOps performs the following responsibilities:

1. Deploying an appropriate static code analysis tool and configuring it as an additional step in the CI process. This way, code reviews can be performed automatically without the possibility of skipping them, ensuring that a change set of low quality will be rejected by the CI server and never be committed in version control.
2. Deploying and configuring the version control system, creating the necessary branches (for example, development and release branches), and setting policies for review requests.
3. Monitoring for updates to third-party components that may affect the software being developed.
4. Checking possible vulnerabilities in the tools used in the project.
5. Advising the team on which architectural patterns and third-party services (e.g., Database as a Service) are most relevant for continued maintenance and monitoring.
6. Consulting the project manager and the customer on which hosting/cloud provider is most suitable for the solution being developed.
7. Setting up the necessary project environments depending on their purpose. For example, DEV  for developers, TEST  for internal QA team, STAGE for user acceptance testing, and PROD  for release. Static analysis quality gates can be configured differently for different environments. Depending on the team’s policy, the DEV environment may be used by the team to speed up synchronization with each other, and therefore, some new technical debt issues may be deployed to it. The higher the environment (DEV to PROD), the higher the quality of the deployed code should be.

How to reduce technical debt with static code analysis tools

Automated code analysis tools can help reduce technical debt and even eliminate it. They address the question of how to measure tech debt and provide recommendations for creating quality code, which is also crucial for security. Among the many platforms that provide code quality inspection, there are two products worth considering: SonarQube and CAST Imaging.

Both SonarQube and CAST Imaging provide static code analysis. They can detect design errors, identify potential problems due to the misuse of certain libraries and expressions, find code smells (namely clear violations of design/coding principles), and detect duplicates in the code base, as well as security vulnerabilities. In addition, both platforms can analyze the result of unit test execution and calculate code coverage.

CAST Imaging provides an in-depth analysis of an architectural design. Using CAST Advisors, software developers can modernize their applications and easily move them to the cloud. At the same time, SonarQube is more flexible and can be customized even for a specific branch of the project. The latter allows teams to have different settings for code quality analysis depending on the stage of development. For example, at the ongoing development stage, the team may focus on short-term issues of technology debt, leaving the long-term issues of tech debt for the refactoring phase.

Using one of these tools can help you with technical debt management.

Let’s compare these platforms.

How technical debt reduction affects the QA team

Taking action to resolve technical debt may seem like too much extra effort. However, it is not. Keeping technical debt at an appropriate level will reduce not only the subsequent maintenance costs, but also the workload of the QA team in the current development cycle.

Considering that testability is one of the components of technical debt, we can confidently say that improving testability and code coverage can help the QA team in their daily work. When developers promptly address short-term technical debt issues and maintain a code coverage of at least 60%, the QA team can focus on testing new features, usability testing, and verifying bug fixes. Moreover, automatically running unit and integration tests frees up the time of the QA engineer who would otherwise have to perform regression testing manually.

When it makes sense to ignore technology debt

Making code academically beautiful is always a pleasure. However, sometimes you can leave everything as is and not pay much attention to the growing technical debt. This makes sense if:

• You are developing a PoC (Proof of Concept) or prototyping some features.
• Time to market is critical, so you are willing to sacrifice code quality.
• You are dealing with a legacy code base that is scheduled to be completely rewritten in the near future.

All of these cases have one important thing in common: this codebase (be it PoC or legacy software) will be thrown away in the near future. Even where time to market takes precedence over code quality, you should keep in mind that you will probably find yourself rewriting the entire product from scratch when gearing up for the next major version. Nevertheless, it is strongly advised to review and address security issues regardless of your future release plans, as a system breach could wreck your business.

Conclusion

Whether you have legacy software or start your own development from the ground up, you need to plan and provide for many things to ensure the project runs smoothly and to maintain the product’s stability. Timspark specialists have extensive experience in both taking over software maintenance and launching new projects. For legacy software, we first conduct a technical assessment and provide you with a plan on how to reduce technical debt. In new projects, we have boilerplate approaches to quickly and inexpensively deploy the required environments with all the necessary project tools for effective software quality management.

