Cloud Solutions AI Architect- Live Training

Cloud Solutions AI Architect

(Design, Deploy & Govern Intelligent Cloud Architectures on AWS | Azure | GCP)

 

A Cloud Solutions AI Architect is a senior technology expert responsible for designing, implementing, and managing intelligent cloud-based solutions that combine the power of artificial intelligence, machine learning, big data, and modern cloud infrastructure. This role bridges business strategy with advanced technology by creating scalable, secure, and high-performing AI ecosystems that support digital transformation and enterprise innovation.

Cloud Solutions AI Architects work closely with stakeholders, software engineers, data scientists, DevOps teams, and business leaders to develop architectures that enable organizations to automate processes, improve decision-making, enhance customer experiences, and gain actionable insights from data. They ensure that AI applications are efficiently deployed in cloud environments while maintaining security, governance, compliance, reliability, and cost optimization.

These professionals use leading cloud platforms such as Amazon Web Services, Microsoft, and Google to build intelligent enterprise solutions including predictive analytics systems, AI-powered chatbots, recommendation engines, computer vision platforms, natural language processing systems, and generative AI applications.

About the Instructor:

Srikanth is a highly experienced Cloud Solutions AI Architect with 14+ years of extensive industry experience in Cloud Computing, Artificial Intelligence, Enterprise Solution Architecture, and AI-driven digital transformation. He has worked on complex, large-scale enterprise applications with a strong focus on designing scalable cloud infrastructures, intelligent automation solutions, AI integration, and cloud-native architecture optimization. He possesses strong expertise in Cloud Architecture, AI Solution Design, Generative AI, Machine Learning integration, DevOps practices, Microservices Architecture, API Integration, and AI-powered cloud workflows using modern AI technologies and enterprise cloud platforms. Srikanth specializes in applying Generative AI techniques to improve business automation, intelligent decision-making, cloud optimization, system scalability, and enterprise productivity. With deep knowledge of AI-assisted cloud engineering and enterprise architecture, he helps professionals effectively leverage Artificial Intelligence and cloud technologies for building smarter, secure, and scalable solutions. His practical understanding of real-time cloud transformation challenges enables learners to confidently adopt modern AI-driven cloud strategies and enterprise architectures. Srikanth also has 14+ years of technical training experience and has trained 700+ students over the last 5 years. His training sessions are highly interactive, practical, and industry-oriented, with a strong emphasis on hands-on exercises, architecture design, cloud implementation strategies, and real-time project scenarios. He has a natural ability to simplify complex Cloud, AI, and Solution Architecture concepts, making his sessions easy to understand for both beginners and experienced IT professionals. His passion for teaching and commitment to learner success make his training programs highly engaging, practical, and career- focused.

Live Sessions  Price:

For LIVE sessions – Offer price after discount is 300 USD 259 USD 192 USD Or 25000 INR 19900 INR 15900 Rupees

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Free Demo Session:

10th June @ 9:00 PM – 10:00 PM (IST) (Indian Timings)

10th June @ 11:30 AM – 12:30 PM (EST) (U.S Timings)

10th June  @ 4:30 PM – 5:30 PM (BST) (UK Timings)

 

Class Schedule:

For Participants in India: Monday to Friday @9:00 PM – 10:00 PM (IST)

For Participants in the US:  Monday to Friday @ 11:30 AM – 12:30 PM (EST)

For Participants in the UK:  Monday to Friday @ 4:30 PM – 5:30 PM (BST)

 

What student’s have to say about Srikanth:

