AWS · MLA-C02 · BETA
Access, networks, and secure pipelines
MLA-C02 · Lesson 22 of 23 · 2:57:38
YouTube loads only after you press play. This chapter opens at its timestamp in the full course. Watch on YouTube ↗
In this lesson
Lesson 22 of the free MLA-C02 beta series (AWS Certified Machine Learning Engineer – Associate, domain 4, operating, monitoring, and securing). In this lesson: Amazon Inspector, CodeGuru, and secrets in the pipeline, execution roles and least privilege, VPCs, endpoints, and troubleshooting access, and credentials for foundation models. With three quick checks and a recap, and AnyCompany Mobility, a car sharing service, as the running example.
Preview the topics
- A training job takes its data from one bucket prefix, and writes its model to another. Its execution role allows every S3 action on every bucket. What should the ML engineer do?
- A training job in private subnets fails with access denied on its input data. Its role allows that bucket, and the data is encrypted with a customer managed KMS key. What should the ML engineer check next?
- A batch tool runs on AWS for two hours each night, and needs an Amazon Bedrock API key to call a model. Which credential fits production?