Early last year, I wanted to learn about Machine Learning Operations(MLOps).

MLOps refers to the whole processes involved in building and deploying machine learning models reliably.

A thread on the importance of MLOps and resources that I used 🧵

As you may have heard, models are a tiny part of any typical ML-powered application.

There is nothing that stresses that as this picture:

Source: Hidden Technical Debt in Machine Learning Systems, https://t.co/JDyAr1s3kc

There are lots of critical processes that are involved in MLOps such as:

- Data processes: collection, labeling, exploration, preprocessing
- Modeling processes: building, training, evaluation, testing
- Production processes - Serving, monitoring, and maintaining models

MLOps is a new topic for almost anyone. Maintaining models for a prolonged period of time is difficult.

Models are very prone to change. They drift over time. The world (that sources the data) changes, and so data change too.

MLOps is a huge topic. All I wanted was to have a reasonable understanding of it.

Here are 3 resources that I used:

- Machine Learning Engineering book by @burkov
- MLOps Specialization by @DeepLearningAI_
- Introducing MLOps book Oreilly