MLOps Mastery: Deploy & Scale Your Machine Learning Models

What you will learn:

Grasp the fundamental principles, advantages, and development of MLOps.
Distinguish between MLOps and DevOps methodologies.
Establish a version-controlled MLOps project using Git and Docker.
Develop complete ML pipelines, encompassing data preprocessing and deployment.
Seamlessly transition ML models from experimentation to production.
Deploy and monitor ML models, addressing performance and data drift.
Gain practical experience with Docker for ML model containerization.
Master Kubernetes essentials and efficiently orchestrate ML workloads.
Configure local and cloud-based MLOps infrastructure (AWS, GCP, Azure).
Troubleshoot common issues related to scalability, reproducibility, and reliability.

Description

In today's rapidly evolving AI landscape, deploying and scaling machine learning models efficiently is paramount. This comprehensive course, "MLOps Mastery," bridges the gap between model development and production, empowering you to build robust and scalable AI systems. Learn to streamline your machine learning workflows through automation, version control, and continuous monitoring. We'll delve into the core principles of MLOps, exploring the entire ML lifecycle: data preparation, model training, deployment, monitoring, and scaling. Unlike traditional DevOps, MLOps addresses the unique challenges of model experimentation, versioning, and performance optimization in dynamic environments.

Hands-on experience with industry-standard tools such as Docker for containerization, Kubernetes for orchestration, and Git for version control will be central to the learning process. You'll integrate cloud platforms like AWS, GCP, and Azure, enabling scalable production deployments. Each module includes practical projects—from building end-to-end ML pipelines in Python to deploying models locally using Kubernetes. You'll tackle real-world challenges like scalability issues, model drift, and performance monitoring. By the course's end, you'll confidently transition your models from Jupyter notebooks to robust production systems, ensuring reliable and consistent results.

Whether you're a data scientist, machine learning engineer, DevOps professional, or AI enthusiast, this course equips you with the knowledge and skills to thrive in the MLOps domain. This isn't just about building models; it's about mastering deployment, monitoring, and scaling for impactful AI solutions. Join this transformative journey into the intersection of AI, ML, and operational excellence, and take your AI expertise to the next level.

Curriculum

Introduction to MLOps

This introductory section begins with an overview of MLOps, its importance in the modern AI landscape, and its evolution. Key concepts like versioning, automation, and monitoring are defined. We then explore the crucial distinctions between MLOps and traditional DevOps. The section culminates in a hands-on project where you'll set up the foundational structure for an MLOps project utilizing Git, Docker, and a model pipeline. Lectures cover: Introduction to the Section, Overview of MLOps and its Importance, Evolution of Machine Learning Operations, Key Concepts in MLOps, MLOps vs. DevOps, and a hands-on project setting up a basic MLOps Project Structure.

Data Science to Production Pipeline

This section focuses on the practical journey of an ML model from data science to a production-ready pipeline. We cover the entire workflow, from data preparation to deployment, highlighting the key differences between experimentation and production environments and commonly encountered deployment challenges. The section concludes with a comprehensive hands-on project to build an end-to-end pipeline. Lectures cover: Introduction to Section, Overview of the ML Workflow, Experimentation vs. Production, Challenges in Deploying ML Models, and hands-on building an end-to-end ML pipeline.

Infrastructure for MLOps

This section delves into the essential infrastructure needed for efficient MLOps. We introduce major cloud platforms (AWS, GCP, Azure), explore containerization using Docker, and teach you how to orchestrate ML workloads with Kubernetes. You'll learn to set up local MLOps environments and conclude with a hands-on project: containerizing a simple ML model and deploying it locally using Kubernetes. Lectures cover: Introduction to Section, Introduction to Cloud Platforms, Containerization with Docker, Kubernetes for Orchestrating ML Workloads, Setting up Local MLOps Environments, and hands-on containerizing and deploying an ML model with Kubernetes.

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