MLOps Engineering on AWS
With the help of the MLOps Engineering on AWS course, students will get the skills they need to apply machine learning (ML) operations on the AWS platform. This extensive course covers every aspect of MLOps, including its goals and guiding principles, moving from DevOps to MLOps, and comprehending the ML workflow in relation to MLOps. Additionally, it explores development processes, including the creation, training, and evaluation of machine learning models, with an emphasis on security, integration with tools like as Apache Airflow and Kubernetes, and the use of Amazon SageMaker for more efficient operations.
Overview
This course builds upon and extends the DevOps practice prevalent in software development to build,
train, and deploy machine learning (ML) models. The course stresses the importance of data, model, and
code to successful ML deployments. It will demonstrate 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 will also discuss the use of tools and processes to monitor
and take action when the model prediction in production starts to drift from agreed-upon key performance
indicators.
What you will Learn in this MLOps Engineering on AWS Course?
- Understand the concept of Machine Learning Operations (MLOps) and its goals in automating ML workflows.
- Learn the transition from traditional DevOps to MLOps and the unique considerations in ML workflows.
- Gain hands-on experience with AWS services to build, train, and evaluate machine learning models within MLOps pipelines.
- Acquire knowledge on integrating security best practices into MLOps processes.
- Familiarize with Apache Airflow and Kubernetes for orchestrating and scaling ML workflows.
- Master the use of Amazon SageMaker’s suite of tools to streamline the MLOps lifecycle, including model training, tuning, and deployment.
- Develop skills to package models, manage inference operations, and deploy models to production with robustness and scalability.
- Conduct A/B testing and deploy models to edge devices, understanding various deployment patterns.
- Implement monitoring solutions for ML models using Amazon SageMaker Model Monitor and learn the importance of monitoring by design.
- Create an MLOps Action Plan and troubleshoot common issues in MLOps pipelines, ensuring continuous improvement and operational excellence.
Who should take up this MLOps Engineering on AWS Course?
- Data Scientists seeking to streamline ML workflows
- DevOps Engineers transitioning into MLOps roles
- Machine Learning Engineers interested in operationalizing ML models
- IT Professionals aiming for expertise in deploying and monitoring ML models on AWS
- Cloud Engineers looking to specialize in ML infrastructure on AWS
- Software Engineers wanting to understand the MLOps lifecycle
- AI/ML Product Managers overseeing the end-to-end ML model lifecycle
- Technical Project Managers looking to manage MLOps projects
- AWS Certified professionals aiming to deepen their MLOps knowledge
- System Administrators interested in ML model deployment and management
Our Package
Module 0: Welcome
- Course introduction
Module 1: Introduction to MLOps
- Machine learning operations
- Goals of MLOps
- Communication
- From DevOps to MLOps
- ML workflow
- Scope
- MLOps view of ML workflow
- MLOps cases
Module 2: MLOps Development
- Intro to build, train, and evaluate machine learning models
- MLOps security
- Automating
- Apache Airflow
- Kubernetes integration for MLOps
- Amazon SageMaker for MLOps
- Lab: Bring your own algorithm to an MLOps pipeline
- Demonstration: Amazon SageMaker
- Intro to build, train, and evaluate machine learning models
- Lab: Code and serve your ML model with AWS CodeBuild
- Activity: MLOps Action Plan Workbook
Module 3: MLOps Deployment
- Introduction to deployment operations
- Model packaging
- Inference
- Lab: Deploy your model to production
- SageMaker production variants
- Deployment strategies
- Deploying to the edge
- Lab: Conduct A/B testing
- Activity: MLOps Action Plan Workbook
Module 4: Model Monitoring and Operations
- Lab: Troubleshoot your pipeline
- The importance of monitoring
- Monitoring by design
- Lab: Monitor your ML model
- Human-in-the-loop
- Amazon SageMaker Model Monitor
- Demonstration: Amazon SageMaker Pipelines, Model Monitor, model registry, and Feature Store
- Solving the Problem(s)
- Activity: MLOps Action Plan Workbook
Module 5: Wrap-up
- Course review
- Activity: MLOps Action Plan Workbook
- Wrap-up
Upcoming Batch
April 20th (Weekends)
FRI & SAT (4 Weeks)
08:30 PM to 01:00 AM (CDT)
April 18th (Weekdays)
MON – FRI (18 Days)
10:00 AM to 12:00 PM (CDT)
MLOps Engineering on AWS FAQs
Q. What will I learn in the MLOps Engineering on AWS training?
Ans.
The MLOps Engineering on AWS course teaches deployment, management, and automation of machine learning models in production, covering AWS tools, CI/CD, monitoring, scaling, and security, improving reliability and efficiency in ML workflows.
Q. What are the career prospects after completing the MLOps Engineering on AWS training?
Ans.
After MLOps Engineering on AWS training, prospects include roles like MLOps Engineer, AI/ML Engineer, Data Scientist, with potential in tech, finance, healthcare. Advancement to lead roles, boosting career trajectory.
Q. What are the prerequisites for enrolling in the MLOps Engineering on AWS course?
Ans.
- Basic understanding of machine learning concepts and terminology.
- Familiarity with cloud computing principles, particularly the AWS ecosystem.
- Experience with DevOps practices and tools.
- Knowledge of programming and scripting languages such as Python.
- Comfort with command-line interfaces and development environments.
- Prior exposure to machine learning model building, training, and evaluation processes.
- Understanding of containerization technologies, ideally Docker and Kubernetes.
Q. What is the duration of the MLOps Engineering on AWS course?
Ans.
The duration of the course 24 hours.
Q. What is required for online training?
Ans.
A laptop, decent internet speed, a Headset with microphone is required.
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