References

1. The SQALE Pyramid: A powerful indicator. sqale.org, 2013. 
2. OWASP Top Ten.  OWASP Foundation, Inc., 2023. 
3. OWASP Mobile Top 10. OWASP Foundation, Inc., 2024.  
4. Prevent, reduce, and manage code-level technical debt. SonarSource SA, 2024.
5. CAST Imaging now features automated advice for accelerating application modernization and cloud migration. CAST Software, 2023. 

Teams are constantly seeking ways to improve the efficiency, reliability, and overall quality of their products. Here, DevOps, a set of practices that combines software development and operations, aims to shorten the development life cycle and provide continuous delivery with high software quality. But even with DevOps, there’s always room for improvement, and that’s where artificial intelligence makes a huge contribution.

In the post, we explore how to apply AI for DevOps teams and make these processes even smarter, faster, and more predictable.

Understanding AI for DevOps

Artificial Intelligence is the simulation of human intelligence in machines programmed to think like humans and mimic their actions. When DevOps and artificial intelligence work together, you can automate complex processes, predict outcomes, and get insights that humans might overlook, significantly enhancing the DevOps workflow.

So, how can a DevOps team take advantage of artificial intelligence? Let’s break down the most popular use cases, starting with the good old mundane routine automation.

Automated code reviews and testing

One of the first areas where AI impacts DevOps is in code reviews and testing. Traditionally, reviewing code for errors and ensuring it meets quality standards is time-consuming and prone to human error. AI-driven tools can automate this process, quickly scanning through code to identify bugs, security vulnerabilities, and coding standard violations. Moreover, AI can learn from past commits and reviews to improve its accuracy over time.

For instance, consider a tool like DeepCode, which uses AI to analyze your code and offer suggestions for improvement. With it, you have an expert to review your code that is faster and available 24/7. Such automation speeds up the development process and helps maintain high-quality code standards.

Predictive analytics for better decision-making

Predictive analytics is another area where AI shines in DevOps. By analyzing historical data, AI can predict future trends, potential failures, and the impact of changes in the development process. This information is invaluable for making informed decisions and preventing issues before they arise.

Imagine deploying a new feature and being able to predict how it will affect your system’s performance or if it’s likely to cause any downtime. With AI, this is possible. Tools like Splunk or New Relic use AI to monitor applications and infrastructure, providing real-time insights and predictive analytics to help teams anticipate and mitigate risks.

Enhanced monitoring and incident management

Monitoring systems and managing incidents are critical components of DevOps. AI enhances these processes by not just reacting to issues but predicting them before they happen. Through machine learning algorithms, AI can analyze logs, metrics, and patterns to detect anomalies that could indicate potential problems.

When an incident occurs, AI can also assist in diagnosing the issue, suggesting potential fixes, and even automating the resolution process in some cases. The proactive approach to incident management can significantly reduce downtime and improve system reliability.

For example, IBM’s Watson AI has been used to predict and prevent IT incidents before they impact users. Watson analyzes vast amounts of operational data, identifies unusual patterns, and alerts teams to potential issues so that they can act before users are affected.

Continuous learning and improvement

One of the most significant benefits of AI in DevOps is its ability to learn and improve over time. As AI-driven tools are exposed to more data, they get better at their tasks, whether it’s identifying code vulnerabilities, predicting system failures, or managing incidents. The continuous learning process means that the more you use AI in your DevOps practices, the more efficient and effective they become.

Moreover, AI helps teams learn from their data and offers insights into the development process, identifying bottlenecks and suggesting areas for improvement. The feedback loop creates a culture of continuous improvement, where teams are always looking for ways to enhance their workflows and product quality.

Let’s also quickly review how Generative AI, particularly its ability to process text and give user-friendly workable output, can optimize the work of DevOps teams.

Generative AI, an easier and more user-friendly type of AI, makes significant strides in IT operations by automating process workflows, managing risk assessments, optimizing infrastructure, and enhancing reporting and interfacing. Technologies like Generative Adversarial Networks and transformers are applied in various stages of the DevOps lifecycle, such as code generation, test generation, bug remediation, and automated deployment.

GenAI tools like GitHub Copilot are changing how code is written and maintained. Taking advantage of AI models trained on vast codebases, these tools suggest code snippets, complete partial codes, and optimize existing code for better performance and efficiency.

