MLOps Engineering on AWS Training & Certification Course
MLOps Engineering on AWS equips DevOps engineers, ML engineers, and operations professionals with skills to automate, deploy, and monitor machine learning models in AWS, solving the critical industry challenge of operationalizing ML at scale.
Course Overview
The course focuses on key AWS technologies including Amazon SageMaker, AWS Step Functions, SageMaker Pipelines, AWS CodeBuild, and SageMaker Model Monitor, all accessed through the AWS Console. Participants engage in hands-on labs where they build automated ML pipelines that handle model training, testing, and deployment, while also implementing monitoring solutions for data drift and model bias. A core project involves configuring a full CI/CD pipeline using SageMaker Projects and AWS infrastructure to automate end-to-end model orchestration, enabling students to gain practical experience in real-world MLOps scenarios.
MLOps Engineering on AWS prepares candidates for the AWS Certified Machine Learning Engineer – Associate (MLA-C01) certification, a role-based credential recognized for validating expertise in operationalizing ML solutions on AWS. Professionals with this certification are well-positioned for career advancement, with ML engineers commanding average salaries around $125,000 according to industry reports. Optiv Solutions supports learners with official AWS courseware, expert-led instruction, and a Guaranteed-to-Run schedule, ensuring reliable access to training. Graduates emerge ready to lead scalable, secure, and maintainable ML deployments in production environments.
Skills You’ll Develop
Who Should Attend
WHO SHOULD ATTEND (TARGET AUDIENCE)
• MLOps Engineers
• Data Scientists
• DevOps Engineers
• Cloud Engineers
• AWS Solutions Architects
• AI/ML Developers
• Software Engineers working on ML applications
• Data Engineers
• ML Platform Engineers
• DevSecOps Professionals
• Cloud Architects responsible for AI/ML workloads
• IT Professionals involved in ML model deployment and operations
• Professionals responsible for ML workflow automation and monitoring
• Technical leads managing enterprise AI/ML infrastructure
Pre-requisites
RECOMMENDED KNOWLEDGE BEFORE TAKING THIS COURSE
- ✓ Basic understanding of machine learning concepts and workflows
- ✓ Familiarity with AWS cloud services and the AWS Management Console
- ✓ Working knowledge of Python and basic programming concepts
- ✓ Understanding of DevOps and CI/CD principles
- ✓ Familiarity with source control systems such as Git
- ✓ Basic knowledge of cloud computing and infrastructure concepts
- ✓ Understanding of machine learning model development and deployment processes
- ✓ Experience with AWS services such as Amazon S3 and Amazon SageMaker is beneficial
- ✓ Basic knowledge of monitoring, automation, and deployment practices
Certification Exam Details
Everything you need to know about the certification exam
Exam Details
Upcoming Batch Schedule
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Curriculum & Course Syllabus
Module 1: Introduction to MLOps
- Machine Learning Operations (MLOps)
- Goals and benefits of MLOps
- DevOps vs. MLOps
- People, processes, and technology
- Machine learning workflow
- MLOps scope and lifecycle
- Communication and collaboration
- MLOps use cases
- Security and governance
- MLOps maturity model
Module 2: MLOps Development and Experimentation
- ML experimentation environments
- Amazon SageMaker for MLOps
- SageMaker Studio
- Setting up ML experimentation environments
- Building, training, and evaluating ML models
- MLOps automation
- MLOps security
- Apache Airflow and Kubernetes integration
- Code and model development workflows
- Hands-on MLOps pipeline development
Module 3: Repeatable MLOps – Repositories and Versioning
- Managing data for MLOps
- Data versioning
- ML model version control
- Code repositories
- Model artifacts and integrity
- Model packaging
- Reproducible ML workflows
- Managing ML assets across environments
Module 4: Repeatable MLOps – Orchestration
- ML pipelines
- Amazon SageMaker Pipelines
- Automated model building workflows
- AWS Step Functions
- End-to-end workflow orchestration
- SageMaker Projects
- CI/CD for machine learning
- Automated testing and deployment
- Third-party tools for MLOps
- Human-in-the-loop workflows
- Governance and security for ML pipelines
Module 5: Reliable MLOps – Scaling and Testing
- Scaling ML workloads
- Multi-account strategies
- ML infrastructure scalability
- Model testing
- Model validation
- Traffic shifting
- SageMaker production variants
- A/B testing
- Deployment strategies
- Amazon SageMaker Inference Recommender
- Testing model variants
- Production deployment considerations
Module 6: Reliable MLOps – Monitoring and Operations
- Importance of ML model monitoring
- Monitoring by design
- Amazon SageMaker Model Monitor
- Data drift detection
- Model performance monitoring
- Inference monitoring
- Model bias monitoring
- Resource consumption and latency monitoring
- Detecting model degradation
- Troubleshooting ML pipelines
- Remediation of monitoring issues
- Automated model retraining
- Operational considerations for production ML
Module 7: MLOps Security and Governance
- ML security fundamentals
- Security threats in ML environments
- SageMaker security best practices
- Identity and access management
- Data protection
- Governance controls
- Multi-account security strategy
- Secure ML deployment
- Compliance considerations
- Human-in-the-loop security controls
Module 8: End-to-End MLOps Implementation
- Building an automated ML workflow
- Automated model training
- Automated testing
- Model packaging
- Automated deployment
- Model versioning and registry
- Continuous monitoring
- Data drift detection
- Automated retraining
- Production troubleshooting
- Building and operating a complete MLOps pipeline
Student Reviews & Testimonials
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Frequently Asked Questions
Is the certification exam included in the MLOps Engineering on AWS course, and what is the exam fee if separate?
The MLOps Engineering on AWS course does not include the certification exam; the AWS Certified Machine Learning - Specialty exam costs 300 USD. Candidates can use a 50% discount voucher from their AWS Certification account for recertification, and taxes may apply depending on the region.
How long is lab access provided for the MLOps Engineering on AWS course, and what environment is used?
Lab access for the MLOps Engineering on AWS course is provided for 6 months from the course delivery date using AWS-provided cloud sandboxes via Amazon SageMaker. These hands-on labs are conducted in a live virtual environment with real-time instructor guidance.
What is the format, number of questions, passing score, and time limit for the AWS Certified Machine Learning - Specialty exam?
The AWS Certified Machine Learning - Specialty exam has 65 questions, including multiple choice and multiple response types, a time limit of 180 minutes, and a passing score of 750 out of 1000 based on a scaled scoring model established by AWS professionals.
How long is the AWS Certified Machine Learning - Specialty certification valid, and what is the renewal process and cost?
The AWS Certified Machine Learning - Specialty certification is valid for 3 years; renewal requires passing the current version of the exam at a cost of 300 USD or using a 50% discount voucher from the AWS Certification account for recertification.
What are the prerequisites or experience needed to attend the MLOps Engineering on AWS course?
Prerequisites for MLOps Engineering on AWS include AWS Technical Essentials, DevOps Engineering on AWS, and Practical Data Science with Amazon SageMaker courses or equivalent experience, ensuring attendees have foundational knowledge in AWS services and DevOps practices.