Machine Learning Operations (MLOps) provides an end-to-end machine learning development process to design, build and manage reproducible, testable, and evolvable ML-powered software.

It is a set of practices for collaboration and communication between data scientists and operations professionals. Deploying these practices increases the quality, simplifies the management process, and automates the deployment of Machine Learning models in large-scale production environments.

With this course, get introduced to MLOps concepts and best practices for deploying, evaluating, monitoring and operating production ML systems.

This course covers the following topics:

1. What is MLOps?

2. Lifecycle of an ML System

3. Activities to Productionize a Model

4. Maturity Levels in MLOps

5. What is Docker?

6. What are Containers, Virtual Machines and Pods?

7. What is Kubernetes?

8. Working with Namespaces

9. MLOps Stack Requirements

10. MLOps Landscape

11. AI Model Lifecycle

12. Introduction to Katonic MLOps Platform

13. End-to-End use case walkthrough

    1. Creating a workspace

    2. Fetching data and working with notebooks.

    3. Building an ML pipeline

    4. Registering & deploying a model

    5. Building an app using Streamlit

    6. Scheduling a pipeline run

    7. Model Monitoring

    8. Retraining a model

By the end of this course, you will be able to:

• Understand the concepts of Kubernetes, Docker and MLOps.

• Realize the challenges faced in ML model deployments and how MLOps plays a key role in operationalizing AI.

• Design an end-to-end ML production system.

• Develop a prototype, deploy, monitor and continuously improve a production-sized ML application.