I had enrolled for the Cloud Solutions AI Architect course by Srikanth of Claude AI, and it was truly a great course for anyone beginning their journey in Cloud and AI technologies. Srikanth sir is an excellent trainer who explains every concept very clearly and thoroughly. I am completely satisfied with the course. Thanks to Srikanth sir and Claude AI for such a wonderful learning experience! – Satanu Bagchi Hey Srikanth, thank you so much for all your sessions! Sorry I couldn’t attend the last few classes because my sleep schedule got disturbed and I couldn’t wake up early. Apologies for that! But I genuinely enjoyed all the other sessions. You’ve been extremely supportive and patient with all our questions and requests. Hoping to join your future courses as well. Good luck everyone! 😊 – Praneetha Mandapati It was a pleasure learning from you throughout this training session. Every topic in the syllabus was explained clearly, and all our doubts were addressed patiently. Your support and assistance are highly appreciated. Thank you once again! 🙏🏻 – Devang Hi @Srikanth Sir from Claude AI, thank you for all the valuable insights shared throughout the sessions. Every class was interactive and motivating. After returning from my second maternity break, I was feeling a bit low and doubtful about restarting my learning journey, but your sessions really helped me regain confidence and quickly refresh my concepts while learning many new things in Cloud and AI technologies too. Will definitely stay connected 😊 – Ramya Hi, the sessions were excellent. Srikanth sir has outstanding knowledge in Cloud Solutions, AI concepts, and modern architecture methodologies. All doubts and queries were explained in a very easy and understandable way. – Vivek Singh Excellent Teaching – Kasthuraiah Bellamkonda Very good sessions, and a very calm and composed trainer. – Pallavi khare The training was really good. I got to learn many new concepts related to Cloud Computing, AI Architectures, DevOps basics, deployment strategies, and real-time project scenarios. Srikanth sir’s expertise is excellent, and he makes every topic easy to understand. I would love to join more courses by him in the future. – Divya The course was quite detailed, and our trainer Srikanth was very approachable in resolving doubts and questions. I would definitely call it a “Value for Money” course. Even though I had some prior knowledge, I noticed that the course covered all the basics thoroughly for beginners as well. Overall, it was a great experience. Thanks!! – Arpit Arora Srikanth is someone who is more dedicated to your learning journey than you are yourself.. – Prashant Singh Excellent explanation of Cloud Solutions and AI Architecture topics. Very useful for beginners as well as experienced professionals who want to revise the basics. – Micheal.s This course is highly recommended for beginners who want to enter the Cloud and AI industry and build a strong foundation in Cloud Solutions AI Architecture. – Naveen The teaching methods are highly interactive, and you can learn a lot from Srikanth sir. This is one of the best courses I have come across for Cloud Solutions and AI Architecture concepts. – Sachin Srikanth has been truly amazing throughout the course. He is undoubtedly one of the best instructors who can teach Cloud Solutions and AI Architecture in such a practical and engaging way. – Sneha It is really a wonderful course that provides valuable insights into multiple core areas of Cloud Computing, AI Solutions, and modern software architecture. – Pooja

Salient Features:

• 120+ Hours of Live Cloud Solutions AI Architect Training with real-time project implementation
• Lifetime access to recorded sessions for continuous learning and revision
• Hands-on practical sessions with real-world performance testing scenarios

Who can enroll in this course?

• Cloud Engineers / Administrators

• DevOps / SRE Engineers

• Software Developers / SDETs

• Data Engineers / Data Scientists

• IT Architects (existing)

• Performance / Testing Professionals

• IT Managers / Tech Leads

• Certification Aspirants

What will I learn by the end of this course?

By the end of this Cloud Solutions AI Architect training, you will be able to:

• Understand core cloud computing concepts, cloud service models (IaaS, PaaS, SaaS), and modern enterprise cloud architecture
• Design scalable, secure, and highly available cloud solutions across AWS, Azure, or GCP environments
• Apply cloud networking fundamentals including VPC/VNet design, subnets, routing, and hybrid connectivity
• Architect end-to-end enterprise systems using microservices, serverless, and container-based architectures
• Build strong foundations in AI/ML concepts and understand how they integrate into cloud platforms
• Design and deploy AI-powered cloud solutions using managed AI services (e.g., model hosting, inference, and pipelines)
• Work with cloud-native services such as compute, storage, databases, and event-driven architectures
• Implement Infrastructure as Code (IaC) using tools like Terraform or cloud-native templates (ARM/CloudFormation)
• Apply DevOps practices including CI/CD pipelines for automated deployment and scaling
• Design secure cloud architectures using IAM, encryption, secrets management, and compliance best practices
• Handle observability using logging, monitoring, tracing, and performance optimization tools
• Build data pipelines for analytics and AI workloads using streaming and batch processing services
• Implement API-driven architectures, integration patterns, and message queues for distributed systems
• Apply cost optimization strategies and cloud governance best practices
• Work with container orchestration platforms like Kubernetes for scalable deployments
• Understand MLOps concepts including model lifecycle management, deployment, and monitoring
• Design fault-tolerant systems with disaster recovery and high availability strategies
• Use AI-assisted and agentic AI tools to automate cloud operations and architecture decisions
• Work on a real-world end-to-end Cloud + AI architecture project
• Prepare for Cloud Architect certifications (AWS Solutions Architect / Azure Architect / Google Cloud Architect)

