Microsoft Certified: Machine Learning Operations (MLOps) Engineer Associate

Intermediate Level

Demonstrate your capability to automate, monitor, and manage enterprise-grade AI models at scale

through Azure Machine Learning and Azure AI Studio.

Machine Learning Operations (MLOps) Engineer Associate (Exam AI-300) serves as a mid-tier professional benchmark for AI developers, data scientists, and DevOps experts. This credential recognizes your ability to architect and maintain the complete lifecycle of machine learning and generative AI solutions, emphasizing the automation of pipelines, model governance, and the deployment of scalable AI assets. It confirms your practical mastery of Azure Machine Learning and Azure AI Studio to deliver secure, high-performance AI operations at an organizational level.

Engineer, automate, and safeguard enterprise-grade machine learning and generative AI lifecycles utilizing Azure’s advanced AI toolsets.

AI-300T00: Operationalizing Machine Learning and Generative AI Solutions is tailored for expert technical practitioners with foundational knowledge in Python, cloud-based AI, and data science. This intensive 3-day program explores:

  • Provisioning and managing robust AI infrastructure using Azure Machine Learning and Azure AI Studio.
  • Constructing automated pipelines for model training, validation, and consistent delivery.
  • Operationalizing Generative AI solutions and Large Language Models using Prompt Flow.
  • Enforcing strict security, governance, and real-time performance monitoring strategies.
  • Participating in hands-on laboratories and practical simulations mapped to the AI-300 exam objectives.

Successfully finishing this course provides you with the expertise to architect, deploy, and maintain professional AI lifecycles and excel in the AI-300 certification exam.

Fees:

RM3,000.00 – RM3,374/pax incl exam

Intakes:

20-23 July | 19-22 Oct

What are the key skills measured:

  • Architecting scalable MLOps infrastructure (15–20%).

  • Managing machine learning model lifecycles and operations (25–30%)

  • Implementing GenAIOps infrastructure (20–25%)

  • Generative AI quality assurance and observability (10–15%)

  • Optimizing system and model performance (10–15%)

Who is this for?

  • Data Scientist

  • Machine Learning Engineer

  • DevOps Engineer

Secure your leadership in the evolving digital landscape by mastering intelligent database architectures with SQL and Azure AI solutions.

According to Technode Global, Malaysia secured RM87.4 billion in digital investments in 2025, driven by key sectors such as AI, big data, data centres, and cloud services. These investments are expected to generate over 31,000 high-value digital jobs, with AI-related roles accounting for more than 12,600 positions, making AI the largest contributor to digital talent demand.
Malaysia’s Budget MADANI 2026 highlights strong relevance for MLOps Engineers, driven by the RM2 billion Sovereign AI Cloud to support secure, scalable, and compliant ML deployment, Malaysia’s commitment to becoming an AI Nation by 2030
According to Statista, Microsoft Azure is the world’s second-largest cloud infrastructure provider, holding about 21% market share in Q4 2025, with over 30% year‑over‑year growth driven by enterprise adoption, hybrid cloud capabilities, and integration with Microsoft’s ecosystem.

Mastery of the End-to-End AI Lifecycle

Authenticates your practical ability to transition machine learning and generative AI models from experimentation to production-scale environments using Azure AI Studio and Azure Machine Learning.

Advanced Operational Governance & Monitoring

Validates your expertise in supervising model performance, identifying data drift, and implementing rigorous security frameworks to maintain the integrity of enterprise AI pipelines.

Elevated Professional Influence and Credibility

As organizations prioritize the deployment of reliable AI solutions, certified MLOps engineers serve as critical bridge-builders between development and operations, making this credential a significant career advantage.

Continuous Alignment with AI Innovation

Ensures your technical skills remain current through Microsoft’s evergreen certification model, which includes regular syllabus updates and cost-free annual renewals to keep pace with the rapidly evolving AI landscape.

Why choose Trainocate?

