MLOps Essentials

About Course

The primary objective of this programme is to equip learners with the knowledge and skills required to implement MLOps practices effectively. This self-paced program in MLOps essentials provides learners with a comprehensive understanding of the essential concepts and practices involved in deploying machine learning models effectively and efficiently.

Understand fundamental concepts and techniques in machine learning to lay the foundation for MLOps.
Grasp the principles and importance of MLOps in modern data science and machine learning workflows.
Understand different types of data used in ML projects and their pre-processing requirements.
Master data engineering practices for efficient data preprocessing and transformation.
Familiarize yourself with different model serving patterns for real-time and batch processing.

Course Content

Recap of ML

Introduction to MLOps

MLOps Components

DevOps vs MLOps

Model Interpretability Demo

Data & Types

Labelling

Featuring Engineering

Feature Store

Data Engineering

Feature Management Demo

Data Engineering Demo

Categories and Detection

Experiment Management

Data Drift Demo

Hyperparameter Optimization

Hyperparameter Optimization Demo

Model Interpretability

Code Environment demo

Drift and Its Types

Model Evaluation

Deployment

Deployment Pipeline

CI/CT/CD Pipeline

Pipeline Steps

Model Serving Patterns

Git & Docker Demo

Monitoring

Monitoring Demo

CI/CT/CD Demo – Github Actions

Experiment Management Demo

Collaboration and Communication