Preparing for AWS Machine Learning Certifications from Scratch
The article discusses the author's experience in preparing for AWS Machine Learning and AI Developer certifications, with a focus on understanding data formats and their impact on machine learning workflows.
Why it matters
Understanding data formats is critical for designing efficient machine learning workflows on AWS, which can impact performance, cost, and scalability.
Key Points
- 1Importance of understanding data formats for machine learning workflows
- 2Difference between validated and non-validated data formats in AWS
- 3Row-based vs. column-based data organization and their performance implications
- 4Practical analogies to explain data format concepts
Details
The article emphasizes the importance of understanding data formats when preparing for AWS machine learning certifications. It explains the distinction between validated and non-validated data formats, where validated formats are natively supported by AWS services like Amazon SageMaker, while non-validated formats require additional transformations. The author also discusses the differences between row-based and column-based data organization, and how this impacts performance and cost in machine learning workflows. To illustrate these concepts, the author uses a relatable analogy of a Pokemon card collection, where each card can be seen as a row-based data point with various attributes. The article suggests that understanding these fundamental data format principles can help developers make better decisions when building real-world machine learning solutions on AWS.
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