Course Program Overview
Multi-Cloud AI & Cross-Platform LLMs: AWS, Azure, and GCP Integration
- Duration: 6 Months
- Format: Live Online / Classroom / Blended / Corporate
- Sessions: 5 per week
- Session Length: 1 Hour each
- Tech Stack: AWS SageMaker, Azure AI Studio, Google Vertex AI, Python, VS Code, Jupyter, TensorFlow/PyTorch, LangChain, LlamaIndex, HuggingFace Transformers, Kubernetes, Docker, CI/CD Pipelines, Multi-Cloud Orchestration Tools.
- Outcome: Enterprise-grade Multi-Cloud AI Portfolio + Certification + Career Acceleration in Cloud-Native AI & LLM Deployment.
Become a AI & cloud Expert
Our placement Record
Future-Ready AI with Multi-Cloud & LLMs
As enterprises globally shift away from single-vendor dependence to avoid "cloud lock-in," the market demand for architects who can bridge the gap between AI research and multi-cloud infrastructure has reached an all-time high. This intensive 6-month program transforms you into a high-demand specialist capable of orchestrating intelligence across AWS, Azure, and GCP simultaneously—a skill set that currently commands luxury salary packages exceeding ₹85L in India or $350K+ globally. By mastering the "Cloud-Agnostic" stack, including AWS Bedrock, Azure OpenAI, and GCP Vertex AI, you gain the unique ability to design fail-safe, cost-optimized AI environments that top-tier companies like Google, NVIDIA, and Microsoft desperately require to scale. This course doesn't just teach you to build AI; it equips you with the FinOps and Hybrid-Cloud expertise to manage it at an enterprise level, positioning you for elite roles such as AI Solutions Architect or Principal Infrastructure Engineer with a competitive edge that standard AI courses cannot provide.
📌 Quick Highlight for Students:
Market Dominance
Multi-cloud adoption is expected to hit 85% in large enterprises by 2026.
Placement Edge
Direct access to "Infrastructure-level" roles that are often immune to general market fluctuations.
Portfolio
A capstone project demonstrating a live, cross-cloud gateway—the ultimate proof of authority for high-stakes interviews.
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
Over the 10-month journey, you'll master essential skills in machine learning, deep learning, and data science using Python, TensorFlow, PyTorch, and real-time frameworks like .You’ll complete industry-relevant projects such as predictive modeling, intelligent automation, and cloud-based AI deployment with real-time dashboards.
30+ Cloud Tools
30+ Cloud Tools
25+ Hybrid Projects
25+ Hybrid Projects
Serverless LLMs
Serverless LLMs
Vector Intelligence
Vector Intelligence
Hardware Tuning
Hardware Tuning
AI FinOps
AI FinOps
Local-First Dev
Local-First Dev
Cloud Failover
Cloud Failover
Course Program
(6 units)
Classroom/Live online | 06 months (140+ Hours)
This program is the bridge between AI engineering and enterprise-grade infrastructure. As organizations move away from cloud lock-in, the demand for architects who can orchestrate AI across AWS, Azure, and GCP has skyrocketed. This course moves beyond basic model building to teach you how to design high-availability “Hybrid Intelligence” systems. You will learn to navigate the specific AI ecosystems of the Big Three, manage data sovereignty in hybrid environments, and optimize the massive costs associated with global AI scaling.
● Unit 1
AWS: Industrial AI with SageMaker, Bedrock & Custom Silicon
Deep dive into the Amazon Web Services ecosystem, the world's most mature platform for industrial-scale AI. This unit focuses on the full-stack orchestration of Amazon SageMaker, moving from data labeling with Ground Truth to automated model tuning (AutoML) and production-grade hosting. You will gain hands-on experience with AWS Bedrock for serverless foundation model integration, mastering the use of "Provisioned Throughput" for enterprise stability. A key focus is placed on hardware acceleration: you will learn to optimize inference and training pipelines specifically for AWS Trainium and Inferentia2 instances to achieve maximum FLOPS at the lowest possible cost.
- Training Methodics & Tasks: 12+ SageMaker Studio Labs, 5 Bedrock API Integration Workshops, 3 Hardware Optimization Case Studies.
