Course Program Overview
AI-300 Microsoft Azure MLOps Engineer Associate
- Duration: 5 Months
- Format: Live Online / Classroom / Blended / Corporate
- Sessions: 5 per week
- Session Length: 01 Hour of each session
- Tech Stack: Azure Machine Learning (v2), GitHub Actions, Azure DevOps, MLflow, Docker, Kubernetes (AKS), ONNX, Responsible AI Dashboard, GenAIOps.
- Outcome: Industry-grade portfolio + Microsoft Certification + Career acceleration
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Our placement Record
đź“Ś Key Exam Information
Certification Name
Microsoft Certified: Azure MLOps Engineer Associate (2026 Edition)
Exam Code
AI-300
Level
Intermediate / Associate (Requires knowledge of Python and Cloud Fundamentals)
Audience
Data Scientists, DevOps Engineers, and AI Engineers who want to automate the lifecycle of Machine Learning and Generative AI models.
Exam Format
Case studies, multiple-choice, and lab-based tasks focusing on automation pipelines.
Number of Questions
Approximately 45–55
Duration
100 minutes (plus 20 minutes for setup)
Passing Score
700 out of 1000
Delivery
Online proctored or at authorized test centers
Languages Available
English, Japanese, Korean, Simplified Chinese, German, French
🎯 Who Should Take AI-300
- Data Scientists Moving to Production Professionals who know how to build models but want to master the automation, scaling, and monitoring required for enterprise-grade AI.
- DevOps Engineers Entering the AI Space Engineers looking to apply CI/CD principles to the unique challenges of Machine Learning and Large Language Models (GenAIOps).
- AI Infrastructure Leads Architects responsible for designing the systems that ensure AI models remain accurate, secure, and cost-effective over time.
🧠Exam Domains & Weightage
20–25%
Design an MLOps strategy and environment
Focus on choosing the right infrastructure, setting up Azure Machine Learning workspaces, and implementing secure data access and versioning.
30–35%
Automate model training and deployment pipelines
Learn to build CI/CD pipelines using GitHub Actions or Azure DevOps to automate the training, testing, and deployment of models to production.
20–25%
Implement monitoring and continuous retraining
Master the tools for tracking model performance, detecting data drift, and triggering automatic retraining to maintain model accuracy.
20–25%
Manage Generative AI operations (GenAIOps)
Focus on the 2026 requirement: operationalizing LLMs, managing prompt versions, and monitoring LLM costs and safety guardrails.
🏆 Benefits of AI-103 Certification
- Leader in the Agentic AI Market: Hold the primary 2026 certification for AI Agents, positioning you as a top-tier candidate for the fastest-growing job sector in technology.
- Expertise in Microsoft’s AI Stack: Demonstrate mastery over specialized tools like Semantic Kernel and Prompt Flow, which are essential for enterprise-grade AI development.
- High-Impact Salary Potential: Associate-level certifications in Agentic AI are currently commanding the highest premiums in the market, often leading to 45 LPA+ opportunities.
- Verified Production Readiness: Proves you can not only "prompt" an AI but also build, secure, and scale a complete autonomous system in a professional cloud environment.
🌟 Top 5 Reasons to Choose Adian Solutions for this course
Leading-Edge Agentic Curriculum
While others teach basic chatbot integration, Adian Solutions focuses on the 2026 shift toward Autonomous AI Agents. Our curriculum is built around the AI-103 blueprint, covering multi-agent orchestration, task planning, and memory management using industry-leading frameworks like Semantic Kernel and AutoGen.
Certification Mastery & Advanced Retake Support
We are committed to your success at the Associate level. If you do not pass the AI-103 exam on your first attempt, we provide specialized "Deep-Dive" sessions and extended lab access at no extra cost. Our team manages your exam readiness through rigorous mock tests and scenario-based simulations.
Production-Grade Agentic Labs
Experience true engineering by building stateful AI agents in our advanced lab environment. You will gain hands-on expertise in configuring Vector Databases, implementing complex RAG (Retrieval-Augmented Generation) pipelines, and deploying agents that can interact with real-world APIs and enterprise data.
Expert Portfolio & Agentic Capstone
Develop a high-impact professional portfolio through our Capstone Project. You will architect a "Multi-Agent Ecosystem" capable of collaborative problem-solving. This project demonstrates your ability to handle complex, production-level AI challenges, making you an immediate asset to global employers.
