Being Agile While Developing AI Products

By Data Science Salon

Agile methodologies are widely deployed into application development for a number of reasons. Establishing this methodology can increase the overall “throughput” of model lifecycle management by streamlining end-to-end tasks, facilitating the use of talent pools, reducing time spent on mundane data tasks, and increasing productivity and collaboration.

To make a project successful, teams need to think about how to practice agility in the context of Artificial Intelligence (AI) and Machine Learning (ML). The goal is to improve the efficient delivery of initiatives and delivery times while  reducing risk. Companies like Google and Facebook are all based on agile principles. So that naturally leads to the question of, why does AI and ML also need to be agile?


Scope of Agile Model Lifecycle Management


Constantly evolving machine learning models

The first one is that there's a lot of research going into NLP Bert models. The only way model managers can figure out if a model is working is when it goes into production, which can incur an expensive cost altogether.

Model development and deployment time

Rather than waiting months or years for a software project to wind its way through design, development, testing, and deployment, the Agile approach focuses on tight, short iterations to meet immediate needs. It then continuously iterates as project requirements become more refined.

Data-centric needs

The data and algorithm combination varies according to the end objective. One can have the same algorithm with different training data, and that would generate a different model. A different algorithm with the same training data would also generate a completely different model. As such, we need to consider additional approaches to augment agility and make data more AI-ML model relevant.

Model monitoring

Manasi Vartak, Founder and CEO at Verta, explains in her webinar about agile development that before there was a central system for model monitoring, ad hoc checks were implemented, which often failed to meet their purpose due to changing data dynamics and corresponding feature selection for different model iterations. Correspondingly, adapting automated AI-ML model monitoring lets model managers continuously measure, while  improving and quantifying data quality for desired model performance.


Iterative AI Project Life Cycle Requirements

Foreseeing the correct product requirements can prove to be an arduous task for innovative products. It can become increasingly difficult to work iteratively as the project grows and matures—iterations require pauses between cycles. However, transparent methodologies, standards, and processes facilitate agility which can significantly reduce the iteration time. The goal is to care about a model that works and then iterating on it once deployed.

Benefits of Iterative Development 

Iterative development delivers business value with each iteration. Since it’s a more accessible and simpler risk management methodology, being agile offers the advantage of risk identification and addresses keeping track of models within each iteration. Agile model life thus translates to increased team productivity through daily task allocation and better project visibility due to the flexible, simple planning system. 


Value Proposition Behind Agile Development

AI is revolutionizing everything from predictive maintenance to delivering in-personal medical care. That’s why businesses and public sector organizations worldwide aim and wish to integrate AI programs to their IT agenda. However, the process of developing and implementing an AI solution can be complicated.

Just as the journey of a thousand miles starts with a single step, a successful AI program can begin with a single sprint, like piloting one use case at a time. That’s where the small scale of the agile approach can prove to be an invaluable business investment.


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