Mlops Masters

Mastering MLOps: Build, Deploy, and Monitor Scalable Machine Learning Pipelines

What you'll learn

Gain a strong understanding of MLOps concepts and their importance in bridging the gap between machine learning and production systems.

Master the use of tools like Git, DVC, Docker, MLflow, and Grafana for efficient ML pipeline management and monitoring.

Learn to set up and use Linux commands and environments for streamlined MLOps workflows.

Explore CI/CD deployment for machine learning projects using tools like GitHub Actions, Jenkins, and CircleCI.

Develop expertise in containerizing ML applications with Docker and creating custom Docker images.

Build end-to-end machine learning pipelines for data ingestion, validation, transformation, model training, and evaluation.

Integrate AWS SageMaker to train, deploy, and serve ML models on the cloud.

Work with BentoML to deploy and manage machine learning models at scale.

Learn how to set up monitoring dashboards with Grafana for real-time application performance tracking.

Implement DVC for version control of data and pipelines, ensuring reproducibility in ML projects.

Requirements

Basic Python Programming Skills – Familiarity with Python syntax and scripting is essential.

Foundational Knowledge of Machine Learning – Understanding basic ML concepts like training, evaluation, and algorithms.

Basic Understanding of Git – Experience with version control systems is helpful but not mandatory.

Command Line Basics – Comfort with navigating and executing commands in the terminal.

Access to a Computer – A system capable of running Docker and handling machine learning workloads.

AWS Free Tier Account – Required for hands-on cloud exercises and deployment practices.

Internet Connection – Reliable internet for cloud integration and software installations.

Eagerness to Learn – A curious mindset and enthusiasm to explore MLOps tools and concepts.

Description

In today’s rapidly evolving AI landscape, deploying machine learning models to production and maintaining them at scale requires a blend of cutting-edge tools, streamlined workflows, and robust operational practices. This course on MLOps (Machine Learning Operations) is your ultimate guide to mastering the art of integrating machine learning into real-world production systems seamlessly and efficiently.Designed for data scientists, ML engineers, and developers, this course walks you through the end-to-end lifecycle of machine learning, from model development to deployment and monitoring. You’ll learn how to bridge the gap between data science and DevOps, implementing reliable, scalable, and efficient pipelines for continuous integration and delivery of ML models.This course covers essential MLOps concepts such as:Model versioning, tracking, and reproducibility.Continuous integration/continuous delivery (CI/CD) for ML.Tools like MLflow, Kubeflow, and TensorFlow Extended (TFX).Automating data pipelines and feature engineering.Monitoring models in production and detecting drift.Ensuring compliance, security, and governance in ML workflows.With practical examples and hands-on labs, you’ll gain real-world skills to optimize your ML pipelines, reduce downtime, and enhance collaboration between teams. By the end of this course, you’ll be equipped to deliver scalable, reliable, and production-ready machine learning solutions for any industry.Transform your passion for machine learning into real-world impact by mastering the tools and skills to deploy and scale with confidence!

Overview

Section 1: Introduction & MLOPs Application Overview

Lecture 1 Introduction & Overview of the course & content

Lecture 2 Prerequisite Learning Resouces

Lecture 3 Understand MLOPs with Real World Analogy

Lecture 4 Introduction to MLOps & Importance

Section 2: Linux Fundamentals for MLOps

Lecture 5 Why Linux for MLOps?

Lecture 6 Setting Up Linux with AWS EC2

Lecture 7 Required Linux Commands for MLOps

Lecture 8 Linux HandBook for Revision

Section 3: Git & GitHub Foundation

Lecture 9 Getting Started With Git And Github

Lecture 10 Local and Remote Repository Setup and Configuration

Lecture 11 How to do code management using Git

Lecture 12 Git Branch Management

Section 4: Data Version Control (DVC) for ML Pipelines

Lecture 13 Introduction to DVC and Its Importance in MLOps

Lecture 14 Build & Track ML Pipelines with DVC

Section 5: Cloud Platforms for MLOps

Lecture 15 Fundamentals of Cloud for MLOps

Section 6: MLFlow for Experiment Tracking

Lecture 16 Introduction to MLFlow and Experiment Tracking

Lecture 17 MLFlow Experiment Tracking with Dagshub

Section 7: Docker for MLOps

Lecture 18 Docker Overview: Purpose, Applications, and Problem-Solving in ML

Lecture 19 Docker Installation and Configuration (Desktop, CLI)

Lecture 20 Docker Practial Demo

Lecture 21 Creating our custom images with Docker

Section 8: Advance ML Pipeline Implementation with Modular Coding

Lecture 22 Project Introduction & Overview

Lecture 23 Github Repository Setup

Lecture 24 Project Template Creation

Lecture 25 Project Setup & Requirements Installation

Lecture 26 Logging, Utils & Exception Module

Lecture 27 Project Workflows

Lecture 28 Entire Project Notebook Experiment

Lecture 29 Data Ingestion Notebook Experiment

Lecture 30 Data Ingestion Moduler Component

Lecture 31 Data Validation Notebook Experiment

Lecture 32 Data Validation Moduler Component

Lecture 33 Data Transformation Notebook Experiment

Lecture 34 Data Transformation Moduler Component

Lecture 35 Model Trainer Notebook Experiment

Lecture 36 Model Trainer Moduler Component

Lecture 37 Model Evaluation Notebook Experiment

Lecture 38 Model Evaluation Moduler Component

Lecture 39 Prediction Pipeline

Lecture 40 User App Implementation

Lecture 41 Dockerization

Section 9: Continuous Integration & Continuous Delivery (CI/CD)

Lecture 42 Overview of CI/CD Concepts and Benefits for ML Projects

Lecture 43 CICD Deployment with Github Action

Lecture 44 CICD Deployment with Jenkins

Lecture 45 CICD Deployment with CircleCI

Section 10: End to End Chicken Disease Classification Project with DVC & MLflow

Lecture 46 Project Introduction & Overview

Lecture 47 Github Repository Setup

Lecture 48 Project Template Creation

Lecture 49 Project Setup & Requirements Installation

Lecture 50 Logging, Utils & Exception Module

Lecture 51 Project Workflows

Lecture 52 Data Ingestion Notebook Experiment

Lecture 53 Data Ingestion Moduler Component

Lecture 54 Prepare Base Model Notebook Experiment

Lecture 55 Prepare Base Model Moduler Component

Lecture 56 Model Trainer Notebook Experiment

Lecture 57 Model Trainer Moduler Component

Lecture 58 Model Evaluation Notebook Experiment with MLflow

Lecture 59 Model Evaluation Moduler Component with MLflow

Lecture 60 DVC integration for pipeline tracking

Lecture 61 Prediction Pipeline

Lecture 62 User App Implementation

Lecture 63 Dockerization

Aspiring Machine Learning Engineers – Looking to enhance their skills in deploying and managing ML models.,Data Scientists – Interested in learning how to take ML models from experimentation to production.,Software Engineers – Seeking to transition into the field of MLOps and gain hands-on experience with tools like Docker, CI/CD, and cloud platforms.,DevOps Professionals – Wanting to integrate ML workflows into existing DevOps pipelines.,AI Enthusiasts – Who want to explore the operational side of AI and ML systems.,Cloud Engineers – Focused on utilizing cloud platforms like AWS for machine learning workflows.,Students and Freshers – With basic ML and Python knowledge, aiming to build a career in MLOps.,Professionals Transitioning to AI/ML Roles – Seeking a structured and practical approach to learning MLOps tools and frameworks.