This course covers part of the data preparation phase of the machine learning (ML) lifecycle. In this course, you will learn about data fundamentals including how to recognize types of data, differentiate effective data from ineffective data, and visualize and analyze data. You will learn about some core Amazon Web Services (AWS) storage services used during the ML process, such as Amazon Simple Storage Service (Amazon S3). This course shares how to choose the most effective AWS storage decision and data format based on your specific ML needs. Finally, you will learn more about some AWS services that assist with ingesting, extracting, and merging data, like Amazon Kinesis.
- Course level: Advanced
- Duration: 60 minutes
Activities
- Online materials
- A demonstration
- Knowledge check questions
- A course assessment
Course objectives
- Describe the fundamentals of data collection.
- Define data formats and ingestion mechanisms.
- Describe different methods for visualizing data.
- Describe AWS storage options for ML data collection, including use cases and trade-offs.
- Choose the most effective storage decision based on cost, performance, and data structure.
- Choose the appropriate data format for an ML task based on data access patterns.
- Describe AWS streaming data sources for data ingestion.
- Extract data from AWS storage services by using AWS services that aid in data transfer.
- Describe how to merge data from multiple sources.
- Identify the cause of data ingestion and storage issues that involve capacity and scalability.
Intended audience
- Cloud architects
- Machine learning engineers
Recommended Skills
- At least 1 year of experience using Amazon SageMaker and other AWS services for ML engineering.
- At least 1 year of experience in a related role such as backend software developer, DevOps developer, data engineer, or data scientist.
- A fundamental understanding of programming languages such as Python.
- Preceding courses in the AWS ML Engineer Associate Learning Plan.
Course outline
- Section 1: Introduction
- Lesson 1: How to Use This Course
- Lesson 2: Domain 1 Introduction
- Lesson 3: Course Overview
- Lesson 4: Fundamentals of Data Collection
- Section 2: Data Collection
- Lesson 5: Types of Data
- Lesson 6: Data Visualization and Exploratory Data Analysis
- Section 3: AWS Data Sources and Services
- Lesson 7: AWS Storage Options
- Lesson 8: Choosing Storage
- Section 4: Ingest, Extract, and Merge Data
- Lesson 9: Data Ingestion
- Lesson 10: Data Extraction
- Lesson 11: Data Merging
- Lesson 12: Data Ingestion and Storage Troubleshooting
- Section 5: Conclusion
- Lesson 13: Course Summary
- Lesson 14: Assessment
- Lesson 15: Contact Us