In predictive analytics mentioned before, GenAI enables proactive scaling and resource allocation by analyzing historical data and usage patterns to predict future requirements. Thus, optimal resource utilization and cost-efficiency become a reality. In cloud environments like AWS or Azure, GenAI can automate deployment processes, analyze the performance of deployment environments, and make data-driven decisions for seamless and risk-free deployments.

GenAI is the tech that actually excels in quickly identifying, diagnosing, and resolving operational and business issues, improving the reliability and stability of DevOps processes. It’s exactly the type of AI that analyzes system logs and metrics in real-time, detects anomalies, and suggests or implements immediate fixes to issues. This reduces the manual workload on DevOps teams across industries and fosters collaboration, communication, wiser use of resources, and improved system performance.

Despite its potential, the adoption of GenAI in DevOps faces challenges like significant time and financial investments for training data, limited knowledge of AI systems, the accuracy of AI output, and potential legal or ethical issues around copyright infringement. However, as GenAI continues to evolve, it’s expected to play an even more significant role in automating tasks, predicting and preventing production issues, managing and scaling infrastructure, and more.

Embrace AI for DevOps today and tomorrow

The integration of AI and DevOps offers the potential to automate tedious tasks, predict and prevent issues, and continuously improve processes. By harnessing the power of AI, DevOps teams can not only enhance their efficiency and productivity but also deliver higher-quality products faster than ever before. As AI technology continues to evolve, its role in DevOps is set to become even more significant, marking a new era of intelligent software development and operations.

If the question ‘How can a DevOps team take advantage of AI’ is still spinning in your head, feel free to reach us out and get all-encompassing consulting on your case.

Although the juxtaposition of MLOps vs. DevOps may seem robust, these approaches can collaborate efficiently to optimize development processes. 

DevOps, a compound of development and operations, emerged as a cultural and professional movement advocating for the automation and integration of software development and IT operations. Its fundamental philosophy centers on collaboration, automation, continuous integration, and continuous delivery, with the goal of reducing the systems development life cycle duration while maintaining high-quality software delivery.

As DevOps evolved, it led to several specialized fields like AIOps, MLOps, DataOps, and DevSecOps. Each variation adapts the core principles of DevOps to specific areas, streamlining and enhancing those domains.

DevOps continues to influence many areas, including cloud computing, big data, and more, demonstrating its adaptability and importance in new and developing technology.

Understanding MLOps

MLOps, or machine learning operations, is a practice that brings together machine learning, data science, and operations. It aims to automate and improve the end-to-end machine learning lifecycle, from data preparation to model training, deployment, monitoring, and maintenance.

MLOps vs. AIOps vs. DataOps

MLOps, AIOps, and DataOps are crucial methodologies, each with distinct focuses on managing data and taking advantage of automation. Let’s explore how these methodologies differ in terms of their pipelines.

MLOps revolves around optimizing the lifecycle management of machine learning models, spanning from development to deployment and ongoing monitoring. Its pipeline typically includes:

• Data acquisition and preparation
• Model development and training
• Model evaluation and validation
• Model deployment
• Monitoring and support

AIOps use AI and machine learning to make operations faster, automate tasks, and improve system performance. Its pipeline encompasses:

• Data ingestion and processing
• Anomaly detection and root cause analysis
• Incident response and automation
• Continuous improvement and optimization

DataOps emphasizes collaboration, automation, and agility in managing data pipelines and workflows, focusing on accelerating insights delivery. Its pipeline comprises these stages:

• Data integration and ingestion
• Data preparation and quality assurance
• Model development and deployment
• Collaboration and governance
• Continuous integration and delivery

So, in essence, while MLOps specializes in managing machine learning models, AIOps focuses on enhancing operations through AI-driven insights, and DataOps emphasizes collaboration and automation in data management. Each methodology’s pipeline reflects its unique role in optimizing specific aspects of data-driven operations in the complex digital space.

How DevOps, AIOps, MLOps, and DataOps work together

These practices complement each other. For instance, AIOps, or DevOps and artificial intelligence, can enhance MLOps by providing advanced analytics to optimize machine learning models, while MLOps can benefit DataOps by maintaining data quality and accessibility for machine learning projects.

