AI DevCamp Notes: ML Engineering from Modeling to Production (Week 7–8)

In the last lesson at AL DevCamp, we dove into “Machine Learning Engineering: From Modeling to Production.” Machine learning allows computers to learn from data and make decisions without explicit programming, transforming many industries. However, moving a machine learning model from the development stage to a production-ready system is not easy; it’s quite a complex process.

Let’s explore these processes together.

Product Scoping

Before diving into the technical aspects of machine learning, it’s essential to define the scope of your project. Product scoping is the process of identifying what your machine learning model should accomplish and how it will be used. This involves:

• Identifying the problem: What specific issue or challenge is the model intended to solve?
• Defining inputs and outputs: What type of data will the model require, and what form will the predictions or outputs take?
• Understanding user needs: Who will be using the model, and how will it fit into their existing workflows?

Proper scoping is crucial because it ensures that the model you build is aligned with the real-world needs of your users and can be effectively integrated into their operations.

Data and Feature Engineering (ETL)

Once the project scope is defined, the next step is to prepare the data that will be used to train your machine learning model. This involves two key processes:

• ETL (Extract, Transform, Load): ETL is the process of extracting data from various sources, transforming it into a format suitable for machine learning, and loading it into a system where it can be used for modelling.
• Example: A retail company might extract sales data from a database, transform it by calculating weekly sales totals, and load it into a machine learning pipeline.
• Feature Engineering: Feature engineering is the practice of creating new features or modifying…