MLOps Engineering on AWS Course in Dubai
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 our training includes:
- Offers MLOps training with AWS tools like SageMaker, Lambda, and S3
- Provides hands-on experience in building, deploying, and monitoring ML models
- Develops skills in automating ML workflows, model versioning, and retraining
- Prepares learners for production-level MLOps implementation
- Teaches security, governance, and compliance in machine learning
- Equips learners with knowledge to scale and optimize ML workloads
- Enables certification preparation for AWS MLOps
Upcoming sessions
Curriculum
1
Processes
2
People
3
Technology
4
Security and governance
5
MLOps maturity model
1
Bringing MLOps to experimentation
2
Setting up the ML experimentation environment
3
Demonstration: Creating and Updating a Lifecycle Configuration for SageMaker Studio
4
Hands-On Lab: Provisioning a SageMaker Studio Environment with the AWS Service Catalog
5
Workbook: Initial MLOps
1
Managing data for MLOps
2
Version control of ML models
3
Code repositories in ML
4
Module 4: Repeatable MLOps: Orchestration
5
ML pipelines
6
Demonstration: Using SageMaker Pipelines to Orchestrate Model Building Pipelines
1
End-to-end orchestration with AWS Step Functions
2
Hands-On Lab: Automating a Workflow with Step Functions
3
End-to-end orchestration with SageMaker Projects
4
Demonstration: Standardizing an End-to-End ML Pipeline with SageMaker Projects
5
Using third-party tools for repeatability
6
Demonstration: Exploring Human-in-the-Loop During Inference
7
Governance and security
8
Demonstration: Exploring Security Best Practices for SageMaker
9
Workbook: Repeatable MLOps
1
Scaling and multi-account strategies
2
Testing and traffic-shifting
3
Demonstration: Using SageMaker Inference Recommender
4
Hands-On Lab: Testing Model Variants
5
Hands-On Lab: Shifting Traffic
6
Workbook: Multi-account strategies
1
The importance of monitoring in ML
2
Hands-On Lab: Monitoring a Model for Data Drift
3
Operations considerations for model monitoring
4
Remediating problems identified by monitoring ML solutions
5
Workbook: Reliable MLOps
6
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
2
Offer hands-on experience with automating workflows, model deployment, and monitoring
3
Develop skills in securing ML models, scaling solutions, and integrating human-in-the-loop reviews
4
Understand the MLOps maturity model and efficient deployment practices for scalability
5
Equip learners with the knowledge to monitor models, detect data drift, and apply 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 in Dubai teaches participants how to automate and manage machine learning workflows using AWS tools. It covers building, training, deploying, and monitoring machine learning models with Amazon SageMaker and other AWS services.
The course helps learners understand MLOps principles, integrating DevOps with machine learning operations, and ensuring continuous model monitoring, re-training, and scaling.
In our course, you will learn how to package, deploy, and automate your ML models independently by using AWS SageMaker, Lambda, API Gateway, and CI/CD workflows. You'll learn end-to-end MLOps practices, such as version control, drift monitoring, retraining triggers, and automated model evaluation, so that you are no longer solely dependent on DevOps teams. The goal is to ship models faster, maintain higher reliability, and contribute more effectively to production AI projects.
Our course targets IT professionals who want to support ML workloads without requiring deep data science knowledge. You'll learn infrastructure design for ML, setting up environments, managing containers, CI/CD for ML pipelines, and monitoring solutions using CloudWatch and SageMaker. You develop the skills to collaborate with data teams and manage ML systems across development, testing, and production environments.
MLOps integrates machine learning lifecycle management with DevOps principles. While DevOps focuses on software deployment, MLOps emphasizes automating workflows for model training, deployment, monitoring, and performance tracking, ensuring machine learning models are reliable and scalable.
It also handles unique challenges such as data drift, model retraining, experiment tracking, and versioning of datasets and models. In practice, MLOps brings together data scientists, ML engineers, and DevOps teams to deliver AI solutions that remain accurate and robust over time.
You’ll learn how to monitor ML models in production using Amazon CloudWatch and SageMaker Model Monitor. The course covers techniques to detect performance degradation, data drift, and other anomalies, ensuring your models are consistently accurate and reliable.
You will also learn how to set alerts, trigger automated retraining, and log important metrics so issues are flagged early. This end-to-end monitoring approach builds trust in your AI systems and supports continuous improvement in models that you have deployed.
This course prepares you for various roles, including:
- MLOps Engineer
- Machine Learning Engineer
- Data Scientist
- AWS Cloud Specialist
- AI/ML Consultant
Our course equips you with practical skills in automating ML workflows, model deployment, and monitoring using AWS. It prepares you for MLOps roles and certification, making you an asset to organizations looking to streamline machine learning operations.
At the same time, you will be practically exposed to the most in-demand tools in real-world AI projects, including SageMaker, Lambda, and S3. These skills will therefore be highly sought after in Dubai's fast-growing AI and cloud ecosystem, positioning you for highly impactful, future-ready roles.
The MLOps Engineering on AWS in Dubai focuses on Amazon SageMaker, AWS Lambda, AWS Step Functions, and other AWS services essential for building, training, deploying, and monitoring machine learning models in a scalable manner.
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