By Stephanie Falla
Machine learning and data projects, in contrast with traditional software engineering projects, are governed by a resource characterized by unpredictable patterns, distributions, and biases – namely, the data itself. For a successful implementation of projects that give value through inference over datasets, we often consider: Does the collaboration between Agile methodologies and Machine Learning projects represent an ideal scenario, or could it be a lost chance? Moreover, let us explore whether it’s a good match or a fine-tuned operation.
The start: Agile methodologies meet Machine Learning
Agile methodologies are all about being flexible and adapting, providing a framework that prioritizes customer satisfaction and responsiveness to change. These methodologies aim to deliver the right product through incremental and frequent delivery of small chunks of functionality. After all, this is achieved by employing small cross-functional self-organizing teams and fostering collaboration among team members with diverse expertise.
If you’re curious about the history of Agile and its evolution within the realm of software development, you can further explore details about Agile in software here: What is Agile methodology? (Modern software development explained) [1]. Exploring deeper into Agile within our machine-learning projects, we risk ignoring the essential iterative nature of machine-learning development. Consequently, this requires flexibility beyond typical agile frameworks.
In the same way, agile appreciates having a structure that emphasizes timing and deliverables, integrating these two approaches may not be straightforward. Machine learning projects often encounter unpredictable obstacles, posing challenges to the seamless fusion of these methodologies. Equally important, we should still commit to the agile principle of prioritizing customer satisfaction through early and continuous delivery of value. In this context, the provision of high-quality insights through data exploration is crucial. After all, it involves embracing change that evolves with metrics.
Not only balance is crucial to preserve agile benefits, ensuring incremental improvements but also plays a vital role in addressing the complex needs of machine learning projects. Check out the image below to see how agile and machine learning work together in a project.
Figure 1. Agile-Machine Learning Synergy Chart.
Exploring Data: True Positive Stories
Agile Methodologies and Machine Learning often team up to create success stories, showing how well they can work together in real-life situations. In our experience, we have found that teams try things out quickly, get feedback and adjust as needed, ultimately making a successful project.
Let’s consider a use case for a retail company. Firstly, Agile ceremonies were crucial for the team to quickly test recommendation algorithms, gather user feedback, and adjust models. In addition, Sprint planning allowed integration of A/B testing results, goal metric assessment, and iteration on machine learning models. Continuous integration of insights improved recommendation algorithms and quickly adapted to user preferences and market dynamics. Agile processes enhanced accuracy and ensured real-time consideration of customer preferences in feature delivery.
Teams appreciate how agile encourages communication and teamwork, leading to creative solutions and adaptive machine-learning models. Even though achieving quick results in research can be tough, Agile’s step-by-step progress kept everyone engaged.
Research time: The Agile-Data Exploration Tango
Now, let’s talk about research in Machine Learning. Machine learning projects often involve heavy research, where teams dive deep into data, algorithms, and testing. This nature of data projects is where Agile Methodologies might hit a speed bump. Agile is all about quick cycles, but in-depth research takes time, nonetheless. It’s like trying to sprint through a marathon. However, the need for extensive experimentation and analysis might not align perfectly with Agile’s fast-paced approach.
To illustrate, consider a use case in healthcare. In this scenario, a team aimed to deploy a machine-learning model for early disease detection. The research phase involved a comprehensive exploration of various medical data sources, algorithm testing, and refinement. Agile’s fast cycles allow quick adjustments, but deep exploratory data analysis needs an extended timeframe.
However, this doesn’t mean they can’t work together. It simply means finding the right balance. Include research in the project plan, adjusting agile practices for deeper dives to create a harmonious relationship. The following diagram illustrates the cycle of how they can interact together in the same workflow.
Figure 2. Ensuring the team’s ability to make inferences within a given cycle.
Scrum with Machine Learning: A dynamic duo or a challenge?
Consider Scrum, a specific agile framework, and its relationship with Machine Learning. Scrum divides work into short development cycles called sprints, each typically lasting two weeks. Moreover, this structure can be both a benefit and a challenge for Machine Learning projects. Scrum’s regular check-ins and sprint reviews offer feedback opportunities, aligning with iterative machine learning development. Nevertheless, fixed sprint durations clash with unpredictable research timelines, causing potential delays.
Adapting Scrum to the unique needs of Machine Learning relies on more flexibility in sprint durations or incorporating research-focused sprints. The key is finding a balance that allows for progress tracking while accommodating the inherent uncertainties of Machine Learning tasks. (How do you manage AI projects with Scrum?) [2]. We enforce timelines for model monitoring and infrastructure deployment to align with predefined schedules in the system design. However, we allow more tolerance during phases like hyper-parameter tuning. This is particularly true where results and time requirements are less predictable. By doing so, we can balance structure and freedom in project management for the exploratory and iterative nature of Machine Learning.
Scrum’s flexibility is crucial for handling unpredictable data in a focused, agile development process, ensuring result delivery. Ensuring another agile principle involves providing motivated individuals with the environment and support they need. Additionally, it involves trusting them to get the job done.
Navigating success: Scrum methodology in real-world Machine Learning projects
In the realm of Machine Learning, Agile methodologies prove instrumental in overcoming diverse challenges and enhancing project outcomes. Specifically, Agile in e-commerce adapts to user feedback, making regular updates for a dynamic, user-focused recommendation system. For financial fraud detection, Agile’s adaptive nature allows teams to respond quickly to emerging threats through regular sprint reviews and refinements. Similarly, in autonomous vehicle navigation, Agile’s iterative development approach allows focused improvements. It ensures adaptability across diverse driving conditions. Real examples show Agile boosts Machine Learning success through collaboration, flexibility, refinement, and continuous improvement.
The verdict: You need a mix of Agile Methodologies and Machine Learning, or it depends
Is Agile and Machine Learning a perfect pair or a lost opportunity? It depends on the synergy they achieve. While some teams make it work, others might find it difficult.
Success depends on the project, how open the team is to change, and if the organization supports trying new things. Agile and Machine Learning can be great. But like any good relationship, it needs work, talking, and a willingness to grow. Teams should regularly assess, refine processes, and adapt, fostering open communication and continuous improvement.
Tech advances, project methods shift. Agile and Machine Learning tales continue evolving. Whether it’s a good match or a missed chance that requires another gradient step, one thing is certain: it’s going to be interesting and full of learning.
References
- [1] What is agile methodology? Modern software development explained Isaac Sacolick
Contributor, InfoWorld (2022) - [2] How do you manage AI projects with Scrum? Powered by AI and the LinkedIn community (2022)