How businesses use MLOps and what benefits they get

Machine learning in DevOps significantly refines the lifecycle of machine learning models. By automating processes and promoting collaboration among data scientists, engineers, and business stakeholders, MLOps enhances the efficiency of developing, deploying, and maintaining ML models.

For example, McKinsey reported that an Asian financial services company reduced the time to develop new AI applications by more than 50% by implementing a common data-model layer and standardizing data-management tooling and processes.

MLOps also preserves the quality and reliability of machine learning models by consolidating and automating processes. This approach reduces errors and makes sure that models perform as expected in real-world environments, such as tackling fraud risks in banking. MLOps enhances model auditability and responsiveness to change and provides a methodology for combining rapid feedback with automated monitoring to maintain model accuracy over time.

Companies using comprehensive MLOps practices shelve 30% fewer models and improve their AI model value by up to 60% (McKinsey).

As for people, MLOps frameworks empower data scientists by automating routine processes and allowing them to focus on higher-value tasks like adding new features to existing models and solving other business challenges.

MLOps optimizes costs and resources by improving model performance and polishing operational processes. By taking advantage of MLOps, organizations can manage machine learning consumption costs more effectively, ensuring resource-heavy analytics solutions are designed with cost considerations in mind.

Overall, implementing machine learning in DevOps delivers substantial business results, including cost savings, productivity gains, faster innovation, and improved model reliability. These examples and statistics demonstrate the transformative impact MLOps can have across different industries.

Challenges of implementing MLOps solutions

Stakeholders sometimes view AI for DevOps as a miraculous solution for all issues, setting unfeasible goals, especially when non-technical stakeholders are involved. However, it doesn’t stop here. Take a minute to review the most common DevOps and machine learning implementation challenges and how to tackle them effectively.

At Timspark, we guide organizations through these challenges, offering tailored solutions that align with business objectives and technical requirements.

We help:

• Set realistic goals
• Improve data management
• Enhance security
• Upgrade tools and infrastructure
• Facilitate better communication and user engagement
• Provide technical and operational support
• Ensure the appropriate use of ML
• Integrate ML models with business systems

Consider MLOps in pursuit of competitive advantage

The incorporation of DevOps methodologies into AI and machine learning represents more than just a passing trend; it’s an essential progression to meet the growing complexity and demands of technology. The implementation of machine learning in DevOps and its associated practices offers businesses the opportunity to achieve greater efficiency, innovation, and competitive edge. As technology advances, the application of these principles will also evolve, heralding promising advancements in the future.

Should you be considering the integration of DevOps and machine learning into your workflows but find yourself facing one or more challenges, don’t hesitate to get in touch and seek comprehensive support throughout the process.

The beginning of the 21st century witnessed an explosive surge in high-tech advancements, making Internet services and mobile apps an integral part of daily life for people of all ages. While this presents a positive trend for software developers with an expanding user base, it comes with its challenges. The number and variety of platforms that need to be supported has increased significantly. Failure to support this diversity results in not reaching the target users properly.

According to the StatCounter service, more than 40% of users access the Internet from Android devices, just under 30% from the Windows operating system, approximately 18% and 7% are users of iOS and MacOS devices, respectively, and the share of Linux users is also growing (statistics for 2023, WorldWide region).

Source: StatCounter Global Stats – OS Market Share

Creating and maintaining a product for various platforms with distinct code bases can be a constant headache for manufacturers. That’s where cross-platform development comes in.

The most promising cross-platform app frameworks

By the time we write this article, various statistical aggregators named .NET MAUI (Xamarin), React Native and Flutter as  top-choice frameworks for cross-platform development. Additionally, we are including Kotlin Multiplatform to this list. Wondering why? Let’s delve into the details below.

.NET MAUI (Xamarin)

Xamarin, released in 2011, emerged as one of the first successful open source technologies for cross platform mobile development. Later acquired by Microsoft, Xamarin received investments and saw its robust features integrated into the .NET platform. The result of this symbiosis was .NET MAUI, representing the next evolutionary step for Xamarin. Meanwhile, official support for the original Xamarin framework is scheduled to end on May 1, 2024.

.NET MAUI (Xamarin) pros:

• The framework is based on .NET, the programming language is C#. According to a Stack Overflow Developer Survey, C# was named a popular language by nearly a third of professional developers
• Officially supported platforms: Android, iOS, MacOS, Windows. WhileLinux is not officially supported, developers can create a Xamarin app for Linux using a workaround suggested by the developer community.