 

Course syllabus:

Module 1: Cloud Fundamentals & Architecture Principles (8 Hours)

• Cloud computing models: IaaS, PaaS, SaaS, FaaS — definitions and use cases
• Public, private, hybrid, and multi-cloud deployment models
• AWS Well-Architected Framework: Operational Excellence, Security, Reliability, Performance, Cost Optimization, Sustainability
• Azure Architecture Framework & Google Cloud Architecture Framework
• Core networking: VPC, subnets, routing, peering, load balancers, CDN
• Compute options: VMs, containers (Docker/Kubernetes), serverless, spot/preemptible instances
• Storage types: object, block, file storage — S3, EBS, Azure Blob, GCS
• Identity & Access Management (IAM): users, roles, policies, cross-account access
• High Availability patterns: multi-AZ, multi-region, active-active vs. active-passive
• Introduction to cloud architect role, responsibilities, and solution design process

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Module 2: Multi-Cloud Deep Dive — AWS, Azure & GCP (12 Hours)

• AWS core services: EC2, S3, RDS, Lambda, EKS, VPC, Route 53, CloudFront, SNS/SQS
• Azure core services: Virtual Machines, Blob Storage, AKS, Azure Functions, Azure SQL, Azure AD, Key Vault
• GCP core services: Compute Engine, Cloud Run, GKE, BigQuery, Cloud Storage, Pub/Sub, Cloud Armor
• Service mapping and equivalence across AWS, Azure, and GCP
• Cloud migration strategies: Rehost, Replatform, Refactor, Repurchase, Retain, Retire (6 Rs)
• Hybrid cloud architectures: AWS Outposts, Azure Arc, Anthos (GCP)
• Inter-cloud connectivity: Direct Connect, ExpressRoute, Cloud Interconnect
• Multi-cloud governance using tools: Terraform, Pulumi, Crossplane
• Disaster recovery architectures: RTO/RPO planning, pilot light, warm standby, multi-site active-active

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Module 3: AI & ML Foundations for Cloud Architects (8 Hours)

• AI, Machine Learning, Deep Learning, and Generative AI — definitions and scope
• ML lifecycle: data collection, preprocessing, feature engineering, training, evaluation, deployment
• Supervised, unsupervised, semi-supervised, and reinforcement learning
• Neural networks fundamentals: perceptrons, activation functions, backpropagation, CNNs, RNNs, Transformers
• Model evaluation metrics: accuracy, precision, recall, F1, AUC-ROC, RMSE
• Common AI use cases: computer vision, NLP, anomaly detection, recommendation systems, forecasting
• AI/ML hardware: CPUs, GPUs, TPUs — choosing the right compute for ML workloads
• Responsible AI: fairness, bias, explainability, regulatory considerations (EU AI Act, NIST AI RMF)
• Mapping business problems to AI solution patterns

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Module 4: Cloud-Native AI/ML Services & Pipelines (12 Hours)

• AWS SageMaker: Studio, Data Wrangler, Feature Store, Training Jobs, Endpoints, Pipelines
• Azure Machine Learning Studio: datasets, compute clusters, pipelines, model registry, endpoints
• Google Vertex AI: Workbench, Pipelines, Feature Store, Model Registry, Vertex AI Prediction
• AutoML services: AWS Autopilot, Azure AutoML, Google AutoML
• Managed notebook environments: JupyterLab on cloud, Sagemaker Notebooks, Azure Notebooks
• Cloud data labeling services: AWS Ground Truth, Azure ML Data Labeling, Vertex AI Data Labeling
• Batch vs. real-time inference architectures
• Model serving patterns: REST APIs, gRPC, batch scoring, streaming inference
• Integration of AI pipelines with data lakes (S3, ADLS, GCS) and data warehouses
• Hands-On Lab: Build an end-to-end ML pipeline on SageMaker and Azure ML for a classification problem

