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.
0/8
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
0/4
Python SDK (local machine)
00:00
Storage
00:00
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.
0/3
Amazon Rekognition
00:00
Amazon Comprehend
00:00
others
00:00
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.
0/6
Users and Groups
00:00
Access Management
00:00
Roles
00:00
Multi-Factor Authentication (MFA)
00:00
Access Key Management
00:00
Audit and Logging
00:00
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.
0/11
Buckets
00:00
Objects
00:00
Data Durability and Availability
00:00
Data Consistency
00:00
Access Control:
00:00
Storage Classes
00:00
Data Encryption
00:00
Lifecycle Management:
00:00
Event Notifications
00:00
Versioning
00:00
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.
0/8
Notebooks and Development Environment
00:00
Data Preparation
00:00
Model Training
00:00
Hyperparameter Optimization
00:00
Model Hosting and Deployment
00:00
Model Monitoring
00:00
Ground Truth
00:00
MLOps Integration
00:00
Prepare data – (Data Wrangler)
0/7
Amazon SageMaker Data Wrangler
00:00
Access, select, and query data
00:00
Generate data insights and understand data quality
00:00
Understand your data with visualizations
00:00
Transform data more efficiently
00:00
Understand the predictive power of your data (quick model)
00:00
Automate and deploy ML data preparation workflows
00:00
Prepare data – Generate and label unstructured data
0/1
Ground Truth
00:00
Prepare data – Store, share, and manage model features
0/6
Ingest data from any source
00:00
Feature storage, catalog, search, and reuse
00:00
Feature consistency
00:00
Lineage tracking
00:00
Time travel
00:00
Security and compliance
00:00
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.
0/4
Set up your Amazon SageMaker Studio domain
00:00
Set up a SageMaker Studio notebook
00:00
Train an XGBoost model
00:00
Clean up the resources
00:00
Build models – SageMaker notebook instance
0/6
Create an Amazon SageMaker notebook instance
00:00
Prepare the data
00:00
Train the ML model
00:00
Deploy the model
00:00
Evaluate model performance
00:00
Clean up
00:00
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

With the SageMaker Data Wrangler UI, you can launch scale to large datasets without the need to author PySpark code, install Apache Spark, or spin up clusters.

You can launch or schedule a job to quickly process your data or export it to a SageMaker Studio notebook. SageMaker Data Wrangler offers several export options, including Amazon SageMaker Data Wrangler jobs, Amazon SageMaker Feature Store, Amazon SageMaker Autopilot, and Amazon SageMaker Pipelines, providing you the ability to integrate your data preparation flow into your ML workflow. Alternatively, you can deploy your data preparation workflow to a SageMaker hosted endpoint.

Join the conversation
Previous
Next