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MLOps Engineering on AWS Certification Training
MLOps Engineering
This 3-days MLOps Engineering on AWS course empowers professionals with cutting-edge techniques to streamline machine learning operations. You will learn from industry experts as you delve into AWS services, mastering the art of deploying, monitoring, and managing ML models at sc
MLOps Engineering on AWS Certification Training
• Dive into practical, hands-on training emphasizing skill mastery
• Guided by AWS Certified Instructors
• Remarkable 98% passing rate for exams
Course Overview
This MLOps Engineering on AWS course is designed to equip you with the essential skills and knowledge needed to effectively implement Machine Learning Operations (MLOps) workflows on the Amazon Web Services (AWS) cloud platform. Throughout this comprehensive course, you will delve into the intersection of machine learning and DevOps, gaining practical insights into how to streamline the deployment, monitoring, and management of machine learning models in production environments.
The course begins with an overview of MLOps concepts, emphasizing the importance of collaboration between data scientists, machine learning engineers, and operations teams. You will then dive into AWS services and tools tailored for MLOps, including Amazon SageMaker, AWS Lambda, AWS Step Functions, Amazon S3, Amazon ECR, and more. Through hands-on labs and exercises, you'll learn how to leverage these services to build scalable and robust machine learning pipelines.
As you progress, you'll explore best practices for model training, versioning, and deployment, ensuring reproducibility and consistency across your workflows. You'll also discover techniques for continuous integration and continuous deployment (CI/CD) of machine learning models, enabling you to iterate rapidly and respond to changing business needs effectively.
By the end of this course, you will be proficient in implementing end-to-end MLOps workflows on AWS, empowering you to accelerate the delivery of machine learning solutions while maintaining operational excellence and scalability. Whether you're a data scientist, machine learning engineer, or DevOps professional, this course will provide you with the skills needed to thrive in today's fast-paced ML-driven environments.
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Course Objectives
Upon completing the course, you will be able to
Gain a comprehensive understanding of Machine Learning Operations (MLOps) principles and practices within the AWS ecosystem.
• Master the utilization of AWS services such as Amazon SageMaker, AWS Lambda, and AWS Step Functions for building efficient MLOps workflows.
• Learn to seamlessly integrate machine learning models into production environments using AWS infrastructure.
• Develop proficiency in model training, versioning, and deployment strategies tailored for AWS cloud environments.
• Explore advanced techniques for model monitoring, scaling, and optimization to ensure peak performance.
• Acquire skills in continuous integration and continuous deployment (CI/CD) of machine learning models on AWS.
• Understand best practices for managing data pipelines, ensuring data quality, and maintaining compliance in MLOps workflows.
• Gain insights into cost optimization strategies specific to deploying and operating machine learning solutions on AWS.
• Enhance collaboration between data scientists, machine learning engineers, and operations teams through effective MLOps practices.
• Apply theoretical concepts to real-world scenarios, fostering the ability to architect, implement, and manage end-to-end MLOps solutions on AWS effectively.
Skills You Will Acquire:
• AWS machine learning model deployment proficiency.
• AWS MLOps service mastery.
• CI/CD pipeline implementation for AWS ML projects.
• Cost-effective AWS ML solution optimization.
• Enhanced collaboration in MLOps teamwork.
Audience
• Data Scientists
• Machine Learning Engineers
• DevOps Engineers
• Cloud Architects
• Software Developers
• Technical Managers
• IT Professionals interested in MLOps on AWS
Prerequisite
Required:
• Completion of AWS Technical Essentials
• Practical Data Science with Amazon SageMaker
• DevOps Engineering on AWS
Recommended:
• Completion of The Elements of Data Science (digital course), or equivalent experience
• Completion of Machine Learning Terminology and Process (digital course)
Course Outline
Introduction
Overview of course
Introduction to MLOps
- Machine learning operations
- The objectives of machine learning operations (MLOps)
- The transition from DevOps to MLOps
- Data science
- Extent
- An MLOps perspective of the Machine learning workflow
- Collaboration
- The significance of MLOps: MLOps instances
MLOps Development
- Introduction to constructing, training, and assessing machine learning models
- Automation
- Apache Airflow
- Integration of Kubernetes for MLOps
- Amazon SageMaker for Machine Learning Operations
- Illustration: Amazon SageMaker
- Introduction to constructing, training, and assessing machine learning models
- Illustration: Overview of Lab
- Laboratory: Incorporating your algorithm into an MLOps pipeline
- Group Exercise: MLOps Strategy Workbook
- Laboratory: Coding and deploying your ML model with AWS CodeBuild
MLOps Deployment
- Introduction to building, training, and evaluating machine learning models
- Automated processes
- Apache Airflow
- Kubernetes integration for MLOps
- Amazon SageMaker for MLOps
- Demonstration: Amazon SageMaker
- Introduction to building, training, and evaluating machine learning models
- Demonstration: Lab overview
- Lab: Integrating your algorithm into an MLOps pipeline
- Group Activity: MLOps Strategy Workbook
- Lab: Coding and deploying your ML model with AWS CodeBuild
Model Monitoring and Operations
- The significance of monitoring
- Monitoring through design
- Lab: Monitoring your ML model
- Human involvement
- Amazon SageMaker Model Monitor
- Demonstration: Amazon SageMaker Model Monitor
- Addressing the issue(s)
- Group Exercise: MLOps Action Plan Workbook
Wrap-up
- Course review
- Group Exercise: MLOps Action Plan Workbook
- Wrap-up
About the Examination
Who can take this exam? The examination targets individuals in development or data science positions with a minimum of two years of practical experience in constructing, deploying, and managing machine learning workloads within AWS. Candidates are expected to possess knowledge in ML algorithms, hyperparameter optimization, ML frameworks, and best practices in machine learning.