- Skills You Get: SageMaker Pipelines, AWS Bedrock Orchestration, S3-based Feature Stores, Custom Silicon (Trainium/Inferentia) Tuning, IAM for AI Security.
● Unit 2
Azure: Enterprise AI, Cognitive Services & OpenAI Integration
This unit explores the robust, security-first ecosystem of Microsoft Azure AI. You will master the orchestration of the Azure OpenAI Service, learning how to manage "Token Quotas," implement Content Filtering, and utilize Private Endpoints to ensure data never traverses the public internet. We dive deep into Azure Machine Learning (AML) Studio, where you will build "Prompt Flows" for iterative LLM testing and utilize Azure AI Search (formerly Cognitive Search) to build sophisticated Vector Indexes. You will also learn to integrate Semantic Rankers and manage multi-index retrieval for complex enterprise datasets.
- Training Methodics & Tasks: 10+ Azure ML Studio Labs, 4 OpenAI Prompt Flow Experiments, 1 Enterprise RAG Architecture Build.
- Skills You Get: Azure OpenAI Orchestration, Vector Indexing, Semantic Search, Managed Online Endpoints, VNet Security Integration.
● Unit 3
GCP: Data-Centric AI with Vertex AI, TPUs & BigQuery ML
Leverage the platform built by the originators of modern Generative AI. This unit focuses on Google Cloud Vertex AI as a unified platform for training and deploying multi-modal systems. You will learn to execute "Zero-ETL" machine learning using BigQuery ML, running models directly on petabyte-scale data warehouses. We provide deep-dive sessions on Google’s proprietary hardware—TPU v5p (Tensor Processing Units)—teaching you how to optimize training performance for massive Large Language Models. Additionally, you will master the Gemini 1.5 Pro API, utilizing its massive context window for long-document analysis and multi-modal reasoning (Text/Image/Video).
- Training Methodics & Tasks: 12+ Vertex AI Workbench Sessions, 5 BigQuery ML SQL Workshops, 1 TPU Performance Benchmarking Lab.
- Skills You Get: Vertex AI Model Garden, BigQuery ML (BQML), TPU Orchestration, Gemini API Tuning, Feature Store Management.
● Unit 4
Hybrid-Cloud Orchestration & Local-First Intelligence
Learn to build professional AI systems without the heavy cloud tax. This unit focuses on Hybrid Architecture, where we use K3s (Lightweight Kubernetes) to manage AI workloads locally before syncing with the cloud. You will master Azure Arc and Google Anthos (Free Tier) to manage "Edge" nodes. We focus heavily on Local LLM Serving using Ollama and LocalAI, teaching you how to run high-performance models (like Llama 3 or Mistral) on local hardware. This ensures students can experiment, prompt-engineer, and test RAG logic for $0 in cloud costs.
- Training Methodics & Tasks: 8+ Local-to-Cloud Sync Labs, 4 Lightweight Kubernetes (K3s) Tasks, 1 "Zero-Cost" Local LLM Server Build.
- Skills You Get: Hybrid-Cloud Synchronization, Local LLM Orchestration, Data Sovereignty, K3s Management, Edge AI Optimization.
● Unit 6
Multi-Cloud AI Capstone: Architecting Scalable, Low-Cost Intelligence
This final unit is a high-intensity, project-based module where you apply the entire 5-unit curriculum to solve a real-world enterprise problem. You will architect a "Fail-Safe Multi-Cloud System" that demonstrates true vendor independence. The project requires building a unified AI application that uses AWS Bedrock for logic, Azure AI Search for vector retrieval, and GCP Vertex AI for multi-modal analysis—all orchestrated via a centralized serverless gateway. A primary focus of the capstone is Economic Engineering: you must demonstrate how to use Quantized Models and Spot Instances to maintain 99.9% uptime while keeping cloud overhead to a absolute minimum.
- Training Methodics & Tasks: 1 Major End-to-End Capstone Project, 3 Milestone Peer Reviews, 1 Portfolio Showcase Presentation.
- Skills You Get: Cross-Cloud API Orchestration, Advanced RAG Debugging, Enterprise FinOps Reporting, Multi-Modal Fusion, Production-Grade Portfolio Build.
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.
Our Clients
- Client Testimonials
What Our Clients Say
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