New Product Development Meet with Divum Experts - Revisit Agile for ML/AI projects.

As a project management approach, Agile has quickly risen to prominence in recent years. Due to their flexibility, Scrum and Extreme Programming (XP) have helped many organizations support a fast-paced product development process. The fact that many standard agile implementations encounter considerable difficulties when addressing the unique lifecycle requirements of AI/ML projects is a big drawback.

Since these are challenging projects that require change management, knowledge sharing, iterative development and frequent communication, agile methods are the most suitable approach for a machine learning project. By following the agile approach, team members can communicate in a timely fashion, avoid bottlenecks, and strive to produce the desired results.

Attend this session to learn how Divum 's product development framework can help you implement an agile approach for ML/AI-based products. Find out how our Agile-ML methodology combines Scrum and Kanban with lean principles to deliver a holistic project management framework. You will also get an actionable blueprint you can immediately apply to your organization.

What is involved in a typical ML/AI project? - Amar

For those new to the machine learning field or unfamiliar with A/B testing or a neural network, let's take a step back and understand how a project driving AI or ML can be structured.

An AI project will usually fall under the following umbrella:

Acquisition: Scans for data and brings it together for analysis. During this phase, machine learning algorithms are trained on ML datasets to get results that other software tools and professional services in the company can use. It is similar to using computer vision algorithms to analyze images, audio signals, or text.

Application: This is the stage where machine learning rules are integrated into business-specific rules, such as search engine results ranking. This phase can be compared to the "Behavioural Modeling" phase in the Machine Learning Cycle. In short, it's when data scientists use machine learning algorithms to train an AI model that can solve a problem or create something new, like a mobile app.

This is just an overview of how many ML/AI projects will operate, and we'll focus on the second step in this post. I've talked about the process of solving a problem through data-driven insights in this previous blog post, and if you'd like some more information on that, I suggest checking it out.

Refresher session on Agile concepts: Narendra

So, to start off, let's talk about what a retrospective is and why it's useful for an agile team.

A retrospective is a review meeting where team members get together and discuss what has been done in the last iteration and how to improve the working process in the next iteration. It's held at the end of every iteration or sprint, so it can help provide continuous improvement.

Agile development relies on self-organization, which means the team sets their own goals, timelines and dates to tackle its work during each sprint cycle. In this way, they can be free of any micromanagement and have more control over prioritizing important tasks.

But how does this work for machine learning projects?

In machine learning and AI, few agile development frameworks are very open about what work is being done. Many companies that use machine learning and AI integrate with existing software tools or create new ones, meaning their projects are unique to their company. So how can you apply agile practices to these blank canvases of projects?

Adjustments we made to our Agile methods to adapt to ML projects - Amar.

We will talk about the differences and similarities in software development and machine learning, an agile engineer's mindset, and the principles applied to ML/AI projects.

There are many approaches to software development, but with machine learning and AI, there are better ways to do it. The key is to understand your team's approach as well as your own development approaches. For example, you might be a team member who has implemented object-oriented programming in their work since they were introduced to Java while working at another company two years ago. Both methods work differently for everyone; you should use what makes sense for your team's comfort zone.

- Best practices from the Divum ML team for smooth, agile execution of ML projects: Amar

Amar will explain the use of these practices in detail at this meetup:

As a team, we need to understand the goals and requirements of the project at hand. This way, we can prioritize tasks and efficiently plan our work accordingly.

This is a great practice for a long-term project that relies on many different kinds of data sources. Once you've identified what data you need, you can start planning how to collect them and define metrics for evaluation.

Like unit tests, integration tests are used to check if an application's components (the software libraries in Java parlance), APIs or microservices are functioning properly after making changes to the source code.

When working with neural networks and other machine learning algorithms, you can run through many different iterations until you're happy with the results. This is one of the reasons why version control is so important.

Release Planning: Not jumping to conclusions about data

We've relied on intuition for a long time when making business decisions based on our software's output. We've relied on what we think is best for our customers rather than testing if it's improving the user experience. But now, with machine learning, companies are beginning to rely on data instead of hunches. We can see how different users interact with our product and make changes based on their behavior rather than any assumptions that human decision-makers might have previously made.

- Best practices from the Divum Scrum team for Agile Project Managers handling ML/AI projects - Narendra

Narendra will explain the approach of handling ML/AI projects with Scrum in detail at this meetup:

With machine learning, we move away from the idea that data must be perfect before we act upon it. By allowing data to change over time, we can improve our algorithms and make better predictions on what users want. Usually, we make changes to our predictions gradually, which can be a significant difference between using machine learning in business and using it at home with some data science tutorials.

Agile doesn't have many rituals or ceremonies, so you can change your processes as the team sees fit. The key is that the team and stakeholders (the people who own the project's goals) are on board with the agreed process. If a group of people aren't happy with how things work in an agile team, they should discuss how to improve it. You'll never find a perfect process or methodology, but you can continue to improve it through iteration after iteration.

In summary, a retrospective will help you understand if you've made progress towards your goals and if your project has met any deadlines. Agile methods are designed around a team's priorities, so they help you understand what your team wants and needs. If you're interested in joining this meetup, please reserve your spot.


Machine learning is becoming more pervasive in many companies as the technology industry evolves. It's essential for anyone working with ML or AI to have a strong understanding of how agile development works and how it can be applied to ML projects. With this meetup, we'll be able to share our experiences and help others learn about the best practices for working in an agile environment with machine learning.