Applying the Agile methodology to AI/ML projects
Have you realized every project runs on a sense of urgency even though they have stipulated timelines to finish? This leads to another question: Are you following the best methodology?
Agile, more like an ideology than a strict methodology, instigates solution improvement using a collaborative approach with an adaptive plan. It follows an iterative approach, aligning to the changing business environment. You choose the methods and procedures that work best for your team. Agile has been titled ‘The gold standard’ in the software world, delivering what the customer wants when they want it. While Agile traditionally relates to software, I’ve also found it to be incredible useful for managing AI and ML projects.
The major difference among the AI/ML projects and software is that AI projects require a predictive approach and the whole journey is conceptual, requiring the flexibility of making changes during the development journey. On the other hand, the software management follows a sequential procedure involving different phases until project closure and might not involve any advanced procedures. However, there is a similarity between these two different types of projects, which is that the project manager has a vision on the deliverable and works to achieve it within the stipulated timeline.
In this blog post, I’d like to share some strategies and concepts of Agile methodology in artificial intelligence and machine learning projects that will deliver solid, repeatable value to the organization.
Revisiting Agile for AI and ML projects
The Agile methodology all started in the spring of 2000, when a group of 17 software developers wanted to develop software faster to bring it to the market ASAP. The result was a clear outcome with a focused goal that progressed in an incremental way.
Now, 20 years later, Agile has become commonplace. Machine learning and artificial intelligence are revolutionizing entire industries, from manufacturing and healthcare to finance and retail. Whatever the project, the goal is to apply cognitive technology that leverages the capabilities of machine learning and associated approaches to meet the deliverable.
Following the Agile approach with domain expertise and data science skills helps an AI project deliver value to the organization. AI and ML projects are not driven by programmatic code, but rather by data learning.
Companies of all sizes are implementing AI and ML, each with their own technologies, business objectives, and industry needs. Despite all these AI project differences the goals of these efforts are the same: How can the customer use cognitive technologies and machine learning to best meet their business objective?
A journey of a thousand miles begins with a single step. Similarly, a successful project can begin with a single sprint. Machine learning uses data and algorithms to create a model, and to keep your system accurate, you need to keep the model updated with the required changes. The Agile approach helps the product teams deal with changing circumstances and build tools in a robust and repeatable process.
DevOps and MLOps
This extends to DevOps and MLOps. Where DevOps aims to shorten the lifecycle by providing continuous delivery, MLOps automates machine learning applications and workflows. All of this improves better understanding and communication. The Agile approach in artificial intelligence and machine learning projects encourages experimentation and quick iterations to facilitate fast-paced problem-solving, which is the need of the hour.
How to apply the Agile Methodology to AI/ML projects
When it comes to applying Agile methodology to AI/ML projects, I look to three key areas:
- Show progress
- Show value
- Pursue improvement and clarity
From a broader perspective, Agile in machine learning projects helps convince non-technical stakeholders supporting black box visibility, i.e., the project is assessed solely by its performance from an outside view. This generally results in better communication and understanding of the overall project. In this case, instead of buying months to build a product or service, you can release an MVP quickly and demonstrate business value immediately.
This approach also helps build trust with your customers. AI/ML projects are new territory for many organizations. By showing progress and keeping stakeholders informed throughout the process, you earn the customer’s trust, thereby expanding your relationship with them. Plus, you’ll be able to collect more data from the MVP for the next sprint.
Practicing Agile in AI or machine learning keeps the data science team focused on outcomes by treating the model as a product. And as a “product” it can be updated with new features and improvements. This could be easily documented and recorded using several tools for the best transparency.
The value or the outcome of a sprint can be seen in a minimum of two weeks, which is a critical selling point for any client. Showing value also means building a working software, measuring the progress as the Agile Manifesto states.
One attractive feature of Agile is that the sprint structure supports continuous delivery and can track the value-added to the business. To do this, you tightly couple continuous testing and inspection throughout the project lifecycle. Much like software, AI/ML projects should include testing that is monitored as well as the constant inspection of the build and code health.
Measuring the outcome is a smart way to work with Agile as this will automatically lead to customer satisfaction. There are multiple options to achieve this, including the number of features and products delivered within a given time. Agile also promotes better clarity and understanding to any technical or non-technical stakeholder as it clearly involves product backlog, sprint backlog and release plan.
Pursue improvement and clarity
There is a relentless drive for improvement in every sprint. Applying an Agile mindset can deeply empower teams and cultivate responsibility. It provides clarity in interaction and communication. With a focus on continuous improvement and clarity, teams can greatly improve self-organization and take their individual skillsets to the next level.
The product or service that you deliver will always have a scope of improvement in three areas:
- Revenue growth
- Technical aspects
- And customer satisfaction
This can be achieved by implementing DevOps and MLOps, thereby allowing organizations to alleviate many issues on the path to AI, managing the machine learning lifecycle through automation and scalability.
Feedback is something everyone needs to improve better. Agile allows decisions to be tested and rejected early with feedback loops, providing benefits not in any other model or methodology. These conversations are especially important in AI/ML projects. After all, you don’t want to realize your model won’t scale to the client’s needs when you’re halfway through a project.
As a methodology, Agile drives constant learning and improvement. AI and machine learning projects are driving the future of business, and I see Agile serving as the backbone to support the successful delivery and continuous improvement in AI/ML project lifecycles.