Fundamentals of MLOps
Introduction to MLOps
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.
Practical Learning Environment
This course emphasizes practical learning through interactive labs. Embrace the opportunity to experiment, make mistakes, and grow your expertise with real-world projects.
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 industry-standard tools. Furthermore, the course introduces techniques for serving machine learning models and facilitating smooth model upgrades.
A hands-on project within the course will guide you through deploying an application for insurance claim reviews. You'll set up servers, connect them to online serving environments, and gain practical experience through guided demos and comprehensive labs.
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 cloud-based MLOps solutions, offering an overview of key features and benefits of leading platforms.
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!
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