Welcome to "MLOps with AWS Bootcamp - Zero to Hero Series." This comprehensive course is designed for individuals aspiring to excel in artificial intelligence and machine learning (AI/ML) development or data science roles, approaching them with a Production Level mindset. Throughout this course, you will enhance your skills in designing, building, deploying, optimizing, training, tuning, and maintaining ML solutions for real-world business challenges, leveraging the power of the AWS Cloud in conjunction with DevOps best practices tailored for Machine Learning.

While you may already possess a fundamental understanding of machine learning, it's essential to recognize that employers seek more than just the basics that can be run on a local notebook.

From an employer's perspective, candidates are expected to demonstrate:

1. Proficiency in following model-training best practices on extensive cloud-based datasets.

2. Expertise in adhering to deployment best practices, ensuring consistent functionality.

3. Capability in implementing operational best practices to guarantee zero downtime.

In essence, you're expected to tackle business problems by implementing solutions on scalable datasets, moving beyond the confines of personal laptops.

Throughout this learning journey, we will follow a structured path, guiding you logically through the course material with in-depth explanations and relevant practical exercises and demonstrations.

The course is structured into the following sections:

• Section 1: Introduction to the AWSMLOPS Course and Instructor
• Section 2: Understanding MLOps
• Section 3: DevOps Principles for Data Scientists
• Section 4: Getting Started with AWS
• Section 5: Fundamentals of Linux for MLOps
• Section 6: Source Code Management using GIT and AWS CodeCommit
• Section 7: A Brief Overview of YAML
• Section 8: Deep Dive into AWS CodeBuild
• Section 9: Mastering AWS CodeDeploy
• Section 10: Streamlining with AWS CodePipeline
• Section 11: Embracing Docker Containers
• Section 12: Practical MLOps with Amazon SageMaker
• Section 13: Feature Engineering and the Feature Store in SageMaker
• Section 14: From Training to Tuning to Deploying Machine Learning Models
• Section 15: Crafting Custom Models
• Section 16: MLOps with SageMaker Pipelines

All course materials, including source code, are readily available on GitHub, ensuring convenient access from anywhere and access to the latest updates.

 

As part of this course, you will gain proficiency in a wide array of tools, technologies, and concepts:

• Data Ingestion and Collection
• Data Processing and ETL (Extract, Transform, Load)
• Data Analysis and Visualization
• Model Training and Deployment/Inference
• Operational Aspects of Machine Learning
• AWS Machine Learning Application Services
• Utilizing Notebooks and Integrated Development Environments (IDEs)
• Version Control with AWS CodeCommit
• Leveraging Amazon Athena
• Efficient Workflows with AWS Batch
• Managing Compute Resources with Amazon EC2
• Containerization with Amazon Elastic Container Registry (Amazon ECR)
• Data Transformation with AWS Glue
• Streamlining Machine Learning with Amazon SageMaker
• Monitoring with Amazon CloudWatch
• Event-Driven Computing with AWS Lambda
• Storage and Scalability with Amazon S3

Embark on this journey to elevate your AI/ML and DevOps skills to the next level, and equip yourself to solve complex business challenges using the latest tools and best practices on the AWS platform. Your success in the world of MLOps begins here.