Course Content
Intro on AI/ML & AWS sign up
Amazon SageMaker is a fully managed machine learning service provided by Amazon Web Services (AWS). It is designed to help data scientists and developers build, train, and deploy machine learning models at scale. With SageMaker, you can easily create, train, and deploy machine learning models without the need to manage the underlying infrastructure.
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introductions etc, Intro to the course, expectations
00:31
Artificial Intelligence (AI) Vs. Machine Learning (ML) Vs. Deep Learning (DL)
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Machine Learning: The Big Picture
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ML – history, Types of ML
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Key Ingredients to Build Machine Learning Models
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Goals of MLOps, MLOps view of ML workflow
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differences between DevOps and MLOps
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AWS Signup and AWS Management Console Tour
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Key AI/ML Components in AWS
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Python SDK (local machine)
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Storage
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Compute
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Models
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Popular Amazon models
Amazon Web Services (AWS) provides a rich set of services and tools for implementing artificial intelligence (AI) and machine learning (ML) solutions.
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Amazon Rekognition
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Amazon Comprehend
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others
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IAM & access mgmt
Amazon Identity and Access Management (IAM) is a service provided by Amazon Web Services (AWS) that enables you to manage user access and permissions to AWS resources. IAM allows you to create and manage user accounts, assign fine-grained permissions, and control access to AWS services and resources.
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Users and Groups
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Access Management
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Roles
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Multi-Factor Authentication (MFA)
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Access Key Management
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Audit and Logging
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Amazon S3
Amazon Simple Storage Service (S3) is a highly scalable and durable object storage service provided by Amazon Web Services (AWS). It is designed to store and retrieve any amount of data from anywhere on the web. S3 offers a simple interface to store and retrieve data, making it a popular choice for storing a wide range of data types, including documents, images, videos, backups, logs, and more.
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Buckets
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Objects
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Data Durability and Availability
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Data Consistency
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Access Control:
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Storage Classes
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Data Encryption
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Lifecycle Management:
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Event Notifications
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Versioning
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DEMO : basic S3 operations (boto3)
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Introductions to Amazon Sagemaker
Amazon SageMaker is a fully managed machine learning service provided by Amazon Web Services (AWS). It is designed to simplify the process of building, training, and deploying machine learning models at scale. SageMaker provides a comprehensive set of tools and services that enable data scientists and developers to focus on the core tasks of developing and refining models, without the need to manage the underlying infrastructure.
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Notebooks and Development Environment
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Data Preparation
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Model Training
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Hyperparameter Optimization
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Model Hosting and Deployment
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Model Monitoring
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Ground Truth
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MLOps Integration
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Prepare data – (Data Wrangler)
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Amazon SageMaker Data Wrangler
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Access, select, and query data
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Generate data insights and understand data quality
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Understand your data with visualizations
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Transform data more efficiently
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Understand the predictive power of your data (quick model)
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Automate and deploy ML data preparation workflows
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Prepare data – Generate and label unstructured data
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Ground Truth
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Prepare data – Store, share, and manage model features
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Ingest data from any source
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Feature storage, catalog, search, and reuse
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Feature consistency
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Lineage tracking
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Time travel
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Security and compliance
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Build Models – SageMaker Studio notebooks
Amazon SageMaker provides all the tools and libraries you need to build ML models, the process of iteratively trying different algorithms and evaluating their accuracy to find the best one for your use case.
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Set up your Amazon SageMaker Studio domain
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Set up a SageMaker Studio notebook
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Train an XGBoost model
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Clean up the resources
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Build models – SageMaker notebook instance
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Create an Amazon SageMaker notebook instance
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Prepare the data
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Train the ML model
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Deploy the model
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Evaluate model performance
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Clean up
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Train and tune the model – Fully managed infrastructure at scale
Train and tune the model – High-performance distributed training
Train and tune the model – Built-in tools for the highest accuracy and lowest cost
Train and tune the model – Built-in tools for interactivity and monitoring
AWS – SageMaker For Data Scientists
About Lesson

SageMaker Feature Store tags and indexes feature groups so they are easily discoverable through the visual interface of Amazon SageMaker Studio. Browsing the feature catalog allows teams to discover existing features they can confidently reuse and avoid duplication of pipelines. SageMaker Feature Store uses the AWS Glue Data Catalog by default, but allows you to use a different catalog if desired. You can also query features using familiar SQL with Amazon Athena or another query tool of your choice.

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