AWS Certified Machine Learning - Specialty (MLS-C01) Cert Prep: 4 Machine Learning Implementation and Operations
26mIntermediate2023-03-01
Authors
Noah Gift
MLOps Expert | Solopreneur | Author | Adjunct Professor | CTO
Course details
Join MLOps expert and CTO Noah Gift to learn all about data engineering and prepare for the machine learning implementation and operations portion of the AWS Certified Machine Learning – Specialty (MLS-C01) certification. This domain involves building machine learning solutions that are performance, available, scalability, resiliency, and fault tolerant. Noah explains using AWS environment logging and monitoring, building error monitoring, and using multiple regions and multiple AZs. He goes over how to use AMI/golden images and docker containers. Noah covers auto-scaling groups, as well as rightsizing instances, volumes, and provisioned IOPS. Plus, he shows you how to use load balancing and follow AWS best practices.
Skills covered
Machine LearningAmazon Web Services (AWS)AmazonCloud ServicesCloud PlatformsCert PrepArtificial Intelligence (AI)Cloud Computing
Concepts
0. Introduction
- 01 - Overview
1. Build ML Solutions for Performance, Availability, Scalability, Resiliency, and Fault
- 02 - AWS environment logging and monitoring
- 03 - Multiple regions, multiple AZs
- 04 - Reproducible workflows
- 05 - AWS flavored DevOps
2. Recommend and Implement the Appropriate ML Services and Features for a Given Problem
- 06 - Provisioning EC2
- 07 - Compute choices
- 08 - Provisioning EBS
- 09 - AWS AI machine learning services
3. Apply Basic AWS Security Practices to Machine Learning Solutions
- 10 - Principle of least privilege (PLP)
- 11 - Integrated security
4. Deploy and Operationalize Machine Learning Solutions
- 12 - SageMaker workflow
- 13 - Predictions with SageMaker Canvas
- 14 - Retraining models
Conclusion
- 15 - Summary