.NET MAUI (Xamarin) cons:

• During the transition from Xamarin to .NET MAUI, Microsoft has shifted its focus from tvOS, Android TV and Apple watchOS. So, if you need to write an app for these platforms, you will have to look for another technology.
• Since .NET MAUI apps operate through the Mono-framework (i.e. they have middleware to run the build on the target platform), their performance may lag behind that of native applications.
• .NET MAUI is actually a superstructure of native components, so customization of the UI is limited.
  
  The architecture of the .NET MAUI app looks like this:

The app code primarily interacts with the .NET MAUI API (1), which in turn directly consumes the platform’s native APIs (3). In addition, app code can directly invoke the platform API (2) if necessary.

Sample of “Hello World” application, created in .NET MAUI

React Native

React Native was released in 2015 by Meta Platforms, Inc. and initially intended for multiplatform mobile app development, that is, creating apps for both iOS and Android on a shared code base. Its programming language is JavaScript, which makes React Native especially popular among front-end developers.

React Native pros:

• The programming language is JavaScript, which is the most widely used language in the world according to a Stack Overflow Developer Survey.
• It is backed by a wide community and a large number of third-party libraries.
• It uses a proprietary engine to render the UI, which allows you to create truly unique widgets and layouts without being tied to a predefined set of native UI components.

Officially supported platforms: iOS and Android. However, through collaboration with partners and the React Native community, it is possible to support MacOS Desktop, Windows Desktop, and the Web as well. 

React Native cons:

• React Native remains in beta, which affects its stability. Its architecture and libraries change frequently, posing challenges for maintaining existing projects.
• Operating on a bridge architecture, React Native has an intermediate layer to provide interaction between the React Native app and the target platform. This results in decreased performance and lack of flexibility.

Briefly, React Native architecture is shown below:

Here is a sample of “Hello World” application, created in React Native:

Flutter

In 2017, Google introduced Flutter, a framework for cross platform app development, with Dart (C-style syntax) as the programming language. While Dart may not be the most widely used language among software developers, it is relatively easy to learn. What sets Flutter apart are its proprietary rendering engines that allow you to create any custom widgets and UI layouts. Also, Flutter compiles assemblies into native machine code, ensuring the performance of Flutter apps is comparable to that of native apps.

Flutter pros:

• Flutter does not rely on target platform widgets; instead, it uses its own rendering engine. This allows software developers to implement the desired UI without worrying about updates on the target platforms affecting the application’s appearance.
• Flutter doesn’t use middleware to run its builds; instead, it allows direct assembly compilation for a specific platform, providing performance comparable to native technologies.
• Officially supported platforms: Android, iOS, Web, Linux (Debian, Ubuntu), MacOS, Windows. 

Flutter cons:

• You can only write a client app in Flutter. While Dart can be used with limitations for server-side development, it is not fully mature for this purpose.
• You can’t invoke native APIs directly from Dart. You will have to use native languages to interact with certain APIs, such as:
  • Kotlin or Java on Android
  • Swift or Objective-C on iOS
  • C++ on Windows
  • Objective-C on macOS
  • C on Linux

Flutter has layered architecture, where each part of the framework layer is designed to be optional and replaceable:

Here is a sample of “Hello World” application in Flutter:

Kotlin Multiplatform

Entering the scene as a fairly new player in the arena, Kotlin Mutiplatform is worth being added to the list. The Kotlin language, renowned among Android and backend developers for a long time, has gained a significant share, with approximately 95% of the top 1000 applications in the Play Store being written in it according to Google statistics. Therefore, it is not surprising that JetBrains decided to take Kotlin to the next level by adding support for various platforms. The beta version of Kotlin Mutiplatform was presented in 2022, and by the end of 2023 JetBrains announced its full-fledged release.

