The ML development life cycle

Before introducing you to automated ML, we should first define how we operationalize and scale ML experiments into production. To go beyond Hello-World apps and works-on-my-machine-in-my-Jupyter-notebook kinds of projects, enterprises need to adapt a robust, reliable, and repeatable model development and deployment process. Just as in a software development life cycle (SDLC), the ML or data science life cycle is also a multi-stage, iterative process.

The life cycle includes several steps – the process of problem definition and analysis, building the hypothesis (unless you are doing exploratory data analysis), selecting business outcome metrices, exploring and preparing data, building and creating ML models, training those ML models, evaluating and deploying them, and maintaining the feedback loop:

Figure 1.1 – Team data science process

A successful data science team has the discipline to prepare the problem statement and hypothesis, preprocess the data, select the appropriate features from the data based on the input of the Subject-Matter Expert (SME) and the right model family, optimize model hyperparameters, review outcomes and the resulting metrics, and finally fine-tune the models. If this sounds like a lot, remember that it is an iterative process where the data scientist also has to ensure that the data, model versioning, and drift are being addressed. They must also put guardrails in place to guarantee the model's performance is being monitored. Just to make this even more interesting, there are also frequent champion challenger and A/B experimentations happening in production – may the best model win.

In such an intricate and multifaceted environment, data scientists can use all the help they can get. Automated ML extends a helping hand with the promise to take care of the mundane, the repetitive, and the intellectually less efficient tasks so that the data scientists can focus on the important stuff.