Master Practical MLOps for Data Scientists & DevOps on AWS

Master Practical MLOps for Data Scientists & DevOps on AWS - Empower Your MLOps Journey: Unleash AI/ML Mastery on AWS with Expert Guidance - From Notebook to Production Operation

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Description

Welcome to "Practical MLOps for Data Scientists & DevOps Engineers with AWS." 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:

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

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

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.

Who this course is for:

Anyone preparing for Data Science , Machine Learning & Deep Learning Interviews
Anyone interested in learning how Machine Learning is implemented on Large scale data
Anyone interested in AWS cloud-based machine learning and data science
Anyone looking to learn the best practices to deploy the Machine Learning Models on Cloud
Anyone looking to learn the best practices to Operationalize the Machine Learning Models