Course Introduction

Hello and welcome to the Fundamentals of MLOps course. I’m Raghunandan Sanur, and I'm delighted to guide you on this transformative journey into the world of MLOps. In today's fast-evolving technological landscape, MLOps has become more than just a buzzword—it is a critical component for streamlining machine learning workflows, driving innovation, and maintaining a competitive edge. Leading companies such as Google, Microsoft, and Amazon are heavily investing in MLOps, and recent reports show a 50% surge in the demand for MLOps professionals over the past year.

Throughout this course, you will participate in hands-on labs that provide a practical, real-world environment. These labs encourage experimentation, allow you to learn through trial and error, and ultimately prepare you for the challenges of implementing DevOps in production settings.

Project Files Overview

In this lab, you will work with the following files:

~/assets/05-project/synthetic_health_claims.py

~/assets/05-project/synthetic_health_claims.csv

To get started, navigate to the project directory and execute the script by running the following commands:

$ cd ~/assets/05-project
$ python synthetic_health_claims.py
Synthetic data generated at ~/assets/05-project/synthetic_health_claims.csv

Overview of MLOps Concepts

We begin by exploring the core principles of MLOps and the integral role of an MLOps engineer. This course will guide you through the entire MLOps lifecycle, including:

• Continuous Integration
• Continuous Deployment
• Continuous Training
• Continuous Monitoring

You will also learn how to select the most appropriate tools for your MLOps projects, and gain insights into high-level MLOps architectures.

Data is the backbone of every machine learning project. In this course, you will cover various data processes including data ingestion, cleaning, transformation, and the use of data lakes.

Model Development and Deployment

Next, the course delves into model development, covering topics such as model training, hyperparameter tuning, and the efficient use of CPU and GPU resources. You will learn how to deploy models into live traffic, understand model drift, and monitor model performance using tools like Prometheus and Grafana. Furthermore, the course introduces BentoML for serving machine learning models and facilitating model upgrades.

A hands-on project within the course will guide you through deploying an application for insurance claim reviews. Using MLflow in combination with BentoML, you'll set up servers and connect them to online serving environments. This section includes practical demos followed by a comprehensive hands-on lab.

Compliance and Cloud Integration

This course also addresses essential compliance topics, including GDPR, HIPAA, and PCI, highlighting the potential consequences and penalties of data mishandling. Additionally, we explore AWS SageMaker, its core components, and its integration with MLOps, offering an overview of its key features and benefits.

Engage with the Community

At KodeKloud, community matters. We foster a vibrant forum where you can ask questions, share insights, and support your fellow learners. Join the KodeKloud community and become an active participant in our dynamic learning ecosystem.

Let's embark on this MLOps journey with enthusiasm and confidence. Without further ado, let's dive into the course. See you there!