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Module 5: Generative AI & LLM Architecture on Cloud (12 Hours)

• Generative AI landscape: GPT-4, Claude, Gemini, Llama, Mistral — capabilities and limitations
• Transformer architecture deep dive: attention mechanisms, tokenization, embeddings
• Prompt engineering: zero-shot, few-shot, chain-of-thought, system prompts, prompt templates
• Retrieval-Augmented Generation (RAG): architecture, chunking, vector stores (Pinecone, Weaviate, pgvector), hybrid search
• Fine-tuning and PEFT: LoRA, QLoRA, instruction tuning, domain adaptation
• AWS Bedrock: foundation models (Claude, Titan, Jurassic, Stable Diffusion), Agents, Knowledge Bases
• Azure OpenAI Service: GPT-4o, embedding models, assistants API, Azure AI Foundry
• Google Vertex AI Gemini: Gemini 1.5 Pro, Grounding, Extensions, Agent Builder
• LangChain / LlamaIndex for agentic AI application architecture
• AI Agents and multi-agent orchestration frameworks on cloud
• GenAI application patterns: chatbots, copilots, document intelligence, code generation
• Security and guardrails for GenAI: content filtering, PII redaction, prompt injection defenses
• Hands-On Lab: Build a RAG-based intelligent document Q&A system on AWS Bedrock / Azure OpenAI

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Module 6: MLOps & AI Pipeline Automation (10 Hours)

• MLOps principles: automating the ML lifecycle end-to-end
• Version control for ML: DVC, Git-LFS, experiment tracking with MLflow and W&B
• CI/CD for ML: building model training pipelines with GitHub Actions, Azure DevOps, Cloud Build
• Containerizing ML: Docker for model packaging, Kubernetes for scalable inference
• Model registry and model governance: SageMaker Registry, Azure ML Registry, Vertex AI Model Registry
• Feature stores: Feast, SageMaker Feature Store, Vertex AI Feature Store — managing reusable features
• Model monitoring: data drift detection (Evidently AI, WhyLabs), model performance degradation alerts
• Automated retraining pipelines: event-triggered and scheduled model updates
• Kubeflow Pipelines for portable ML workflow orchestration
• A/B testing and canary deployments for ML models
• Compliance and auditability in MLOps: lineage tracking, model cards

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Module 7: Serverless, Microservices & Event-Driven Architecture (10 Hours)

• Serverless architecture: AWS Lambda, Azure Functions, Google Cloud Run — design patterns and limits
• Microservices architecture: service decomposition, API gateway patterns, service mesh (Istio, App Mesh)
• Event-driven architecture: Apache Kafka, AWS Kinesis, Azure Event Hubs, Google Pub/Sub
• Kubernetes deep dive: deployments, services, ingress, horizontal pod autoscaler, GPU node pools for AI
• Container orchestration for AI workloads: running ML training and inference on EKS/AKS/GKE
• API design and management: REST, GraphQL, gRPC — AWS API Gateway, Azure API Management, Apigee
• Choreography vs. orchestration patterns in event-driven AI pipelines
• Saga pattern for distributed transactions in microservices
• Hands-On Lab: Deploy a GenAI microservice on Kubernetes with auto-scaling and API gateway integration

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Module 8: Cloud Security, Governance & Compliance (10 Hours)

• Zero Trust security model: never trust, always verify — implementation on cloud
• IAM advanced: attribute-based access control (ABAC), AWS Organizations SCPs, Azure PIM, Google Cloud IAP
• Data security: encryption at rest and in transit, key management (AWS KMS, Azure Key Vault, Cloud KMS)
• Network security: WAF, DDoS protection, private endpoints, VPC security groups and NACLs
• Cloud Security Posture Management (CSPM): AWS Security Hub, Microsoft Defender for Cloud, Google SCC
• Compliance frameworks: GDPR, HIPAA, SOC 2, ISO 27001, PCI-DSS on cloud
• AI-specific governance: data privacy for ML training data, model explainability requirements, EU AI Act overview
• Incident response and forensics on cloud: CloudTrail, Azure Monitor, GCP Audit Logs
• Secrets management: HashiCorp Vault, AWS Secrets Manager, Azure Key Vault integration patterns
• Shift-left security: DevSecOps — integrating security scanning into ML and cloud CI/CD pipelines