Elite Placement in the AI Sector
Leverage Adian’s exclusive network of 1,000+ global recruiters. We provide targeted career support for high-end roles, including AI Architect and Agent Developer positions. Our placement program ensures your technical certification translates into a high-paying 45 LPA+ career outcome.
Skills You Will Get
Through this course, you will master the engineering discipline of Machine Learning Operations (MLOps) on the Azure platform. You will gain expert-level proficiency in automating AI lifecycles, implementing robust CI/CD pipelines, and managing the unique operational challenges of Generative AI (GenAIOps). By completing our high-intensity technical labs and a complex MLOps capstone project, you will build a portfolio that demonstrates your ability to keep AI models accurate, scalable, and secure in production. Finally, you will command the 2026 job market with verified expertise in MLflow, Kubernetes, and automated retraining systems.
Automated CI/CD for AI
Automated CI/CD for AI
GenAIOps & LLM Management
GenAIOps & LLM Management
Model Orchestration with MLflow
Model Orchestration with MLflow
Drift Detection & Monitoring
Drift Detection & Monitoring
Continuous Retraining Pipelines
Continuous Retraining Pipelines
Containerization & Scalable Deployment
Containerization & Scalable Deployment
Responsible AI Operations
Responsible AI Operations
End-to-End MLOps Capstone
End-to-End MLOps Capstone
Course Program
(5 Months)
Classroom / Live Online
120+ Hours
This five month AI 300 program is an intensive engineering track focused on the lifecycle management of Artificial Intelligence and Large Language Models. By extending the roadmap, learners gain deeper exposure to MLOps infrastructure, automation pipelines, monitoring, and GenAIOps. The journey begins with enterprise MLOps strategy, progresses into automation pipelines, then monitoring and drift detection, followed by Generative AI operations, and finally culminates in a “Zero Touch” MLOps Capstone Project.
â—Ź Month 1
Exam Coverage: 15–20% MLOps Strategy & Infrastructure Foundations
Learners begin by mastering the blueprint of enterprise AI environments. We move away from manual model building to focus on scalable infrastructure. Students learn to set up Azure Machine Learning workspaces, manage compute clusters, and implement role based access control (RBAC). A major emphasis is placed on “Data Asset Management”—learning to version control datasets and models using MLflow. By the end of this month, participants will be able to design a secure, collaborative environment that serves as the foundation for automated AI lifecycles.
Practical Labs:
Provision Azure ML workspaces via Infrastructure as Code (IaC), configure secure datastores, and track model experiments using MLflow.
Case Studies:
How a global pharmaceutical company uses standardized MLOps environments to ensure reproducibility and regulatory compliance.
â—Ź Month 2
Exam Coverage: 20–25% Infrastructure Scaling & Experiment Management
This month deepens infrastructure skills. Learners explore advanced compute scaling, GPU provisioning, and distributed training. Students practice experiment tracking, hyperparameter tuning, and model versioning across teams. Emphasis is placed on collaboration workflows, ensuring multiple developers can contribute to shared AI projects without conflict.
Practical Labs:
Configure GPU clusters, run distributed training jobs, and implement automated experiment tracking with MLflow.
Case Studies:
How a global automotive company manages large scale model training across multiple research teams.
â—Ź Month 3
Exam Coverage: 20–25% Automation Pipelines & Production Deployment
The third month focuses on the “Ops” in MLOps. Learners master automation by building CI/CD pipelines that handle the transition from code to production. We cover GitHub Actions and Azure DevOps to trigger automated training jobs, run validation tests, and package models into Docker containers. Students deploy models to Azure Kubernetes Service (AKS) for high availability enterprise use.
Practical Labs:
Build a GitHub Actions workflow that trains a model, evaluates accuracy against a baseline, and automatically deploys it to production if validated.
Case Studies:
How an e commerce giant uses automated deployment pipelines to update recommendation engines hourly without downtime.
â—Ź Month 4
Exam Coverage: 20–25% Monitoring, Drift Detection & Closed Loop Retraining
This month introduces monitoring and continuous retraining. Learners implement Azure Monitor dashboards, detect data and model drift, and configure closed loop pipelines that trigger retraining automatically. Students learn to manage model decay and ensure AI systems remain accurate over time.
Practical Labs:
Build a drift detection system, configure alerts via Azure Monitor, and implement closed loop retraining pipelines.
Case Studies:
How a FinTech leader maintains fraud detection accuracy by retraining models against evolving criminal tactics.
