Amazon Sagemaker

Learn how to leverage the power of AWS SageMaker for your Machine Learning and AI Projects on AWS!

What you'll learn

Learn to set up and navigate the AWS SageMaker console for machine learning projects.

Understand the fundamentals of AWS SageMaker, including domains and notebook instances.

Identify how to utilize SageMaker Notebook Instances for machine learning development.

Define the capabilities of the Amazon SageMaker Python SDK and its application in data processing.

Comprehend the features of SageMaker Studio Classic and its role in machine learning workflows.

Master the use of SageMaker Canvas for Auto ML, data import, wrangling, and inference.

Discover how to effectively use SageMaker Ground Truth for data labeling projects.

Apply comprehensive SageMaker knowledge in real-world scenarios through Capstone Projects.

Requirements

AWS Console Access and Full Permissions to create AWS SageMaker Resources

Description

Welcome to the ultimate online learning experience with our comprehensive AWS SageMaker Bootcamp course on Udemy! This meticulously designed course is your gateway to mastering AWS SageMaker, a powerful cloud machine learning platform that allows developers to build, train, and deploy machine learning models quickly.Embark on a learning journey starting with an introduction to the course, where we cover frequently asked questions, provide essential course downloads, and give you a detailed curriculum overview. We'll also guide you through setting up your AWS console, ensuring you're prepared to dive deep into the world of AWS SageMaker.Delve into the heart of AWS SageMaker with an in-depth exploration of what SageMaker is and how to navigate its console. Learn about SageMaker domains and how to create your own, setting the stage for practical, hands-on learning.Transform your theoretical knowledge into practical expertise with our section on SageMaker Notebook Instances. Discover the power of SageMaker Notebooks, learn how to utilize them effectively, and engage in a project that puts your newly acquired knowledge to the test.Advance your skills further with the Amazon SageMaker Python SDK. This section introduces you to the SageMaker Python Library, data processing techniques, and leads you through a project that leverages SageMaker's auto ML capabilities.Explore the possibilities with SageMaker Canvas, starting with an introduction to Auto ML. Discover the Canvas Overview, and dive into data import, data wrangling, preparation, and inference using ready-to-use models. Learn about custom model creation, model evaluation, and inference to broaden your machine learning capabilities.This course is designed for learners of all levels interested in AWS SageMaker, from beginners to advanced users looking to refine their skills. Whether you're aiming to advance your career, embark on new machine learning projects, or simply passionate about cloud computing and machine learning, this course is the perfect stepping stone to achieving your goals. Join us on this exciting journey to mastering AWS SageMaker.

Overview

Section 1: Introduction to Course

Lecture 1 FAQs and Course Downloads

Lecture 2 Course Curriculum Overview

Lecture 3 What is AWS SageMaker?

Lecture 4 Understanding SageMaker Pricing

Section 2: Introduction to AWS SageMaker

Lecture 5 AWS Console Set Up

Lecture 6 SageMaker Domains and Roles

Lecture 7 SageMaker Studio and Users

Lecture 8 SageMaker Studio JupyterLab Instance

Section 3: SageMaker Canvas

Lecture 9 Download Penguins Data Set Here

Lecture 10 Introduction to Amazon SageMaker Canvas

Lecture 11 Understanding Data Wrangler

Lecture 12 Data Wrangler in Canvas

Lecture 13 ML Models inside of Canvas

Lecture 14 Ready-to-Use Models - Text Data

Lecture 15 Ready-to-Use Models - Documents and Image Data

Lecture 16 Model Deployment with Canvas

Section 4: SageMaker JumpStart

Lecture 17 Introduction to JumpStart

Lecture 18 JumpStart Example Notebook

Section 5: Foundational Models on Amazon Bedrock

Lecture 19 Amazon Bedrock Overview

Lecture 20 Connecting to Amazon Bedrock via Python API

Lecture 21 Text Generation Parameters Overview

Lecture 22 Amazon Titan Text Generation Model

Lecture 23 Llama Model Call

Lecture 24 Image Generation Parameters

Lecture 25 Image Generation - Stability AI

Lecture 26 Image Generation - Titan Model

Lecture 27 RAG - Overview

Lecture 28 RAG - Example Workflow

Python Developers and Machine Learning Practitioners looking to use AWS SageMaker for Machine Learning