Name: MLOps Engineering on AWS
Availability: InStock
Rating: 4.6 (5878 reviews)

Duration:

24.00 hours

19.5 CPD hours

Overview:

This course builds upon and extends the DevOps methodology prevalent in software development to build, train, and deploy machine learning (ML) models. The course is based on the four-level MLOPs maturity framework. The course focuses on the first three levels, including the initial, repeatable, and reliable levels. The course stresses the importance of data, model, and code to successful ML deployments. It demonstrates the use of tools, automation, processes, and teamwork in addressing the challenges associated with handoffs between data engineers, data scientists, software developers, and operations. The course also discusses the use of tools and processes to monitor and take action when the model prediction in production drifts from agreed-upon key performance indicators.

Description:

This course builds upon and extends the DevOps methodology prevalent in software development to build, train, and deploy machine learning (ML) models. The course is based on the four-level MLOPs maturity framework. The course focuses on the first three levels, including the initial, repeatable, and reliable levels. The course stresses the importance of data, model, and code to successful ML deployments. It demonstrates the use of tools, automation, processes, and teamwork in addressing the challenges associated with handoffs between data engineers, data scientists, software developers, and operations. The course also discusses the use of tools and processes to monitor and take action when the model prediction in production drifts from agreed-upon key performance indicators.

Course Outline:

Day 1

Module 1: Introduction to MLOps

- Processes
- People
- Technology
- Security and governance
- MLOps maturity model
Module 2: Initial MLOps: Experimentation Environments in SageMaker Studio

- Bringing MLOps to experimentation
- Setting up the ML experimentation environment
- Demonstration: Creating and Updating a Lifecycle Configuration for SageMaker Studio
- Hands-On Lab: Provisioning a SageMaker Studio Environment with the AWS Service Catalog
- Workbook: Initial MLOps

Module 3: Repeatable MLOps: Repositories

- Managing data for MLOps
- Version control of ML models
- Code repositories in ML

Module 4: Repeatable MLOps: Orchestration

- ML pipelines
- Demonstration: Using SageMaker Pipelines to Orchestrate Model Building Pipelines
Day 2

Module 4: Repeatable MLOps: Orchestration (continued)

- End-to-end orchestration with AWS Step Functions
- Hands-On Lab: Automating a Workflow with Step Functions
- End-to-end orchestration with SageMaker Projects
- Demonstration: Standardizing an End-to-End ML Pipeline with SageMaker Projects
- Using third-party tools for repeatability
- Demonstration: Exploring Human-in-the-Loop During Inference
- Governance and security
- Demonstration: Exploring Security Best Practices for SageMaker
- Workbook: Repeatable MLOps

Module 5: Reliable MLOps: Scaling and Testing

- Scaling and multi-account strategies
- Testing and traffic-shifting
- Demonstration: Using SageMaker Inference Recommender
- Hands-On Lab: Testing Model Variants
Day 3

Module 5: Reliable MLOps: Scaling and Testing (continued)

- Hands-On Lab: Shifting Traffic
- Workbook: Multi-account strategies

Module 6: Reliable MLOps: Monitoring

- The importance of monitoring in ML
- Hands-On Lab: Monitoring a Model for Data Drift
- Operations considerations for model monitoring
- Remediating problems identified by monitoring ML solutions
- Workbook: Reliable MLOps
- Hands-On Lab: Building and Troubleshooting an ML Pipeline, MLOps Development, MLOps Deployment, Introduction, Introduction to MLOps, Model Monitoring and Operations, Wrap-up

Available Delivery Options for MLOps Engineering on AWS

Live Instructor Led Classroom Online (Live Online)
Traditional Instructor Led Classroom training (TILT/ILT)
Delivery at your offices in United Kingdom
Private dedicated course for your staff

How many CPD hours does MLOps Engineering on AWS provide?

The 3 day MLOps Engineering on AWS course gives you up to 19.5 CPD hours/structured learning hours.

Who is the correct audience for MLOps Engineering on AWS?

This course is intended for:

- MLOps engineers who want to productionize and monitor ML models in the AWS cloud
- DevOps engineers who will be responsible for successfully deploying and maintaining ML models in production

Do you provide training for MLOps Engineering on AWS?

Yes, we provide corporate training, dedicated training and closed classes for MLOps Engineering on AWS. This can take place anywhere in United Kingdom including London, Manchester, Birmingham, Edinburgh or live online.

What is the duration of MLOps Engineering on AWS?

The MLOps Engineering on AWS training takes place over 3 day(s), with each day lasting approximately 24.00 hours including breaks.

What other terms do people search for?

Popular related searches include: MLOps Engineering on AWS, Machine Learning, MLOE, Amazon Web Services, vILT

Why are Nexus Human the best provider for MLOps Engineering on AWS?

Nexus Human are recognised as one of the best training companies. Our trainers have won awards including Small Firms Best Trainer, national training partner of the year for Ireland, and global top 30 instructor awards in 2012, 2019 and 2021.

Is there a discount code for MLOps Engineering on AWS?

Yes, the discount code PENPAL5 is currently available for MLOps Engineering on AWS training. Only one discount code can be used per booking.

MLOps Engineering on AWS