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AI-300 Microsoft Certified Machine Learning Operations Engineer Associate

Course Information

  • Sessions 5 days
  • Duration 37.5 hrs
  • Level Beginner
  • Assessment NA

Venue

Kuala Lumpur: G-3A-02, Suite Pejabat Korporat, KL Gateway, No 2, Jalan kerinchi, Gerbang kernichi Lestari, 59200 Kuala Lumpur, Malaysia
Penang: Jalan Sungai Dua, 11700 Penang, Malaysia.

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Certification

  • Certificate of Completion from Tertiary Courses - Upon meeting at least 75% attendance and passing the assessment(s), participants will receive a Certificate of Completion from Tertiary Courses.

AI-300 Microsoft Certified Machine Learning Operations Engineer Associate

Course Code: C1762
  • HRDF

6 Review(s) | Add Your Review

What's This Course About

The AI-300 Microsoft Certified Machine Learning Operations Engineer Associate course equips learners with the knowledge and skills required to set up and operate machine learning operations (MLOps) and generative AI operations (GenAIOps) on Azure. Participants will explore designing MLOps infrastructure with Azure Machine Learning, managing the model lifecycle, and building GenAIOps infrastructure with Microsoft Foundry, GitHub Actions and infrastructure as code.

Learners will gain hands-on expertise training, registering, deploying and monitoring machine learning models, deploying foundation models, and implementing prompt versioning, evaluation, observability and cost optimization for generative AI applications and agents. Additionally, the course covers optimizing retrieval-augmented generation and advanced fine-tuning. By completing this course, participants will be prepared to deliver scalable, automated and well-monitored AI solutions on Azure.

Funding and Grant Applications

No funding is available for this course

Bonus: Free Practice Exams

Get exam-ready on our Practice Exam Portal — train in realistic Practice Mode and timed Exam Mode, then retake them as many times as you like before the real exam.

Start Practising →

Course Fee

MYR7,000.00

Course Date

Course Time

Additional Note

Please bring your own laptop for hands-on training. If you don't have laptop, we can provide spare laptop for training use.

Disclaimer: The course dates displayed on our website are tentative and subject to trainer availability. We will confirm the final date after checking with the trainer. You are also welcome to email us your preferred date at sales@tertiarycourses.com.my, and we will do our best to coordinate with the trainer's schedule.

Post-Course Support

  • We may provide consultation related to the subject matter after the course.
  • Please email your queries to sales@tertiarycourses.com.my and we will forward your queries to the subject matter experts and get back to you as soon as possible.

Cancellation & Reschedule Policy

  • We reserve the right to cancel or re-schedule the course due to unforeseen circumstances. If the course is cancelled, we will refund 100% to participants.
  • Note: the venue of the training is subject to changes due to class size and availability of the classroom. The minimum class size to start a class is 3 Pax.

Course Details

Course Details

What You'll Learn

This course prepares you for the AI-300 certification exam, covering all official skills measured and their approximate weightings:

Domain 1 Design and implement an MLOps infrastructure (15-20%)

  • Create and manage a Machine Learning workspace, datastores and compute targets, and configure identity and access management
  • Create and manage data assets, environments and components, and share assets across workspaces using registries
  • Configure GitHub integration, and deploy Machine Learning workspaces and resources using Bicep and Azure CLI
  • Automate resource provisioning with GitHub Actions workflows and restrict network access to workspaces
  • Manage source control for machine learning projects using Git

Domain 2 Implement machine learning model lifecycle and operations (25-30%)

  • Orchestrate model training: configure experiment tracking with MLflow, use automated machine learning and notebooks, and automate hyperparameter tuning
  • Run training scripts, manage distributed training, implement training pipelines, and compare model performance across jobs
  • Register and version models, package feature retrieval specifications, and evaluate models using responsible AI principles
  • Deploy models as real-time or batch endpoints, test and troubleshoot endpoints, and implement progressive rollout and safe rollback
  • Monitor production models: detect data drift, track performance metrics, and configure retraining or alert triggers

Domain 3 Design and implement a GenAIOps infrastructure (20-25%)

  • Create and configure Foundry resources and project environments, and configure identity and access management with managed identities and RBAC
  • Implement network security and private networking, and deploy infrastructure using Bicep templates and Azure CLI
  • Deploy foundation models with serverless API endpoints and managed compute, and select appropriate models for use cases
  • Implement model versioning and production deployment strategies, and configure provisioned throughput units for high-volume workloads
  • Design and develop prompts, create prompt variants, and implement version control for prompts using Git repositories

