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Data Engineering with AWS Machine Learning

3.7(34) on Pluralsight
Intermediate 2.9 hours English
SkillsData engineeringAWSData lakesData warehousesML pipelinesStorage

Is this course right for you?

Our take
A Pluralsight course on the often-overlooked layer that makes AWS machine learning actually work: the data engineering that feeds it.

Good for: The data-engineering layer behind AWS machine learning.

Skip if: You are new to AWS or want ML modelling itself.

It focuses on choosing storage options, comparing AWS database services, and using warehouses and lakes as repositories for ML — the pipeline work that happens before any model is trained. That's the part that quietly decides whether an ML project succeeds or stalls, since a model is only as good as the data plumbing behind it, and it's a genuinely useful specialism to own.

That specificity is also why it's the wrong course if you're new to AWS or you actually want the modelling side rather than the data layer. It's on a Pluralsight subscription (Standard about $29/month or $299/year, Premium about $45/month or $499/year, 10-day trial). AWS services change often, so check details against the current console (as of 2026).

Comparison · LBS

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About this course

This course focuses specifically on the data engineering layer that feeds AWS machine learning workflows: choosing between storage options, comparing database services, using data warehouses and data lakes as ML repositories, streaming versus batch ingestion, and transforming raw data with AWS Data Pipeline, Apache Spark on EMR, or serverless AWS Glue and Athena.

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Instructor

KS
Kim Schmidt
Pluralsight instructor

Kim Schmidt is an AWS Partner and Vendor instructor with prior experience at Dun & Bradstreet, Google, Microsoft, and AWS.

Frequently asked questions

No, not primarily — it is about the data-engineering side that supports machine learning on AWS: building the pipelines and infrastructure that collect, store, and prepare data for ML, rather than training models themselves. So if you want to build ML models, that is a different focus; this teaches the data plumbing that feeds them, using AWS services, which is a distinct and important part of real ML systems.

Yes, some. It uses AWS services for data engineering, so familiarity with core AWS concepts and the console makes it far smoother — ideally at least fundamentals-level knowledge. It is not an AWS-from-scratch course. If AWS is entirely new, a fundamentals course (like Cloud Practitioner material) first helps; with that grounding, the data-engineering-on-AWS content is much more approachable.

Data engineering for machine learning on AWS: using AWS services to ingest, store, process, and prepare data so it is ready for ML workflows — the pipelines and data infrastructure behind machine-learning systems. It focuses on the AWS tools and patterns for handling data at scale for ML, rather than the modelling itself, which suits people building the data side of ML on AWS.

Yes. Pluralsight offers a free trial (commonly around ten days), enough to work through a focused course like this at no cost if you are disciplined. Beyond the trial it is a subscription. Note that actually using AWS services can incur cloud charges separately, so watch your AWS billing if you follow along hands-on with real resources.

Pluralsight issues a course completion record rather than an industry certification. Recognised AWS credentials — like the AWS Certified Data Engineer or Machine Learning certifications — are earned separately through their own exams. Treat the completion as a marker of learning; the AWS certifications carry the real weight with employers, and this kind of course helps build toward them.
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