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AI in Cloud & Edge online training course

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Advance your expertise with Multisoft Systems’ AI in Cloud & Edge Online Training in India. Learn how AI models operate on cloud platforms and edge devices, explore IoT integration, real-time decision-making, and scalable deployments. Ideal for IT professionals, developers, engineers, and innovators seeking future-ready skills in intelligent systems.

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The AI in Cloud & Edge Online Training Course by Multisoft Systems empowers learners to understand, build, and deploy intelligent solutions across cloud platforms and edge devices. This comprehensive program covers the convergence of Artificial Intelligence, Cloud Computing, and Edge Computing, enabling participants to work with scalable, real-time AI applications. Through expert-led sessions, learners explore cloud-based AI pipelines, distributed model deployment, IoT integration, microservices, and latency-optimized edge AI architectures.

The course provides practical exposure to leading platforms such as AWS, Azure, and Google Cloud, along with hands-on training in edge frameworks, containerized environments, and model optimization techniques. Participants also learn how to enable real-time analytics, automate workflows, and design high-performance AI systems for industries like manufacturing, healthcare, retail, and smart cities.

Whether you are an IT professional, developer, data engineer, or technology enthusiast, this course equips you with the job-ready expertise needed to build future-ready AI solutions across cloud and edge environments.

  • Overview of AI, Cloud Computing & Edge Computing
  • Cloud vs. Edge vs. Hybrid architectures
  • Evolution of distributed AI systems
  • Real-world use cases across industries
  • Benefits & challenges of Cloud and Edge AI

  • Cloud concepts: IaaS, PaaS, SaaS
  • Virtual machines, containers & serverless computing
  • Cloud storage, compute, networking & security essentials
  • Introduction to AWS, Azure & Google Cloud
  • Cloud-native architecture principles

  • Cloud-based AI development workflows
  • Data pipelines for model training & deployment
  • AutoML services on AWS, Azure & GCP
  • Scalable AI model hosting & APIs
  • Monitoring & governance of cloud AI systems

  • Edge computing fundamentals
  • Low-latency processing & real-time decision-making
  • Edge devices: gateways, sensors, embedded systems
  • Role of IoT in Edge AI
  • Edge communication protocols (MQTT, CoAP, 5G)

  • Edge AI development lifecycle
  • Model optimization techniques: pruning, quantization, acceleration
  • TensorFlow Lite, ONNX Runtime, Nvidia Jetson, Intel OpenVINO
  • Deploying AI models on microcontrollers & edge CPUs/GPUs
  • Managing updates & remote deployments

  • Distributed AI deployment architecture
  • Cloud to edge model synchronization
  • Secure communication between cloud & edge nodes
  • Data federation & edge-to-cloud integration
  • Designing resilient hybrid AI systems

  • Stream processing fundamentals
  • Tools: AWS Kinesis, Azure Stream Analytics, Kafka
  • Event-based automation workflows
  • Real-time anomaly detection models
  • Implementing autonomous decision systems

  • Docker fundamentals for AI deployment
  • Kubernetes & K3s for edge orchestration
  • CI/CD pipelines for AI
  • Monitoring AI models in production
  • Scaling AI across distributed environments

  • Cloud & edge security best practices
  • Data encryption, authentication & access control
  • Governance frameworks & responsible AI
  • Risk analysis & compliance guidelines

  • Cloud-based AI deployment project
  • Edge AI automation project (IoT-based)
  • Hybrid cloud-edge real-time application
  • End-to-end project implementation & review

Understand AI Deployment Across Cloud & Edge Environments
Gain a strong foundation in how AI systems operate on cloud platforms and edge devices, including architecture and data flow.

Learn Cloud-Based AI Development Workflows
Explore training, hosting, and scaling AI models using cloud services such as AWS, Azure, and Google Cloud.

Master Edge AI Concepts and Frameworks
Understand edge computing, IoT integration, latency optimization, and real-time AI inference on devices.

Build and Deploy Distributed AI Applications
Learn to design solutions that combine cloud intelligence with edge processing for enhanced performance and reliability.

Work with Containerization and Microservices
Use Docker, Kubernetes, and serverless frameworks to deploy AI models efficiently.

Implement Real-Time Data Processing & Analytics
Build pipelines for continuous data ingestion, streaming analytics, and low-latency AI decision-making.

Optimize AI Models for Edge Devices
Learn quantization, pruning, compression, and model acceleration techniques.

Develop Job-Ready Technical Skills
Gain hands-on experience through practical labs and real-world projects to prepare for roles in AI engineering, cloud computing, and IoT systems.

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This Course Includes:
  • Globally Recognized Certification
  • 12+ Hrs. of Expert-Led Training
  • Interactive Live Masterclasses
  • Practical, Hands-On Learning
  • Build Your Own AI-Powered App
  • multisoft ai tools Explore a Multiple AI Tools
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