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)
00:00
Machine Learning: The Big Picture
00:00
ML – history, Types of ML
00:00
Key Ingredients to Build Machine Learning Models
00:00
Goals of MLOps, MLOps view of ML workflow
00:00
differences between DevOps and MLOps
00:00
AWS Signup and AWS Management Console Tour
00:00
Key AI/ML Components in AWS
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Python SDK (local machine)
00:00
Storage
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Compute
00:00
Models
00:00
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
00:00
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
00:00
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:
00:00
Event Notifications
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Versioning
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DEMO : basic S3 operations (boto3)
00:00
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
00:00
Data Preparation
00:00
Model Training
00:00
Hyperparameter Optimization
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Model Hosting and Deployment
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Model Monitoring
00:00
Ground Truth
00:00
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

  1. Access Control: S3 offers several mechanisms for controlling access to your buckets and objects. You can use AWS Identity and Access Management (IAM) policies, bucket policies, access control lists (ACLs), and presigned URLs to define granular access permissions.

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