Domain 4 Implement generative AI quality assurance and observability (10-15%)

  • Create test datasets and data mapping, and implement AI quality metrics including groundedness, relevance, coherence and fluency
  • Configure risk and safety evaluations for harmful content detection, and set up automated evaluation workflows
  • Monitor performance metrics including latency, throughput and response times using continuous monitoring in Foundry
  • Track and optimize cost metrics including token consumption and resource usage
  • Configure detailed logging, tracing and debugging for production troubleshooting

Domain 5 Optimize generative AI systems and model performance (10-15%)

  • Optimize retrieval-augmented generation (RAG) by tuning similarity thresholds, chunk sizes and retrieval strategies
  • Select and fine-tune embedding models for domain-specific accuracy, and implement hybrid semantic and keyword search
  • Evaluate and improve RAG performance using relevance metrics and A/B testing
  • Design and implement advanced fine-tuning methods, and create and manage synthetic data for fine-tuning
  • Monitor and optimize fine-tuned models from development through production deployment

Course Info

Promotion Code

Your will get 10% discount voucher for 2nd course onwards if you write us a Google review.

Minimum Entry Requirement

Knowledge and Skills

  • Able to operate using computer functions
  • Minimum 3 GCE ‘O’ Levels Passes including English or WPL Level 5 (Average of Reading, Listening, Speaking & Writing Scores)

Attitude

  • Positive Learning Attitude
  • Enthusiastic Learner

Experience

  • Minimum of 1 year of working experience.

Target Age Group: 18-65 years old

Minimum Software/Hardware Requirement

Software:

free Microsoft Azure account (https://azure.microsoft.com/en-us/) here 

Hardware: Window or Mac Laptops

Job Roles

Job Roles

  • Dynamics 365 Business Central Developer
  • ERP Developer
  • Business Systems Consultant
  • Dynamics 365 Administrator
  • Systems Integration Specialist
  • Power Apps Developer
  • Power Automate Specialist
  • ERP Implementation Consultant
  • Financial Systems Analyst
  • Warehouse Management Specialist
  • Supply Chain Management Specialist
  • IT Project Manager
  • Data Analyst for Business Central
  • Business Intelligence Developer
  • Reporting Specialist
  • Dynamics 365 Functional Consultant
  • Procurement and Inventory Specialist
  • Business Process Automation Specialist
  • Dynamics 365 Security Administrator
  • Technical Support Engineer

Trainers

Trainers

Saeid is co-founder of Skymics Sdn Bhd. He has 8 years of experience in the field of IoT (Internet of Things) and Information Technology. He is a certified IBM IoT Practitioner and instructor, and a Certified Citizen Data Scientist Train-The-Trainer. He has been co-inventor of 3 inventions during the last 4 years.

Review

Customer Reviews (6)

Great practical training Review by Course Participant/Trainee
1. Do you find the course meet your expectation?
2. Do you find the trainer knowledgeable in this subject?
3. How do you find the training environment
Solid content and a supportive trainer. I would definitely sign up for more courses here. (Posted on 19/01/2026)
Practical and relevant Review by Course Participant/Trainee
1. Do you find the course meet your expectation?
2. Do you find the trainer knowledgeable in this subject?
3. How do you find the training environment
The trainer was knowledgeable and explained the concepts clearly with real-world examples. (Posted on 16/01/2026)
Great practical training Review by Course Participant/Trainee
1. Do you find the course meet your expectation?
2. Do you find the trainer knowledgeable in this subject?
3. How do you find the training environment
The course content was comprehensive and up to date. The practical exercises were the best part. (Posted on 07/10/2025)
Knowledgeable trainer Review by Course Participant/Trainee
1. Do you find the course meet your expectation?
2. Do you find the trainer knowledgeable in this subject?
3. How do you find the training environment
I found the course extremely useful and relevant to my job. Highly recommend it to others. (Posted on 24/08/2025)
Great learning experience Review by Course Participant/Trainee
1. Do you find the course meet your expectation?
2. Do you find the trainer knowledgeable in this subject?
3. How do you find the training environment
A very practical course. The trainer made a complex topic simple and enjoyable to learn. (Posted on 22/01/2024)

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