â—Ź Month 5
Exam Coverage: 20–25% + Consolidation of 100% GenAIOps, Safety & Capstone Project
The final month introduces Generative AI operations. Students learn to operationalize LLMs, manage prompt versions, monitor token costs, and implement safety guardrails for autonomous agents. The program concludes with the Zero Touch MLOps Capstone Project, integrating automation, monitoring, and GenAI operations into a single portfolio ready system.
Practical Labs:
Build a GenAIOps pipeline for LLMs, implement prompt versioning, configure content safety filters, and deploy a real time monitoring dashboard.
Case Studies:
How healthcare startups operationalize LLMs with strict safety guardrails for medical assistants.
Capstone Project
Design and deploy a “Self Healing AI Factory”—an end to end system that monitors performance, retrains on new data, versions updated models via MLflow, and manages prompt iterations for Generative AI agents.
Real Roles. Real Results.
Explore Your Post-Course Career
After completing the course, learners can unlock high-impact roles such as Machine Learning Engineer, Cloud Solutions Architect, Data Scientist, and AI Product Lead—across sectors like technology, BFSI, healthcare, retail, and manufacturing.
Machine Learning Engineer
Designs, trains, and optimizes ML models for prediction, classification, and automation across industries like finance, healthcare, and manufacturing.
Data Scientist
Extracts insights from structured and unstructured data using statistical analysis, visualization, and modeling techniques. Drives decision-making and business intelligence.
AI Solutions Architect
Leads the design and deployment of scalable AI systems using cloud platforms, MLOps pipelines, and enterprise-grade frameworks
Deep Learning Researcher
Specializes in neural networks, CNNs, RNNs, GANs, and transformers. Builds models for image recognition, NLP, and generative tasks.
MLOps Engineer
Implements CI/CD pipelines, containerization, and cloud-native deployment for AI models. Ensures reliability, scalability, and performance monitoring.
Generative AI Developer
Builds intelligent applications using LangChain, Hugging Face, and LLMs. Applies RAG pipelines and transformer models to domains like legal tech and marketing.
Computer Vision Engineer
Develops image and video analysis systems using OpenCV, TensorFlow, and deep learning. Works on facial recognition, object detection, and OCR.
NLP Engineer
Creates language models and conversational AI systems using NLTK, SpaCy, and transformers. Powers chatbots, sentiment analysis, and document summarization.
AI Product Lead
Bridges technical teams and business strategy. Oversees AI product lifecycle—from ideation to deployment—ensuring alignment with market needs and ethical standards.
Frequently Asked Questions
AI-300 Microsoft Azure MLOps Engineer Associate
AI-300 is the 2026 Associate-level certification from Microsoft that focuses on MLOps (Machine Learning Operations). It replaces the older DP-100 and shifts the focus from "how to build a model" to "how to automate and run AI at scale."
Yes. While Data Science is about creating models, MLOps is about the engineering required to keep those models running, updating, and performing accurately in a real-world production environment.
GenAIOps is a core part of the AI-300 syllabus for 2026. It involves the specific operational workflows for Generative AI, such as managing prompt versions, tracking LLM costs, and monitoring the safety of autonomous agents.
A basic understanding of Python and Azure Cloud Fundamentals (like AZ-900 or AI-901) is recommended. Experience with DevOps concepts is a plus but not mandatory, as we cover the basics of CI/CD.
You will gain hands-on expertise in the industry-standard "Big Three" of automation: GitHub Actions, Azure DevOps, and MLflow, all integrated within the Azure Machine Learning ecosystem.
Adian Solutions provides 100% coverage of the Microsoft AI-300 blueprint. Our labs are specifically designed to mirror the tasks you will face in the official certification exam.
It is a fully automated system you will build that monitors a live AI model for "decay" (accuracy loss), automatically triggers a retraining job with new data, and redeploys the better model—all without human intervention.
Yes. MLOps is a high-demand sector. We offer direct placement pathways and resume optimization for roles like MLOps Engineer and AI Architect, targeting the 45 LPA+ salary bracket.
The exam is 100 minutes long and features a mix of multiple-choice questions and case studies where you must design an automation strategy for a specific business scenario.
Microsoft Associate certifications are renewed annually through a free online assessment on the Microsoft Learn portal. Adian Solutions provides updated materials to help our alumni pass these renewals easily.
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Partnering with Adian Soft Solutions transformed our AI adoption journey. Their training programs gave our team the confidence to deploy advanced ML models in production. The hands-on labs and real-world case studies were game changers.
Priya Sharma, CTO Global Tech Innovators