Kotlin Multiplatform pros:

• You can use Kotlin not only for the client app, but also for the server-side development.
• You can implement shared logic below the UI layer.
• Developers have unrestricted, direct access to both Android and iOS SDKs.
• With Compose Multiplatform (additional technology from JetBrains), developers can reuse UI code across platforms. However, Compose Multiplatform is currently only stable for Android and desktop.
• Beyond the virtual machine option (JVM), Kotlin Multiplatform allows compilation of native binaries using Kotlin/Native ensuring no loss in performance.
• Officially supported platforms: Android, iOS (Alpha), MacOS, Linux, Windows, Web Wasm (Alpha).

Kotlin Multiplatform pros:

• Unlike the three cross-platform app frameworks discussed above, Kotlin Multiplatform does not support hot reloading, which affects the speed of software development and debugging.

Since Compose Multiplatform, which is required for sharing UI code, is still in alpha for iOS and web, Kotlin Multiplatform cannot be used as a full-fledged alternative to Flutter at this point.

High-level Kotlin architecture is shown below:

Here is a sample of “Hello World” application in Kotlin:

Cross-platform app development frameworks comparison

Cross-platform development challenges

Despite the attractiveness of cross-platform technologies, there are a number of questions and nuances that must be taken into account when planning development, timelines and budget.

Cross-platform software development cost

• You can use Kotlin not only for the client app, but also for the server-side development.
• You can implement shared logic below the UI layer.
• Developers have unrestricted, direct access to both Android and iOS SDKs.
• With Compose Multiplatform (additional technology from JetBrains), developers can reuse UI code across platforms. However, Compose Multiplatform is currently only stable for Android and desktop.
• Beyond the virtual machine option (JVM), Kotlin Multiplatform allows compilation of native binaries using Kotlin/Native ensuring no loss in performance.
• Officially supported platforms: Android, iOS (Alpha), MacOS, Linux, Windows, Web Wasm (Alpha).

Multiplatform app development risks

If you decide to dive into cross-platform development, it’s important to consider the following potential risks when planning the project:

1. You may have to look for workarounds to access the hardware. Therefore, during the analysis and design phase, it is advisable to identify critical hardware requirements for your future product and decide on cross-platform technology. The same applies to target operating system functions (API calls).
2. If your product needs to interact with third-party applications installed on the user’s device (for example, Google Maps), it’s crucial to ensure that the chosen framework supports such interactions.
3. Since some cross-platform technologies use middleware (or bridges) in their architecture, you should keep an eye on the product’s performance.
4. Cross-platform technology manufacturers typically lag behind updates to the target platform itself. Therefore, you need to ensure that the development framework considers crucial changes potentially affecting your product’s interactions with the target platform.
5. In case of cross-platform mobile app development, you need to make sure that the approach you choose won’t drain the device’s battery or cause memory leaks, and will use the device’s storage sparingly.Additional attention is needed in case ofAndroid and iOS app development. In general, Google Play and the App Store have similar guidelines for reviewing apps.
   However, Apple has more stringent requirements for data backed up on the user’s device (point 2.23 of the iOS Data Storage Guidelines [10]). Also according to requirement 4.2 (App Store Review Guidelines), ‘Your app should include features, content, and UI that elevate it beyond a repackaged website. If your app is not particularly useful, unique, or “app-like,” it doesn’t belong on the App Store’. This means that if you simply wrapped your website using cross-platform or hybrid technology and perhaps only added a login page, your app may be rejected by Apple.

Useful tips for cross-platform development

How can you minimize the costs of commissioning and further maintenance of software? Here are some tips that may be useful for cross-platform development, especially if you are working on a mobile app:

1. Set up a continuous delivery process where each alteration in the source code will be checked for consistency and errors and automatically delivered to the required environment (test or production). For example, in the case of an iOS application, approved changes can be automatically compiled into a new build, verified using autotests and added to TestFlight ( for beta versions) or directly to the store. An experienced DevOps can handle this task.
2. Plan time required to write unit and integration tests (so-called white box testing). Initially perceived as an extra expense, these automated tests run automatically during build phase, ensuringe development stability. The software developers themselves take charge of these tests.
3. In addition to white box testing, you can add black box testing: automated tests written using specialized frameworks that simulate real human behavior. For example, toggling the location or Internet connection on the target device and check the app’s response. A proficient AQA specialist can develop such tests and thus reduce the time required for manual QA engineers to verify the acceptability of a new release.

Conclusion

We’ve only looked at the tip of the cross-platform development iceberg. There are several dozen cross-platform currently existing technologies, each of them with its own pros and cons.