As an award‑winning global training provider, Trainocate is recognized as the Microsoft Malaysia Learning Partner of the Year (2023 & 2024), Trainocate delivers expert‑led training aligned with the AI‑300 curriculum. Through hands‑on labs and real‑world scenarios, learners gain practical skills in MLOps and Generative AI, enabling them to pursue Microsoft certification with confidence and apply AI solutions effectively in real‑world environments.

Exam Overview

Category:

Intermediate

Exam Duration:

100 minutes

Exam Format:

65 questions

Cost:

83 USD

Language offered:

English

Candidate role examples:

Data Analyst, Data Engineer, AI Engineer.

Testing Provider:

Pearson VUE testing center or online proctored exam

Open up new possibilities for your career

Frequently Asked Questions (FAQs)

It is a specialized professional recognition that confirms your mastery in orchestrating, supervising, and protecting the complete lifecycle of both machine learning and generative AI workflows on the Microsoft Azure platform.

AI engineers, data scientists, and DevOps practitioners who possess foundational expertise in Python and have experience managing automated AI workflows within the Azure environment.

The Microsoft Certified: Machine Learning Operations (MLOps) Engineer Associate (Exam AI-300) is built for technical specialists who will:

  • Architect and automate secure AI infrastructure using Bicep, Azure CLI, and GitHub Actions;
  • Orchestrate end-to-end model lifecycles, including experiment tracking, versioning, and seamless deployment;
  • Establish GenAIOps frameworks to manage foundation models and prompt engineering workflows;
  • Execute rigorous observability and quality assurance for both traditional ML and Generative AI systems.

These competencies are vital as modern enterprises shift from isolated experiments to integrated, scalable AI operations. The certification emphasizes a unified approach (AIOps) that bridges the gap between data science and DevOps to ensure AI solutions are reliable, safe, and cost-efficient. Qualified candidates are expected to demonstrate proficiency in Python for scripting, MLflow for lifecycle management, and Microsoft Foundry for operationalizing advanced Generative AI and agentic architectures.

As the demand for production-ready AI accelerates, professionals holding this credential are prime candidates for high-impact roles such as MLOps Engineer, AI Platform Engineer, or Cloud AI Architect.

Pro Tip: Construct a comprehensive portfolio project that showcases a fully automated CI/CD pipeline from model registration in Azure Machine Learning to automated evaluation using Foundry to prove your ability to handle real world AI operational challenges.

AI-300 is relevant because organizations are moving beyond AI experiments and need professionals who can deploy, monitor, govern, and optimize machine learning and generative AI solutions in production. The growth of MLOps, GenAIOps, AI agents, cloud automation, and enterprise AI governance is increasing demand for skills in model lifecycle management, observability, CI/CD, responsible AI, and performance optimization on Azure.

This certification aligns with the industry shift from simply building AI models to operationalizing scalable, secure, and production-ready AI systems. MLOps adoption is also growing rapidly, with the global MLOps market projected to reach USD 16.6 billion by 2030.

AI-300 creates strong opportunities in Malaysia by preparing professionals to operationalise machine learning and generative AI solutions on Azure. With Microsoft expanding its local cloud infrastructure through Malaysia West and Johor Bahru, organisations will need more skilled talent in MLOps, GenAIOps, AI monitoring, automation, governance, and production-ready AI deployment. This opens career and business opportunities across sectors such as finance, healthcare, energy, public services, telco, and enterprise technology.

DP-100 proves that you can build machine learning models. AI-300 proves that you can run AI systems reliably in production. While DP-100 focuses on data science tasks such as data exploration, model training, evaluation, and deployment, AI-300 is built for today’s MLOps and GenAIOps roles, covering automation, CI/CD, monitoring, governance, observability, Microsoft Foundry, and generative AI operations. With DP-100 retiring on June 1, 2026, AI-300 is the recommended path for learners who want to validate production-ready AI operations skills on Azure.

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