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Course : Amazon Web Services (AWS) - MLOps engineering on AWSOfficial AWS course
Practical course - 3d
- 21h00 - Ref. MLS
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 | Explain the advantages of MLOps |
| Compare and contrast DevOps and MLOps |
| Assess the safety/governance needs of an ML case and propose solutions and mitigation strategies |
| Setting up experimental environments for MLOps with Amazon SageMaker |
| Present 3 options for creating a complete CI/CD pipeline in the ML context |
| Review best practices for automating packaging, testing and deployment (data/model/code) |
| Demonstrate how to monitor ML-based solutions |
| Demonstrate the automation of an ML solution: testing, packaging, deployment, drift detection and retraining |
| Explain best practices for versioning and integrity of ML assets (data, model, code) |
Intended audience
MLOps and DevOps engineers in charge of deploying and monitoring ML models on AWS
Prerequisites
Completion of the course "AWS Technical Essentials" (Ref. AWG), "DevOps Engineering on AWS"( Ref. AWC) or "Practical Data Science with Amazon SageMaker" (Ref. PDW).
Certification
Official course without certification.
Comment passer votre examen ?
Comment passer votre examen ?
Practical details
Teaching methods
Training in French. Official course material in English and digital format. Good understanding of written English.
Course schedule
1 Introduction to MLOps
- Procedures.
- Actors.
- Technologies.
- Security and governance.
- MLOps maturity model.
2 Initial MLOps - Experimentation environments in SageMaker Studio
- Integrate MLOps into the experimentation phase.
- ML environment configuration.
- Demo: creating and updating a lifecycle configuration in SageMaker Studio.
- Workbook: MLOps initial.
Hands-on work
Deploying a SageMaker Studio environment via AWS Service Catalog
3 Reproducible MLOps - Repositories
- Data management for MLOps.
- ML model version management.
- Code repositories for ML.
4 Reproducible MLOps - Orchestration
- Pipelines ML.
Demonstration
Orchestrate template creation with SageMaker Pipelines
5 Reproducible MLOps - Orchestration (continued)
- End-to-end orchestration with AWS Step Functions.
- Complete orchestration with SageMaker Projects.
- Demo: standardizing an end-to-end ML pipeline with SageMaker Projects.
- Use of third-party tools to ensure reproducibility.
- Demo: integrating the human into the inference loop.
- Governance and safety.
- Demo: good security practices with SageMaker.
- Workbook: MLOps reproducible.
Hands-on work
Automate a workflow with Step Functions
6 Reliable MLOps - Scalability and testing
- Scalability and multi-account strategies.
- Tests and traffic distribution.
- Demo: using SageMaker Inference Recommender.
Hands-on work
Testing model variants
7 Reliable MLOps - Scalability and testing (continued)
- Workbook: multi-account strategies.
Hands-on work
Traffic distribution management
8 Reliable MLOps - Supervision
- The importance of supervision in machine learning.
- Operational issues linked to model supervision.
- Resolution of problems detected by supervision.
- Workbook: Reliable MLOps.
- Practical workshop: building and troubleshooting an ML pipeline.
Hands-on work
Monitor a model for data drift
PARTICIPANTS
MLOps and DevOps engineers in charge of deploying and monitoring ML models on AWS
PREREQUISITES
Completion of the course "AWS Technical Essentials" (Ref. AWG), "DevOps Engineering on AWS"( Ref. AWC) or "Practical Data Science with Amazon SageMaker" (Ref. PDW).
TRAINER QUALIFICATIONS
The experts who lead the training courses are specialists in the subjects covered. They are approved by the publisher and certified for the course. They have also been validated by our teaching teams in terms of both professional knowledge and teaching skills for each course they teach. They have at least three to ten years of experience in their field and hold or have held positions of responsibility in companies.
TERMS AND DEADLINES
Registration must be completed 24 hours before the start of the training course.
ACCESSIBILITY FOR PEOPLE WITH DISABILITIES
Do you have specific accessibility requirements? Contact Ms FOSSE, disability advisor, at the following address: psh-accueil@orsys.fr so that we can assess your request and its feasibility.
MLOps and DevOps engineers in charge of deploying and monitoring ML models on AWS
PREREQUISITES
Completion of the course "AWS Technical Essentials" (Ref. AWG), "DevOps Engineering on AWS"( Ref. AWC) or "Practical Data Science with Amazon SageMaker" (Ref. PDW).
TRAINER QUALIFICATIONS
The experts who lead the training courses are specialists in the subjects covered. They are approved by the publisher and certified for the course. They have also been validated by our teaching teams in terms of both professional knowledge and teaching skills for each course they teach. They have at least three to ten years of experience in their field and hold or have held positions of responsibility in companies.
ASSESSMENT TERMS
Assessment of targeted skills prior to training.
Assessment by the participant, at the end of the training course, of the skills acquired during the training course.
Validation by the trainer of the participant's learning outcomes, specifying the tools used: multiple-choice questions, role-playing exercises, etc.
At the end of each training course, ITTCERT provides participants with a course evaluation questionnaire, which is then analysed by our teaching teams. Participants also complete an official evaluation of the publisher.
An attendance sheet for each half-day of attendance is provided at the end of the training course, along with a certificate of completion if the participant has attended the entire session.
Assessment of targeted skills prior to training.
Assessment by the participant, at the end of the training course, of the skills acquired during the training course.
Validation by the trainer of the participant's learning outcomes, specifying the tools used: multiple-choice questions, role-playing exercises, etc.
At the end of each training course, ITTCERT provides participants with a course evaluation questionnaire, which is then analysed by our teaching teams. Participants also complete an official evaluation of the publisher.
An attendance sheet for each half-day of attendance is provided at the end of the training course, along with a certificate of completion if the participant has attended the entire session.
TEACHING AIDS AND TECHNICAL RESOURCES
The teaching resources used are the publisher's official materials and practical exercises.
The teaching resources used are the publisher's official materials and practical exercises.
TERMS AND DEADLINES
Registration must be completed 24 hours before the start of the training course.
ACCESSIBILITY FOR PEOPLE WITH DISABILITIES
Do you have specific accessibility requirements? Contact Ms FOSSE, disability advisor, at the following address: psh-accueil@orsys.fr so that we can assess your request and its feasibility.
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