Mlops Engineering On AWS
Instructor-led AWS-aligned training in Mlops Engineering On AWS. Delivered live online by senior practitioners, with hands-on labs and a final assessment. Includes an Skilvi course-completion certificate.
Overview
This course provides a comprehensive overview of MLOps practices on the AWS platform, enabling participants to effectively manage and operationalize machine learning models. By understanding the integration of development and operations, you'll be equipped to streamline workflows and enhance collaboration in ML projects.
What you'll learn
- You will be able to design and implement MLOps pipelines on AWS.
- You will be able to deploy machine learning models using AWS services.
- You will be able to monitor and optimize model performance in production environments.
- You will be able to automate workflows for data ingestion and model training.
- You will be able to implement version control for models and datasets.
- You will be able to leverage AWS tools for security and compliance in ML operations.
- You will be able to collaborate effectively with cross-functional teams in an MLOps context.
Curriculum
5 modules · outline is indicative and can be tailored to your team.
1Introduction to MLOps
- Understanding MLOps and its importance
- Overview of the MLOps lifecycle
- Key concepts and terminology in MLOps
2AWS Services for MLOps
- Introduction to AWS AI/ML services
- Overview of Amazon SageMaker
- Using AWS Lambda for serverless ML
3Building MLOps Pipelines
- Creating data ingestion workflows
- Automating model training and tuning
- Implementing CI/CD for ML models
4Model Deployment and Monitoring
- Deploying models with Amazon SageMaker
- Setting up monitoring and logging with AWS CloudWatch
- Performance evaluation and optimization techniques
5Collaboration and Governance
- Best practices for team collaboration in MLOps
- Implementing version control with Git and DVC
- Ensuring compliance and security in ML workflows
Prerequisites
Familiarity with machine learning concepts and AWS services is beneficial.
Who should attend
This course is ideal for data scientists, ML engineers, and DevOps professionals seeking to enhance their skills in MLOps.
Certification
Prepares you for the official AWS certification exam.
Skilvi is an independent training provider and is not affiliated with, or endorsed by, AWS. Trademarks are the property of their respective owners.
Frequently asked questions
What is the delivery format of the course?
The course is instructor-led and delivered live online.
How long is the course?
The course duration is 24 hours, typically spread over several sessions.
Will I receive a certificate upon completion?
Yes, participants will receive an Skilvi course-completion certificate.
Are exam vouchers provided?
Exam vouchers are available through authorized channels on request.
What are the prerequisites for this course?
Familiarity with machine learning concepts and AWS services is beneficial.
Pricing on request
- Live online (VILT)
- 24–32 hours
- Hands-on labs & assignments
- Skilvi completion certificate
- Exam voucher on request
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