Choose Your Preferred Mode
Online Training
- 3 days Instructor-led Online Training
- Experienced Subject Matter Experts
- Approved and Quality Ensured training Material
- 24*7 leaner assistance and support
Corporate Training
- Blended Learning Delivery Model (Self-Paced E-Learning And/Or Instructor-Led Options)
- Course, Category, And All-Access Pricing
- Enterprise-Class Learning Management System (LMS)
- Enhanced Reporting For Individuals And Teams
- 24x7 Teaching Assistance And Support
FAQ’s
Is this course suitable for individuals new to cloud computing and IT infrastructure management?
Absolutely, this course caters to participants of all levels, including beginners. It starts with foundational concepts before advancing to more complex topics, ensuring accessibility for learners with diverse backgrounds.
Are there any prerequisites required to enroll in this course?
While prior knowledge of AWS or cloud computing is beneficial, it's not mandatory. A willingness to learn and engage in hands-on exercises is essential. The course structure accommodates participants at varying stages of their professional journey.
Are specific software or tools required for the course?
Participants need access to a computer with internet connectivity. Detailed setup instructions will be provided to ensure seamless access to course materials and resources.
Will I receive a certificate upon completion of the course?
Yes, upon successfully fulfilling all course requirements, including assignments and practical labs, participants will receive a certificate of completion from Vinsys, validating proficiency in AWS migration strategies and practices.
How are the course sessions delivered?
The course sessions are typically conducted online in a live format, facilitating real-time interaction with instructors and peers.
Are there any assessments or exams during the course?
While the course may include assignments and practical labs, there are no formal exams. Emphasis is placed on practical application and experiential learning.
Can I access course materials after completing the course?
Yes, participants will have access to course materials, including slides and recordings, for a specified period post-completion to support ongoing learning and reference.
Will I have opportunities to ask questions and seek clarification during the course?
Certainly! Instructors encourage active participation, welcoming questions and discussions during sessions to enrich the learning experience.
Is there a support system available if I encounter difficulties during the course?
Yes, participants can reach out to instructors or support staff for assistance with course-related queries or technical issues. Support is readily available to ensure a smooth learning journey.
How can I enroll in the course?
Enrollment can be done through the Vinsys website or by contacting the enrollment team for guidance through the enrollment process. They are available to assist with any questions or concerns you may have.
Why Vinsys
Seasoned Instructors
Official Vendor Partnerships
Authorized Courseware
3,000+ Courses & 2,000+ Modules
In Synch with Tech-advancements
Customizable Blended Learning Options
Reviews
I can't recommend the MLOps Engineering on AWS course at Vinsys enough! The curriculum was well-structured, covering everything from model deployment to monitoring and optimization. The interactive nature of the sessions kept me engaged, and the group activities fostered collaboration and learning from peers. The instructors were not only experts in AWS and machine learning but also passionate about imparting their knowledge. Thanks to this course, I feel confident in my abilities to implement MLOps workflows on AWS and drive impactful results for my organization.
Kajal kapurProject Manager
Enrolling in the MLOps Engineering on AWS course at Vinsys was one of the best decisions I made for my career advancement. The course content was comprehensive, and the hands-on approach allowed me to gain practical experience with AWS services like SageMaker and Kubernetes. The instructors were supportive and provided personalized guidance whenever needed. By the end of the course, I felt equipped to tackle real-world MLOps challenges confidently. I highly recommend this course to anyone looking to enhance their skills in machine learning operations on AWS.
Yogesh RaiIT Head
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