Amazon SageMaker

Name: Amazon SageMaker
Rating: 4.5 (2496 reviews)

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

Highest Rated

Created byJose Portilla, Pierian Training

Last updated 5/2024

English

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.

Course content

6 sections • 30 lectures • 5h 3m total length

FAQs and Course Downloads0:26
Course Curriculum Overview3:44
Explore the Amazon SageMaker suite—SageMaker Studio notebooks, Python SDK, SageMaker Jumpstart, SageMaker Canvas, deployment, and monitoring—in a practical curriculum overview with downloadable resources, and note it is not exam prep.
What is AWS SageMaker?14:13
SageMaker is a comprehensive suite to build, train, and deploy machine learning models, not a single service; it includes SageMaker Studio, notebooks, Jumpstart, and pipelines.
Understanding SageMaker Pricing6:53
Compare on-demand pricing and savings plans for SageMaker to understand associated costs. Refer to the latest pricing documentation for free tier, regional rates, and pricing examples.

Download Penguins Data Set Here0:05
Introduction to Amazon SageMaker Canvas3:19
Understanding Data Wrangler2:46
Data Wrangler in Canvas16:57
ML Models inside of Canvas8:17
Ready-to-Use Models - Text Data7:27
Explore ready-to-use text models in SageMaker canvas for sentiment analysis, language detection, entity extraction, and personal information detection with single or batch predictions.
Ready-to-Use Models - Documents and Image Data5:25
Model Deployment with Canvas5:14
Deploy canvas models by creating a deployment, selecting the model version, and choosing the instance type and count. Activate the endpoint for real-time inferences and monitor costs.

Amazon Bedrock Overview13:54
Connecting to Amazon Bedrock via Python API24:14
Learn to connect Python with AWS via boto3 by installing boto3, configuring IAM access keys, and managing credentials in the dot AWS folder.
Text Generation Parameters Overview7:51
Amazon Titan Text Generation Model17:29
Llama Model Call12:29
Image Generation Parameters4:48
Image Generation - Stability AI16:21
Image Generation - Titan Model12:37
Explore the Titan image generator in Amazon Bedrock, generating 512x512 images from text prompts, adjusting quality to premium, and using image variation to stylize an apple on a table.
RAG - Overview8:58
Learn Rag, retrieval augmented generation, by embedding text into vectors and using cosine similarity to retrieve relevant documents. Augment prompts with that content for more accurate responses.
RAG - Example Workflow18:16
Learn how to build retrieval augmented generation workflows by embedding documents, storing vector embeddings, and performing cosine similarity to retrieve context for answering questions with Amazon Bedrock.

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.

Who this course is for:

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

Amazon SageMaker

What you'll learn

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Course content

Introduction to Course4 lectures • 25min

Introduction to AWS SageMaker4 lectures • 31min

SageMaker Canvas8 lectures • 50min

SageMaker JumpStart2 lectures • 22min

SageMaker via Python SDK2 lectures • 39min

Foundational Models on Amazon Bedrock10 lectures • 2hr 17min

Requirements

Description

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