MLOps Engineering on AWS
Duration : 3 Days (24 Hours)
MLOps Engineering on AWS Course Overview:
This course is designed to provide you with the knowledge and skills you need to effectively build, train, and deploy machine learning (ML) models using AWS. You will learn how to use DevOps practices to automate the ML lifecycle, from data preparation to model deployment and monitoring.
The course covers the following topics:
- Data preparation: You will learn how to prepare data for ML, including data cleaning, feature engineering, and data validation.
- Model training: You will learn how to train ML models using various algorithms, including supervised learning, unsupervised learning, and reinforcement learning.
- Model deployment: You will learn how to deploy ML models to production using AWS services, such as Amazon SageMaker and Amazon Elastic Container Service (ECS).
- Model monitoring: You will learn how to monitor ML models in production to ensure that they are performing as expected.
The course also includes hands-on labs that will give you the opportunity to apply what you have learned. Each lab is designed to help you build your MLOps skills and knowledge.
By the end of this course, you will be able to:
- Understand the principles of MLOps.
- Use DevOps practices to automate the ML lifecycle.
- Train and deploy ML models using AWS services.
- Monitor ML models in production.
This course is ideal for data scientists, software engineers, and DevOps engineers who want to learn how to build, train, and deploy ML models using AWS. It is also a valuable resource for anyone who wants to learn more about MLOps.
Benefits of Attending This Course
- Gain the knowledge and skills you need to effectively build, train, and deploy ML models using AWS.
- Learn how to use DevOps practices to automate the ML lifecycle.
- Participate in hands-on labs that will give you the chance to apply what you have learned.
- Network with other professionals who are interested in MLOps.
Course level: Intermediate
Intended audience
This course is intended for any one of the following roles with responsibility for product-ionizing machine learning models in the AWS Cloud:
- DevOps engineers
- ML engineers
- Developers/operations with responsibility for operationalizing ML models
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
Required
- AWS Technical Essentials course (classroom or digital)
- DevOps Engineering on AWS course, or equivalent experience
- Practical Data Science with Amazon SageMaker course, or equivalent experience
Recommended
The Elements of Data Science (digital course), or equivalent experience
Machine Learning Terminology and Process (digital course)
Q: What is MLOps Engineering on AWS training?
A: MLOps Engineering on AWS training is a comprehensive program designed to provide individuals with the knowledge and skills needed to implement and manage end-to-end machine learning operations (MLOps) workflows using Amazon Web Services (AWS). It covers various aspects of MLOps, including model deployment, monitoring, scaling, and automation using AWS services such as Amazon SageMaker, AWS Step Functions, and AWS Lambda.
Q: Who should consider taking MLOps Engineering on AWS training?
A: This training is suitable for data scientists, machine learning engineers, software developers, and DevOps professionals who are involved in building and deploying machine learning models in production environments. It is beneficial for those who want to enhance their understanding of MLOps concepts and gain hands-on experience with AWS services for managing and scaling machine learning workflows.
Q: What topics are covered in MLOps Engineering on AWS training?
A: The training covers a range of topics, including MLOps fundamentals, model deployment strategies, managing model versions and artifacts, monitoring and logging, automation and orchestration, continuous integration and delivery (CI/CD) for machine learning, and best practices for implementing MLOps on AWS.
Q: Are there any prerequisites for taking MLOps Engineering on AWS training?
A: While there are no strict prerequisites, having a good understanding of machine learning concepts, AWS services, and experience with programming and scripting languages will be helpful in understanding the content effectively. Familiarity with DevOps principles and practices will also be advantageous.
Q: How can I prepare for MLOps Engineering on AWS training?
A: To prepare for the training, it is recommended to have a solid understanding of machine learning concepts, AWS services, and DevOps principles. Familiarize yourself with AWS services like Amazon SageMaker, AWS Step Functions, and AWS Lambda. Exploring relevant online resources, tutorials, and documentation on MLOps and AWS services can further enhance your preparation.
Q: Is MLOps Engineering on AWS training available online?
A: Yes, we offer online training options for MLOps Engineering on AWS to provide flexibility for learners.
Q: Can MLOps Engineering on AWS training help in obtaining AWS certifications?
A: While MLOps Engineering on AWS training provides valuable knowledge and skills in implementing MLOps workflows, it does not directly prepare you for specific AWS certifications. However, it lays a solid foundation for pursuing advanced certifications related to machine learning, DevOps, or AWS architecture.
Discover the perfect fit for your learning journey
Choose Learning Modality
Live Online
- Convenience
- Cost-effective
- Self-paced learning
- Scalability
Classroom
- Interaction and collaboration
- Networking opportunities
- Real-time feedback
- Personal attention
Onsite
- Familiar environment
- Confidentiality
- Team building
- Immediate application
Training Exclusives
This course comes with following benefits:
- Practice Labs.
- Get Trained by Certified Trainers.
- Access to the recordings of your class sessions for 90 days.
- Digital courseware
- Experience 24*7 learner support.
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