ML Engineer - Collect, Ingest, and Store Data

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ML Engineer - Collect, Ingest, and Store Data

This course is part of AWS ML Engineer Associate Professional Certificate

Instructor: AWS Instructor

Included with Learn more

5 modules

Gain insight into a topic and learn the fundamentals.

Advanced level

Recommended experience

2 hours to complete

Flexible schedule

Learn at your own pace

5 modules

Gain insight into a topic and learn the fundamentals.

Advanced level

Recommended experience

2 hours to complete

Flexible schedule

Learn at your own pace

What you'll learn

Differentiate appropriate AWS storage and ingestion services for ML and AI workloads based on data requirements and compliance constraints.
Differentiate appropriate data formats based on access patterns and processing requirements.
Describe different methods for visualizing data.

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Build your Machine Learning expertise

This course is part of the AWS ML Engineer Associate Professional Certificate

When you enroll in this course, you'll also be enrolled in this Professional Certificate.

Learn new concepts from industry experts
Gain a foundational understanding of a subject or tool
Develop job-relevant skills with hands-on projects
Earn a shareable career certificate from Amazon Web Services

There are 5 modules in this course

This course covers part of the data preparation phase of the machine learning (ML) lifecycle. In this course, you will learn how to collect, ingest, and store data for ML and artificial intelligence (AI) workloads. This course covers data fundamentals, such as how to recognize data types and formats and how to choose the right storage service based on cost, performance, and access patterns. You will use Amazon Web Services (AWS) services such as Amazon Simple Storage Service (Amazon S3), Amazon Kinesis, and AWS Glue to ingest, extract, and merge data. You will also configure vector databases with Amazon OpenSearch Service and manage features with Amazon SageMaker Feature Store.

What's included

1 video4 readings

What's included

14 readings2 assignments

14 readingsTotal 15 minutes

Structuring and formatting your data1 minute
Data types in machine learning1 minute
Structured, semi-structured, and unstructured data1 minute
Data formats and file types1 minute
Row-based data format1 minute
Column-based data format1 minute
Object-notation data format2 minutes
Check your understanding1 minute
Exploring data before modeling1 minute
Goals of data visualization1 minute
Exploratory data analysis1 minute
Visualizing categorical data1 minute
Visualizing numerical data1 minute
Check your understanding1 minute

2 assignmentsTotal 4 minutes

Knowledge Check2 minutes
Knowledge Check2 minutes

What's included

19 readings3 assignments

19 readingsTotal 20 minutes

Comparing storage services1 minute
Amazon S32 minutes
Amazon EBS1 minute
Amazon EFS1 minute
Amazon FSx for Lustre1 minute
Check your understanding1 minute
Evaluating storage options1 minute
Performance1 minute
Key considerations1 minute
Data access patterns1 minute
Cost and compliance1 minute
Check your understanding1 minute
Vector databases for AI applications1 minute
How embeddings work1 minute
AWS services for vector storage1 minute
Configuring vector indexes1 minute
Chunking strategies1 minute
Choosing a vector database1 minute
Check your understanding1 minute

3 assignmentsTotal 6 minutes

Knowledge Check2 minutes
Knowledge Check2 minutes
Knowledge Check2 minutes

What's included

39 readings6 assignments

39 readingsTotal 42 minutes

Collecting data1 minute
Data ingestion methods1 minute
Data ingestion uses cases1 minute
AWS streaming services1 minute
Amazon Kinesis1 minute
Streaming use cases1 minute
Amazon MSK1 minute
Choosing the right streaming service1 minute
Check your understanding1 minute
Ingest diverse data types for AI and ML1 minute
Understanding multimodal data1 minute
Text data ingestion1 minute
Image data ingestion1 minute
Audio and video data ingestion1 minute
Metadata and cataloging1 minute
Check your understanding1 minute
Transferring data1 minute
Data transfer and extraction tools2 minutes
Extracting from AWS storage and database services2 minutes
Choosing the right extraction approach1 minute
Check your understanding1 minute
Merge and combine datasets1 minute
Merge strategies1 minute
AWS services for data merging1 minute
AWS Glue1 minute
Amazon EMR1 minute
Data quality during merging1 minute
Programming techniques for merging1 minute
Check your understanding1 minute
Storing and serving features1 minute
Feature groups1 minute
Ingestion methods1 minute
Retrieval and use cases1 minute
Check your understanding1 minute
Identify and solve issues1 minute
Monitoring tools1 minute
Common failure patterns2 minutes
Optimization techniques1 minute
Check your understanding1 minute

6 assignmentsTotal 12 minutes

Knowledge Check2 minutes
Knowledge Check2 minutes
Knowledge Check2 minutes
Knowledge Check2 minutes
Knowledge Check2 minutes
Knowledge Check2 minutes

What's included

1 video4 readings1 assignment

Earn a career certificate

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Instructor

AWS Instructor

Amazon Web Services

233 Courses394,596 learners

Offered by

Amazon Web Services

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