How to choose the right technology then? The silver bullet for avoiding possible challenges with implementation of the required functionality or even the need to completely rewrite the software from scratch is hiring seasoned specialists. A system analyst and software architect can help you choose the appropriate technology stack, while a skilled project manager can cope with project timeline and development risks. Interested? Simply contact Timspark for a free quote.

References

1. Operating System Market Share Worldwide. StatCounter, 2024.
2. What is .NET MAUI? Microsoft, 2023.
   
3. 2023 Developer Survey. Stack Overflow, 2024.
4. React Native. Meta Platforms, Inc, 2024.
5. Out-of-Tree Platforms. Meta Platforms, Inc, 2024.
6. Flutter Docs. Google, 2024.
7. Get started with Kotlin Multiplatform. JetBrains, 2023.
8. Why do teams adopt Kotlin? Google, 2024
9. Stability of supported platforms. JetBrains, 2024.
10. Optimizing Your App’s Data for iCloud Backup. Apple Inc., 2024.
11. App Store Review Guidelines. Apple Inc., 2024.

“What gets measured gets managed.”

This quote is said to be from Peter Drucker, a leading management consultant from the 20th century. But the actual words go something like this: ‘What gets measured gets managed — even when it’s pointless to measure and manage it, and even if it harms the purpose of the organization to do so.’ And the credit goes to the journalist Simon Caulkin for coming up with those words.

Drucker, in turn, put it like this: ‘Moreover, because knowledge work cannot be measured the way manual work can, one cannot tell a knowledge worker in a few simple words whether they are doing the right job and how well they are doing it.’

We’re totally on board with Drucker here. People are more than just numbers, and there are various factors that can impact how effective software developers are. Plus, you can’t quantify passion, creativity, and commitment to company values with numbers.

Yet, when it comes to catching hiccups in a project, it’s crucial to use and scrutinize metrics and KPIs consistently. So, how do we do it in software development?

What is a KPI in the remote development team?

In software development, a key performance indicator, or KPI, is like a scoreboard for your team’s success. It’s a set of numbers and metrics that tell you how well your software project performs. Instead of guessing or hoping for the best, KPIs provide concrete data to show whether your team is meeting its goals.

For example, one KPI might track how quickly your team resolves issues or fix bugs. This helps you gauge the efficiency of your development process. Another KPI could measure user satisfaction, indicating whether people are happy with your software.

In a nutshell, KPIs are the numbers that keep your team on track and help you build high-class software.

Why are KPIs important in software development?

KPIs are crucial in software development, especially when working with remote teams or outsourcing projects. Here’s why KPIs are so important:

• KPIs find and fix problems in making software, ensuring everything runs more smoothly and leading to better results.
• KPIs are progress reports for software projects. They tell everyone how things are going, like a snapshot of the project. This helps manage the work better and plan for the future, so everyone knows what’s happening.
• By keeping an eye on how things are going and making things better as you go along, you can avoid extra work and costs. It’s like fixing things before they become big problems, saving time and money.
• KPIs also help make smart choices. Instead of guessing, use data to decide where to put our energy for the best results.

In essence, KPIs in software development represent a strategic roadmap. They navigate distributed agile teams through challenges and keep the work cost-effective, ending up in the delivery of high-quality software products.

When is it efficient to introduce KPIs to a remote software development team

Introducing KPIs will make sure you’re on the right track. But when is the best time to start using them? Use these three signs to decide:

1. It’s not a quick project. If your project is a marathon, not a sprint, that’s the perfect time for KPIs. They work best when you have a bit of time to see progress and make improvements.

    

2. You have clear milestones and deliverables. This way, you know where you’re going and can measure progress along the way.

    

3. There’s at least a high-level project plan. Before you hit the road, it’s good to have a plan. Having at least a high-level project plan means you’re ready to start using KPIs to keep things on course.

So, if your project is more like a journey than a quick ride, with clear milestones and a roadmap in hand, that’s the efficient time to bring in KPIs and set up a smoother, more successful trip.

