Course Program Overview
Specialisation in AI in Microsoft Azure
- Duration: 3 Months
- Format: Live Online / Classroom / Blended / Corporate
- Sessions: 5 per week
- Session Length: 1 Hour each
- Outcome: Industry-grade portfolio + Certification + Career acceleration
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Our placement Record
Specialisation in AI in Microsoft Azure
This professional program empowers learners to build, train, and deploy intelligent systems using Microsoft Azure’s AI and ML ecosystem. Designed for engineers, developers, and enterprise teams, the course blends foundational cloud skills with advanced modeling, generative AI, and full-scale deployment. Participants gain hands-on experience with Azure Machine Learning, Cognitive Services, OpenAI APIs, and orchestration tools like Azure Functions and Logic Apps. Real-world projects span domains such as healthcare, finance, retail, and automation—culminating in a capstone showcase aligned with global enterprise standards.
Build AI. Don’t Just Use It.
Join the only AI & ML course that teaches you the math, programming, and mindset behind ₹1 crore+ careers.
Why Choose Us
- Deep Mathematics: Calculus, probability, ability linear algebra, optimization
- Algorithm-Centric Programming: Python, TensorFlow, custom architectures
- Career-Built Curriculum: Designed for ₹1 crore+ AI roles
- Thought Leaders as Mentors: Resume coaching, GitHub setup, interview prep
Can You Crack This AI Math Challenge?
Solve for the gradient
Where exact behind how ChatGPT ables.
Your ₹1 Crore Career Starts Here.
Your ₹1 Crore Career Starts Here.
Who’s Hiring AI/ML Experts?
We design our training programs around real-world demand — here's who’s building with AI:
Tech & Cloud Leaders
Google · Microsoft · Amazon · Meta · Apple · IBM · NVIDIA
Scaling enterprise AI, cloud services, and generative technologies
AI Research & Platforms
OpenAI · DeepMind · Hugging Face · Anthropic · Cohere
Pioneering large language models, multimodal learning, and democratized AI
Industrial & Robotics AI
Tesla · Waymo · Boston Dynamics · ABB
Deploying autonomous systems for mobility, automation, and smart infrastructure
Consulting & Strategy
Accenture · PwC · Deloitte · EY · Palantir
Leading AI adoption and transformation for Fortune 500 clients
Finance & Fintech
JPMorgan · Goldman Sachs · Capital One · Stripe
Revolutionizing risk modeling, fraud detection, and customer intelligence
Media & Digital Productivity
Netflix · Spotify · Zoom · Notion · LinkedIn
Enhancing personalization with recommendation engines and content intelligence
Skills you will get
In this course, you’ll master the full lifecycle of enterprise-grade AI models—building, training, deploying, and automating with Microsoft Azure's native tools. You'll also learn to apply generative AI and LLMs to real-world problems using OpenAI, Cognitive Services, and MLOps pipelines.
30+ Tools
30+ Tools
20+ Real time project
20+ Real time project
Azure ML Deployment
Azure ML Deployment
Generative AI Engineering
Generative AI Engineering
Cloud Data Automation
Cloud Data Automation
Ethical AI Compliance
Ethical AI Compliance
MLOps Integration
MLOps Integration
AI Product Development
AI Product Development
Course Program
(3 units)
Classroom/Live online
03 months (80+ Hours)
● Unit 1
Azure AI Foundations & Cognitive Services
This unit introduces learners to the Azure ecosystem and its core AI services. Participants set up Azure ML workspaces, configure compute targets, and build models using drag-and-drop and code-first approaches. They explore Cognitive Services including Computer Vision, Text Analytics, Speech-to-Text, and Language Understanding (LUIS). Labs focus on multimodal integration and ethical AI deployment across domains like customer support, document automation, and accessibility. By the end, learners can build secure, modular AI workflows using Azure-native tools.
- Skills You Gain – Cognitive Service Integration & Cloud-Based Model Training
- Training Methodology – Azure Studio Labs, API Testing, Workflow Assembly
● Unit 2
Generative AI & Deep Learning with Azure OpenAI
This unit focuses on building and deploying generative models using Azure OpenAI and deep learning frameworks. Learners fine-tune LLMs for summarization, Q&A, and chatbot applications using GPT models. They also build custom neural networks with PyTorch and TensorFlow on Azure ML, exploring CNNs, RNNs, and transformers. MLOps workflows are introduced using Azure DevOps, Functions, and Logic Apps for scalable deployment. Governance, explainability, and prompt engineering are emphasized throughout. By completion, learners can deploy secure, production-grade generative AI systems.
- Skills You Gain – LLM Fine-Tuning & Scalable Generative AI Deployment
- Training Methodology – Prompt Design, Model Training, CI/CD Pipeline
● Unit 3
Real-Time Projects, Case Studies & Capstone Deployment
In this final unit, learners apply their skills to full-cycle projects across industries like healthcare, finance, and smart cities. Teams collaborate using GitHub, Azure Boards, and DevOps pipelines to scope problems, build solutions, and deploy AI products with live dashboards and performance metrics. Case studies foster critical thinking in edge deployment, latency optimization, and stakeholder readiness. The capstone project requires a fully deployable AI solution with measurable impact. Graduates leave with portfolio-ready deliverables aligned to enterprise expectations.
- Skills You Gain – End-to-End AI Product Development & Stakeholder Presentation
- Training Methodology – Agile Sprinting, Collaborative Tools, Evaluation Rubrics
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.
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Blogs and Insights
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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