MLOps Engineering on AWS Course in Oman

Transition ML from Trial to Scalable Deployment

Simplify orchestration, scaling, & model version management

Adopt strong security & governance practices for ML systems

Ensure consistent, high-quality ML operations using MLOps Maturity Model

Easy & convenient payment options

Flexible learning to fit your schedule

4.78/5

3658 EnrolledEnrolled Learners

4.78/5

3658 EnrolledEnrolled Learners

Overview

What you will learn:

Teaches MLOps using AWS tools like SageMaker, Lambda, & S3
Explains how to build, deploy, & monitor scalable machine-learning models
Provides skills to automate workflows, manage versions, & streamline retraining
Prepares to apply production-level MLOps in real-world scenarios
Delivers essential knowledge on ML security, governance, & compliance
Develops the ability to scale & manage complex ML systems effectively
Prepares you for the AWS MLOps certification to boost your career

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:

Learn to manage ML operations using AWS tools like SageMaker & Kubernetes

Attain hands-on skills in automation, deployment, & monitoring of ML workflows

Understand model security, scaling, & human-in-the-loop integration

Apply the MLOps maturity model for scalable deployments

Monitor models, detect data drift, & follow security best practices

Ready to get started?

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 in Oman is all about helping professionals learn how to manage, automate, and scale machine learning operations using AWS tools. The training will introduce professionals to the MLOps maturity model and teach them how to bring repeatability, reliability, and security into their ML workflows using tools like SageMaker Pipelines, AWS Step Functions, and SageMaker Projects.

Yes, it does. Even if you're coming from analytics, our course will teach you how to support and manage ML workflows. You'll learn how to automate model training, deploy models using SageMaker, and monitor their performance. These practical skills will help you move toward ML Engineering or MLOps roles in high demand across Oman's emerging tech and digital sectors.

Our course focuses on deployment, security, infrastructure, and monitoring rather than deep data science concepts. In this course, you will learn how to manage the ML environments, perform CI/CD for the ML pipelines, and ensure the production systems run smoothly. This makes you a valuable contributor to ML-focused projects without needing advanced model-building expertise.

The MLOps Engineering on AWS course includes dedicated modules on: