MLOps Engineering on AWS Course
Transform ML from Experimentation to Seamless Deployment
Streamline Orchestration, Scaling, and Version Control
Unlock Top Security and Governance Practices for ML
Drive Reliable, High-Quality ML Operations with the MLOps Maturity Model
Convenient and hassle-free payment plans
Flexible learning options
4.78/53658 EnrolledEnrolled Learners
4.78/53658 EnrolledEnrolled Learners
Overview
What you will learn:
- Learn MLOps with AWS tools like SageMaker, Lambda, and S3 for seamless ML operations
- Gain hands-on experience in building, deploying, and monitoring scalable ML models
- Master automating ML workflows, model versioning, and efficient retraining techniques
- Prepare for implementing production-level MLOps strategies in real-world environments
- Understand security, governance, and compliance best practices in machine learning
- Acquire skills to scale, optimize, and manage complex ML workloads effectivel
- Get ready for certification preparation in AWS MLOps and elevate your expertise
Upcoming sessions
Curriculum
• Processes • People • Technology • Security and governance • MLOps maturity model
• 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
• 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
• 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
• Scaling and multi-account strategies • Testing and traffic-shifting • Demonstration: Using SageMaker Inference Recommender • Hands-On Lab: Testing Model Variants • Hands-On Lab: Shifting Traffic • Workbook: Multi-account strategies
• 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
Meet your Trainer
Our Trainers
Learners Point has a reputation for high-quality training that makes a difference in people's lives. We undertake a practical and innovative approach to working closely with businesses to improve their workforce. Our expertise is wide-ranging with ample support from our expert trainers who are globally recognized and hold a diverse set of experiences in their field of expertise. We are proud of our instructors who take ownership of our distinctive and comprehensive training methodologies, help our students imbibe those with ease, and accomplish gracefully.
We at Learners Point believe in encouraging our students to embark upon a journey of lifelong learning and self-development, with the aid of our comprehensive and distinctive courses tailored to current market trends. The manifestation of our career-oriented approach is what we assure through a pleasant professional enriched environment with cutting-edge technology, and an outstanding while highly acknowledged training staff that uses up-to-date methodologies and quality course material. With our aim to mold professionals to be future leaders, our industry expert trainers provide the best in town mentorship to our students while endowing them with the thirst for knowledge and inspiring them to strive for professional and human excellence.
Learning Outcomes
Upon finishing the training, you will:
1
Prepare participants to manage ML operations using AWS tools like SageMaker and Kubernetes for streamlined workflows
2
Gain hands-on experience in automating ML workflows, model deployment, and continuous monitoring
3
Develop expertise in securing ML models, scaling solutions, and integrating human-in-the-loop for model reviews
4
Understand the MLOps maturity model and adopt efficient deployment practices for scalable ML solutions
5
Equip learners with the skills to monitor models, detect data drift, and implement security best practices
KHDA Certificate
Earn a KHDA attested Course Certificate. The Knowledge and Human Development Authority (KHDA) is the educational quality assurance and regulatory authority of the Government of Dubai, United Arab Emirates.
Learners Point Certificate
Earn a Course Completion Certificate, an official Learners Point credential that confirms that you have successfully completed a course with us.
Overall ratings by our students
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Frequently asked questions
The MLOps Engineering on AWS course teaches professionals how to implement MLOps best practices using AWS tools such as SageMaker, Kubernetes, and AWS Step Functions. This intermediate-level course covers automating machine learning workflows, deploying models, and monitoring their performance in production environments.
It focuses on ensuring the scalability, security, and governance of ML solutions, enabling data scientists and DevOps engineers to efficiently manage ML operations at scale. The course prepares learners to apply MLOps principles in real-world projects, streamlining the development and deployment of ML models.
Yes, our course teaches you how to package, deploy, and automate models using AWS SageMaker, Lambda, API Gateway, and CI/CD pipelines. You will learn how to version your models, monitor them in production, and trigger retraining automatically, reducing reliance on engineering teams. Our course enables you to take your ML projects from notebooks to full-scale, real-world applications.
Our program focuses on infrastructure, automation, monitoring, and governance-all areas IT engineers can master without being ML experts. You'll learn how to build secure ML environments, manage model endpoints, implement MLOps pipelines, and monitor workloads with CloudWatch and SageMaker tools. That makes you a vital bridge between the IT operations and the data science teams of any organization.
After completing the MLOps Engineering on AWS Course, you can pursue several rewarding career paths:
- MLOps Engineer
- Machine Learning Engineer
- DevOps Engineer
- AI Solutions Architect
These roles are in high demand across industries like finance, tech, and healthcare.
The MLOps Engineering on AWS course is designed with flexibility in mind, allowing professionals to learn while managing full-time jobs. The course offers self-paced modules along with scheduled live sessions, giving you the freedom to learn at your convenience. Hands-on labs and projects ensure practical learning that you can apply directly in your work environment.
The MLOps Engineering on AWS course focuses specifically on AWS tools, providing hands-on experience with the platform’s powerful services like SageMaker and Step Functions. Unlike other MLOps courses, it emphasizes end-to-end deployment and monitoring in a production environment, ensuring you gain practical, industry-relevant skills.
The MLOps Engineering on AWS certification is globally recognized and highly valued by organizations adopting machine learning at scale. With the increasing demand for MLOps professionals, especially in industries like healthcare, finance, and technology, this certification enhances career prospects.
By completing the MLOps Engineering on AWS Course, you will gain expertise in automating ML workflows and deploying models at scale. This knowledge significantly enhances your job prospects in industries that rely on machine learning, such as tech, e-commerce, finance, and healthcare.
Roles such as MLOps Engineer, Machine Learning Engineer, DevOps Engineer, and AI Solutions Architect are within reach. Additionally, the course will position you for senior roles in managing end-to-end ML pipelines and scaling AI solutions in production environments.
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