+91-9871262198
info@cloud360.co
Our cloud training videos have over 100K views on
Cloud360-logo

ARCH-492: Architecting Cloudera Edge to AI

ARCH-492: Architecting Cloudera Edge to AI equips professionals with the knowledge to design and implement scalable AI and machine learning solutions across edge devices and cloud environments using Cloudera technologies. This course covers end-to-end architecture, from edge data collection and processing to advanced AI model deployment and real-time analytics. You will learn how to integrate various Cloudera components such as Cloudera DataFlow (CDF), Cloudera Data Science Workbench (CDSW), and Cloudera Machine Learning (CML) into a unified architecture that optimizes data-driven decision-making and AI model performance, ensuring seamless scalability, security, and governance.

Register Your Interest

450K+

Career Transformation

250+

Workshop Every Month

100+

Countries and Counting

Schedule Learners Course Fee Register Your Interest
April 28th - 01st
09:00 - 17:00 (CST)
Live Virtual Classroom
USD 1,600
Fast Filling! Hurry Up.
April 21st - 24th
09:00 - 17:00 (CST)
Live Virtual Classroom
USD 1,600
May 12th - 21st
09:00 - 13:00 (CST)
Live Virtual Classroom
USD 1,600
June 02nd - 05th
09:00 - 17:00 (CST)
Live Virtual Classroom
USD 1,600

Course Prerequisites

  • Familiarity with machine learning and AI concepts
  • Basic knowledge of Cloudera components (Cloudera DataFlow, Cloudera Machine Learning, etc.)
  • Experience with cloud platforms (AWS, Azure, GCP)
  • Understanding of distributed computing and big data architectures
  • Proficiency in Python, Java, or similar programming languages

Prior experience with Cloudera or edge-to-cloud architectures is beneficial but not required.

Learning Objectives

By the end of this course, participants will be able to:

  • Design and implement scalable Cloudera Edge to AI architectures for end-to-end data processing
  • Integrate edge devices with cloud infrastructure for real-time data management
  • Develop and deploy machine learning models using Cloudera’s platform
  • Ensure the scalability, security, and governance of AI solutions across edge and cloud environments
  • Troubleshoot and optimize performance across the entire AI pipeline from edge to cloud

Target Audience

This course is intended for professionals working on AI and machine learning solutions, especially those focused on deploying and managing AI models across edge and cloud environments. The target audience includes:

  • Data Architects
  • Data Engineers
  • Machine Learning Engineers
  • AI Solution Architects
  • Cloud Engineers
  • Big Data Developers
  • IoT and Edge Computing Engineers

Course Modules

  • Introduction to Edge to AI Architecture

    • Overview of the Edge to AI concept and its importance in modern AI solutions
    • Key components of the Cloudera platform for AI workloads
    • Benefits of an end-to-end AI architecture from edge devices to cloud

  • Setting Up the Cloudera Environment for Edge to AI

    • Configuring Cloudera components: Cloudera DataFlow, Cloudera Machine Learning, and Cloudera Data Science Workbench
    • Setting up Cloudera Edge Management for data ingestion from edge devices
    • Integration with cloud platforms and edge devices for real-time data processing

  • Data Ingestion and Management at the Edge

    • Strategies for collecting and managing data from IoT and edge devices
    • Real-time data streaming and batch processing
    • Using Cloudera DataFlow (CDF) for efficient data ingestion and transformation

  • Building Scalable AI Pipelines

    • Designing scalable data pipelines that process large-scale datasets
    • Utilizing Cloudera’s tools for ETL (Extract, Transform, Load) processes in edge-to-cloud environments
    • Real-time data synchronization between edge and cloud environments

  • Machine Learning Model Development with Cloudera

    • Introduction to Cloudera Machine Learning (CML) and Cloudera Data Science Workbench (CDSW)
    • Building, training, and validating AI models using CML/CDSW
    • Leveraging MLlib for model optimization and performance tuning

  • AI Model Deployment and Integration

    • Deploying trained models to edge devices and cloud environments
    • Managing model lifecycle: versioning, updating, and rollback
    • Integrating deployed models into production systems for real-time decision-making

  • Scaling AI Workloads and Ensuring Security

    • Ensuring scalability of AI solutions across edge and cloud infrastructures
    • Best practices for managing distributed computing resources for AI
    • Implementing data security, privacy, and governance frameworks across the architecture

  • Monitoring, Troubleshooting, and Optimizing Edge to AI Solutions

    • Monitoring edge and cloud systems for performance, latency, and security
    • Troubleshooting common issues in edge-to-cloud AI architectures
    • Performance tuning and optimizing AI models and data pipelines for faster decision-making

What Our Learners Are Saying

The training, courseware, and lab experience were insightful and valuable. Keep up the great work and learning experience!

Nitish A. Anand – Accenture

Course: SC-200: Microsoft Security Operations Analyst
Date: 15th Jan 2025

The instructor was professional and very content.

Justine Daudi Mlimbilah – Bank of Africa, Tanzania

Course: MD-102: Microsoft 365 Endpoint Administrator
Date: 20th Dec 2024

The instructor was so knowledgeable & humble. Rare to find someone so confident but so down to earth these days. So appreciative to him.”

Mohd. Hassan – Ministry of Finance, UAE

Course: AZ-700: Designing and Implementing Microsoft Azure Networking Solutions
Date: 31st July 2024

Instructor is experienced and knowledgeable in guiding.

Dharshini Mahalaxmi – Dr. MGR Education and Research Institute, Chennai, India

Course: SC-300: Microsoft Identity and Access Administrator
Date: 4th May 2024