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Module 9: Data Architecture for AI Workloads (10 Hours)

• Modern data architecture patterns: data lakes, data warehouses, data lakehouses (Delta Lake, Apache Iceberg)
• AWS data stack: S3, Glue, Athena, Redshift, Kinesis, Lake Formation
• Azure data stack: ADLS Gen2, Azure Data Factory, Synapse Analytics, Event Hubs, Purview
• GCP data stack: BigQuery, Dataflow, Pub/Sub, Dataplex, Looker
• Data ingestion patterns: batch, micro-batch, streaming — Lambda and Kappa architectures
• Vector databases for AI: Pinecone, Weaviate, Qdrant, pgvector, Chroma — use cases and architecture
• Feature engineering at scale: Apache Spark, Dask, cloud-native distributed compute
• Data mesh architecture: domain-oriented data ownership, data-as-a-product, federated governance
• Real-time ML feature serving: low-latency feature pipelines for online prediction
• Data governance and cataloging: data lineage, metadata management, data quality for AI

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Module 10: Observability, Resilience & Performance Engineering (8 Hours)

• The three pillars of observability: Metrics, Logs, Traces — tools and best practices
• AWS observability: CloudWatch, X-Ray, OpenSearch Service, AWS Managed Grafana
• Azure observability: Azure Monitor, Log Analytics, Application Insights, Azure Managed Grafana
• GCP observability: Cloud Monitoring, Cloud Logging, Cloud Trace, Cloud Profiler
• Distributed tracing for AI/ML microservices: OpenTelemetry integration
• AI model observability: tracking inference latency, throughput, error rates, model drift in production
• Chaos engineering: AWS Fault Injection Simulator, Azure Chaos Studio, Chaos Monkey principles
• SLA, SLO, SLI definition and error budget management
• Performance testing cloud architectures: integration with JMeter, Gatling, Locust (leveraging Isha’s testing expertise)
• Capacity planning and auto-scaling for AI inference workloads

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Module 11: FinOps & Cloud Cost Optimization (6 Hours)

• FinOps principles: visibility, optimization, and operational efficiency
• Cloud pricing models: on-demand, reserved, savings plans, spot/preemptible, committed use
• Cost allocation and tagging strategies across AWS, Azure, and GCP
• Cost monitoring tools: AWS Cost Explorer, Azure Cost Management, GCP Cloud Billing
• Right-sizing compute and storage for ML training and inference
• GPU/TPU cost optimization: spot instances for training, efficient batching, model quantization
• Serverless cost optimization: function memory tuning, concurrency management
• Automated budget alerting and cost anomaly detection
• FinOps for AI workloads: balancing performance and cost for large-scale ML
• Chargebacks and showbacks for multi-team cloud usage

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Module 12: Real-Time Capstone Architecture Project (14 Hours)

The capstone project is the centerpiece of the course — participants design, build, present, and defend a complete Cloud AI solution from a real-world business scenario, simulating a professional architect engagement.

 

Sample Project Scenarios:

• Intelligent Customer Support Platform: Design a GenAI-powered support system using RAG, deployed on AWS Bedrock + EKS with full observability and security controls.
• End-to-End Fraud Detection Pipeline: Build a real-time ML fraud detection system on Azure ML + Event Hubs + Synapse Analytics with MLOps CI/CD.
• Enterprise AI Document Intelligence: Design a document processing pipeline using Google Vertex AI, Cloud Run, BigQuery, and a RAG layer for enterprise search.
• Multi-Cloud AI-Driven E-Commerce Recommendation Engine: Architect a recommendation system spanning AWS (SageMaker) and Azure (Cosmos DB) with a unified governance layer.

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Frequently Asked Questions (FAQs):