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.

Schedule & Fee
Learning Objectives
Prerequisites
Target Audience
Course Modules
FAQs

May 09^th - 17^th 09:00 AM - 05:00 PM (CST) Live Online (32 Hrs.)		10% Off $1,600 $1,440 Fast Filling! Hurry Up.
May 11^th - 14^th 09:00 AM - 05:00 PM (CST) Live Online (32 Hrs.)		10% Off $1,600 $1,440
May 16^th - 24^th 09:00 AM - 05:00 PM (CST) Live Online (32 Hrs.)		10% Off $1,600 $1,440
May 18^th - 27^th 06:00 PM - 10:00 PM (CST) Live Online (32 Hrs.)		10% Off $1,600 $1,440
May 25^th - 28^th 09:00 AM - 05:00 PM (CST) Live Online (32 Hrs.) Guaranteed-to-Run		10% Off $1,600 $1,440
May 30^th - 07^th 09:00 AM - 05:00 PM (CST) Live Online (32 Hrs.)		10% Off $1,600 $1,440
June 01^st - 04^th 09:00 AM - 05:00 PM (CST) Live Online (32 Hrs.)		20% Off $1,600 $1,280
June 08^th - 11^th 09:00 AM - 05:00 PM (CST) Live Online (32 Hrs.)		20% Off $1,600 $1,280
June 08^th - 11^th 09:00 AM - 05:00 PM (CST) Live Online (32 Hrs.) Guaranteed-to-Run		20% Off $1,600 $1,280
June 13^th - 21^st 09:00 AM - 05:00 PM (CST) Live Online (32 Hrs.)		20% Off $1,600 $1,280
June 15^th - 24^th 06:00 PM - 10:00 PM (CST) Live Online (32 Hrs.)		20% Off $1,600 $1,280
June 22^nd - 25^th 09:00 AM - 05:00 PM (CST) Live Online (32 Hrs.) Guaranteed-to-Run		20% Off $1,600 $1,280
June 27^th - 05^th 09:00 AM - 05:00 PM (CST) Live Online (32 Hrs.)		20% Off $1,600 $1,280
July 06^th - 09^th 09:00 AM - 05:00 PM (CST) Live Online (32 Hrs.)		25% Off $1,600 $1,200

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

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