Cloud Native AI Course Overview

Cloud Native AI Course Overview

Discover the essential Cloud Native AI techniques with Koenig Solutions' 2-day course. Dive into the dynamic ecosystems of Azure, AWS, Google Cloud, and NVIDIA AI. Gain practical skills in model building, deployment, and scaling with top cloud platforms. Learn to implement AI governance, security strategies, and optimize performance for cloud and edge AI applications. This course covers Azure’s AI/ML services and OpenAI, AWS’s SageMaker and serverless AI, Google Cloud’s Vertex AI, and NVIDIA’s AI tools for enhanced model acceleration and edge deployments**. Ideal for professionals aiming to leverage generative AI in real-world scenarios efficiently.

Purchase This Course

USD

850

View Fees Breakdown

Course Fee 850
Total Fees
850 (USD)
  • Live Training (Duration : 16 Hours)
  • Per Participant
  • Guaranteed-to-Run (GTR)
  • Classroom Training fee on request
  • date-img
  • date-img

♱ Excluding VAT/GST

You can request classroom training in any city on any date by Requesting More Information

  • Live Training (Duration : 16 Hours)
  • Per Participant
  • Classroom Training fee on request

♱ Excluding VAT/GST

You can request classroom training in any city on any date by Requesting More Information

Request More Information

Email:  WhatsApp:

Koenig's Unique Offerings

Course Prerequisites

Prerequisites for Cloud Native AI Course


To ensure you get the most out of our Cloud Native AI course, we recommend having a basic understanding and knowledge in the following areas:


  • Basic Knowledge of Cloud Platforms: Familiarity with one or more cloud platforms such as Azure, AWS, Google Cloud, or NVIDIA.
  • Fundamental Understanding of AI/ML Concepts: Basic understanding of Artificial Intelligence and Machine Learning terminologies and concepts.
  • Programming Skills: Some experience with programming languages, particularly Python, as it is widely used in AI/ML development.
  • Basic Understanding of DevOps: Familiarity with Continuous Integration/Continuous Deployment (CI/CD) pipelines and version control systems like Git.
  • Data Management Basics: Basic knowledge of data privacy, security, and compliance considerations in relation to AI workflows.

These prerequisites will help you follow along more effectively and gain the maximum benefit from the hands-on and theoretical components of the course.


Target Audience for Cloud Native AI

Cloud Native AI is a comprehensive 2-day course covering AI/ML ecosystems across Azure, AWS, Google Cloud, and NVIDIA, designed for tech professionals keen on mastering cloud-native AI solutions.


Job Roles and Target Audience:


  • Data Scientists
  • Machine Learning Engineers
  • AI Specialists
  • Cloud Engineers
  • DevOps Engineers
  • IT Managers
  • System Architects
  • AI/ML Enthusiasts
  • Software Developers
  • IT Consultants
  • Solutions Architects
  • AI/ML Solution Developers
  • Cloud Solution Architects
  • IT Security Professionals
  • R&D Engineers
  • IT Compliance Officers
  • Technical Project Managers
  • Data Engineers


Learning Objectives - What you will Learn in this Cloud Native AI?

Introduction:

The Cloud Native AI course at Koenig Solutions provides an in-depth understanding of the AI/ML ecosystems across Azure, AWS, Google Cloud, and NVIDIA. This course includes practical applications and best practices for deploying and managing AI models in various cloud environments.

Learning Objectives and Outcomes:

  • Introduction to Azure AI and Machine Learning

    • Gain an overview of Azure AI/ML services (Azure Machine Learning, Cognitive Services, Bot Service)
    • Understand use cases and applications of generative AI on Azure
  • Azure OpenAI Service

    • Learn to utilize Azure OpenAI for building generative models and applications
    • Explore key features and integration options with Azure AI services
  • Integration and Deployment on Azure

    • Build CI/CD pipelines for AI models with Azure DevOps
    • Develop strategies for monitoring, scaling, and maintaining models in production
  • AI Governance and Security on Azure

    • Implement responsible AI practices with Azure tools
    • Understand data privacy, governance, and compliance strategies
  • Introduction to AWS AI/ML Services

    • Get an overview of AWS AI/ML offerings (Amazon SageMaker, Comprehend, Rekognition, Polly, Translate)
    • Study real-world use cases of generative

Suggested Courses

USD