MLOps Engineering on AWS Course in Saudi Arabia

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

4.8/5

4500 Enrolled

Overview

What you will master with us:

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

Learning Outcomes

Upon finishing the training, you will:

Prepare participants to manage ML operations using AWS tools like SageMaker and Kubernetes for streamlined workflows

Gain hands-on experience in automating ML workflows, model deployment, and continuous monitoring

Develop expertise in securing ML models, scaling solutions, and integrating human-in-the-loop for model reviews

Understand the MLOps maturity model and adopt efficient deployment practices for scalable ML solutions

Equip learners with the skills to monitor models, detect data drift, and implement security best practices

Ready to get started?

Overall ratings by our students

Upcoming sessions

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.

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.

Related courses

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

Frequently asked questions

Our MLOps Engineering on AWS course equips you with the skills to manage and scale machine learning operations using AWS tools. You'll learn to automate ML workflows, optimise model performance, and ensure security and governance throughout the MLOps lifecycle. This certification is ideal for professionals looking to boost their MLOps expertise and advance in the rapidly growing field of machine learning operations.

Yes, it is. Our course is perfect for a data engineer aiming to broaden their responsibilities into the ML operations sphere. You will learn how to create automated ML pipelines, deploy models using SageMaker, manage containers with EKS, and set up monitoring dashboards. These skills enable you to advance into roles like an MLOps Engineer, ML Engineer, or AI Infrastructure Specialist, which are among the fastest-growing roles in Saudi Arabia's tech and enterprise sectors.

Certainly, it does. Our course covers essential governance practices such as audit trails, model lineage, secure deployment, and drift detection, which are vital for regulated industries like fintech. You will also learn how to set up alerts, automate retraining, and monitor model performance using SageMaker Model Monitor and CloudWatch. This helps keep your models accurate, secure, and compliant with Saudi Arabia's evolving regulatory standards.

Our course teaches you how to scale ML models using SageMaker Inference Recommender and implement multi-account strategies for large-scale deployment. You will also learn how to shift traffic and manage model versions in a production environment, ensuring that models perform efficiently even under high demand.

Our course covers key areas, including setting up MLOps experimentation environments, managing ML models with version control, and orchestrating ML pipelines using SageMaker and AWS Step Functions. Additionally, it addresses scaling models with SageMaker Inference Recommender, monitoring data drift, and ensuring model security and governance. Practical labs and demonstrations enhance real-world applications.

After completing the MLOps Engineering on AWS course, you can pursue roles such as MLOps Engineer, Cloud Solutions Architect, Data Scientist, or Machine Learning Engineer. The growing demand for skilled MLOps professionals in the UAE and Saudi Arabia means you’ll have excellent career prospects, particularly in tech-driven industries.