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

Artificial Intelligence (AI) Vs. Machine Learning (ML) Vs. Deep Learning (DL)

Machine Learning: The Big Picture

ML – history, Types of ML

Key Ingredients to Build Machine Learning Models

Goals of MLOps, MLOps view of ML workflow

differences between DevOps and MLOps

AWS Signup and AWS Management Console Tour

Key AI/ML Components in AWS

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Python SDK (local machine)

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

Amazon Comprehend

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

Access Management

Multi-Factor Authentication (MFA)

Access Key Management

Audit and Logging

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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Data Durability and Availability

Data Consistency

Access Control:

Storage Classes

Data Encryption

Lifecycle Management:

Event Notifications

DEMO : basic S3 operations (boto3)

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

Data Preparation

Hyperparameter Optimization

Model Hosting and Deployment

Model Monitoring

MLOps Integration

Prepare data – (Data Wrangler)

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Amazon SageMaker Data Wrangler

Access, select, and query data

Generate data insights and understand data quality

Understand your data with visualizations

Transform data more efficiently

Understand the predictive power of your data (quick model)

Automate and deploy ML data preparation workflows

Prepare data – Generate and label unstructured data

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Prepare data – Store, share, and manage model features

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Ingest data from any source

Feature storage, catalog, search, and reuse

Feature consistency

Lineage tracking

Security and compliance

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

Set up a SageMaker Studio notebook

Train an XGBoost model

Clean up the resources

Build models – SageMaker notebook instance

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Create an Amazon SageMaker notebook instance

Prepare the data

Train the ML model

Deploy the model

Evaluate model performance

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

Overview
Comments

About Lesson

You can ingest different types of data into SageMaker Feature Store, including streaming data from sources such as application and service logs, clickstreams and sensors, and tabular data from sources such as Amazon S3, Amazon Redshift, AWS Lake Formation, Snowflake, and Databricks Delta Lake. With Amazon SageMaker Data Wrangler you can publish features directly into SageMaker Feature Store. With the Apache Spark connector, you can batch ingest a high volume of data with a single line of code.

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