Types of KPIs for software developers team

Before we move to the custom software development KPIs, let’s take a short look at what matters for any project:

• Schedule compliance: Keeps the development progress aligned with the planned schedule, highlighting reasons for deviations such as missing requirements or technical risks.
• Accuracy estimate variance: Indicates how much the actual efforts deviate from the initial estimate, helping determine the remote development team’s velocity.
• Budget variance: Tracks deviations from the planned budget, especially when involving unplanned expenses for specific tasks.

But just like with any project, details compose the whole picture. It is essential to break KPIs down to the level where production happens, finances flow, and customers use the software.

First, let’s take some time to explore the efficiency and workflow metrics and what decisions they help make on the way to successful delivery.

Productivity and workflow software development KPIs

As you can see, we focus on executing tasks and regular monitoring. However, remember that the remote software development team’s motivation and comfort are always as important as their expertise and speed of accomplishing projects.

Software development performance and code quality KPIs

Checking how well the code performs and its quality is crucial. It helps start things like code reviews and testing on time. Plus, since distributed agile teams change, having good code becomes even more important. Gone are the days when developers wrote code just for themselves — now, it needs to be neat, accurate, and well-organized.

Customer satisfaction KPIs in software development projects

Keeping customers happy usually comes down to two things: how easy your software is to use and how well it solves problems. If any metrics show issues in these areas, the responsible remote development team gets a heads-up that improvements are needed.

Financial KPIs in software development projects

Finally, money matters, too. Software development isn’t just about pure coding; it includes steps like project discovery and business analysis. This means the whole project can bring in financial profit. That’s why we keep a close eye on financial metrics — to make sure the team did the job right and made the project a success.

These financial indicators tell us how the project is doing now and how it could make more profit in the future. Again, any differences can be specific to each case, showing problems in different parts of the project.

When KPIs might not be useful

KPIs are powerful, but they shouldn’t stifle creativity and innovation. Use them as guiding principles, not rigid constraints. Allow flexibility within the development process, balancing the pursuit of KPI targets with the need for exploration and creativity.

We recommend seeing KPIs as tools that inform decision-making rather than strict rules. This approach ensures that development teams can adapt, experiment, and foster a culture of continuous improvement.

How to avoid micromanagement when placing KPIs

Implementing KPIs is like steering a ship — you want to set the course without becoming too controlling. So, we offer ten steps to make sure you empower employees with this tool rather than constrain them:

1. Lay out why KPIs matter and how they tie into the bigger picture. When the remote development team understands the ‘why,’ they’re more likely to sail in the right direction independently. 
2. Get your team’s input on setting targets. When they have a say in the destination, they’re more invested in the journey. 
3. Keep the focus on what needs to be achieved, not how to get there. This allows your team to chart their own course and find creative solutions. 
4. Drop anchor regularly with team check-ins. Open communication minimizes the need for constant oversight and keeps everyone on course. 
5. Harness the power of trust. When your remote development team feels trusted, they navigate their responsibilities with confidence and skill. 
6. Arm your crew with the tools and training they need. This empowers them to navigate challenges without waiting for your command. 
7. Hoist the flag for every achievement. Celebrating successes builds morale and motivation without having to keep an eagle eye on every move. 
8. Be ready to adjust the sails with changing winds. Flexibility in adapting KPIs ensures that your journey stays on course, even through unexpected waves. 
9. Lead from the helm, striking the right balance between guidance and letting your crew steer. The way you lead sets the tone for the entire project. 
10. Instead of handing out maps for every challenge, encourage your crew to plot their own course. Fostering a culture of self-sufficiency turns your team into seasoned navigators.

These ten steps will help you chart a course with KPIs, making your remote development team sail toward success without feeling like they’re under a watchful eye at every turn.

Manage the measurable, nourish the immeasurable

In the world of software development, success isn’t just about hitting numbers; it’s about the passion, creativity, and commitment that drive projects forward. While these aspects might not fit neatly into spreadsheets, they are the heartbeat of any successful endeavor. However, the strategic use of KPIs is essential to keep projects on track and tackle challenges effectively. KPIs act like a roadmap, helping distributed teams in agile navigate complexities without losing the human touch.

If you’re looking to ace the KPI realm, especially in the remote work setup, consider teaming up with Timspark, the experts in optimizing KPIs for remote engineers. Let’s collaborate and turn your software development journey into a data-driven success story. Ready when you are!