Future-Proof Your Mainframes With Ai/Ml

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Future-Proof Your Mainframes With Ai/Ml - AD-TEAM - 01-11-2025

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Future-Proof Your Mainframes With Ai/Ml
Published 1/2025
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 1.56 GB | Duration: 5h 58m

Introduction to AI/ML for Mainframe Professionals: Basics of AI/ML and Their Relevance to Mainframe Environments

[b]What you'll learn[/b]

Foundational Understanding of AI/ML Concepts

Awareness of AI/ML's Relevance to Mainframes

Practical Knowledge of Tools and Frameworks

Data Preparation Skills

Strategic Implementation Framework

Business Communication and Stakeholder Alignment

Common Challenges and Best Practices

Foundations for Continuous Learning

[b]Requirements[/b]

This course is not for beginners. This course is for a specific audience, including IT professionals responsible for overseeing and maintaining large-scale mainframe environments in industries like finance, government, and telecommunications.

[b]Description[/b]

Future-Proof Your Mainframes with AI/MLUnlock the transformative potential of Artificial Intelligence (AI) and Machine Learning (ML) in mainframe environments with our comprehensive online course, Future-Proof Your Mainframes with AI/ML. This course is designed for IT professionals, mainframe managers, and data scientists eager to modernize enterprise systems by integrating cutting-edge AI/ML solutions.Mainframes are the backbone of critical operations in industries like banking, healthcare, and logistics. However, as organizations strive to harness the power of AI/ML, bridging the gap between legacy systems and modern technologies is crucial. This course equips you with the knowledge and practical skills to make this transition seamless.What You'll Learn:The Foundations of AI/ML in Mainframes: Understand the role of AI/ML in enhancing mainframe operations, from predictive maintenance to real-time fraud detection.Building AI/ML Models: Learn step-by-step how to design, train, and validate models tailored for mainframe data.Hybrid Cloud Integration: Explore hybrid cloud architectures to scale AI/ML solutions while maintaining the reliability of mainframes.Automation and Optimization: Discover how AI/ML automates workflows, improves efficiency, and drives innovation.Best Practices for Deployment and Maintenance: Ensure smooth implementation with robust monitoring, retraining, and continuous improvement strategies.Course Highlights:This course combines interactive video lectures, real-world case studies, and practical exercises to help you master the integration of AI/ML with mainframe systems. You'll also gain exclusive access to companion materials from our book, Future-Proof Your Mainframes with AI/ML. This book complements the course by providing in-depth explanations, additional resources, and step-by-step guides for hands-on projects.

Overview

Section 1: Introduction

Lecture 1 Introduction

Section 2: Module 1: Understanding AI/ML Fundamentals

Lecture 2 Lesson 1: What is Artificial Intelligence?

Lecture 3 Lesson 2: What is Machine Learning?

Lecture 4 Lesson 3: Types of Machine Learning: An Overview

Lecture 5 Lesson 4: The AI/ML Workflow: How It Works

Section 3: Module 2: Exploring the Role of AI/ML in Mainframes

Lecture 6 Lesson 1: AI/ML Applications in Enterprise Systems

Lecture 7 Lesson 2: AI/ML Use Cases for Mainframes

Lecture 8 Lesson 3: Business Benefits of AI/ML Integration

Section 4: Module 3: Assessing Your Mainframe Environment

Lecture 9 Lesson 1: Conducting a Mainframe System Audit

Lecture 10 Lesson 2: Defining Your Modernization Goals

Lecture 11 Lesson 3: Creating a Strategic Roadmap

Section 5: Module 4: Preparing Data for AI/ML Integration

Lecture 12 Lesson 1: Understanding Mainframe Data

Lecture 13 Lesson 2: Data Extraction and Transformation

Lecture 14 Lesson 3: Ensuring Data Security and Compliance

Lecture 15 Lesson 4: Addressing Data Quality Issues

Section 6: Module 5: Selecting Tools and Frameworks for Integration

Lecture 16 Lesson 1: Overview of AI/ML Tools for Mainframes

Lecture 17 Lesson 2: Using APIs and Middleware for Integration

Lecture 18 Lesson 3: Hybrid Cloud Options for AI/ML

Section 7: Module 6: Building and Testing AI/ML Models

Lecture 19 Lesson 1: Developing Your First AI/ML Model

Lecture 20 Lesson 2: Training and Validating the Model

Lecture 21 Lesson 3: Testing AI/ML Models in a Controlled Environment

Section 8: Module 7: Implementing AI/ML in Mainframe Operations

Lecture 22 Lesson 1: Scaling AI/ML Solutions

Lecture 23 Lesson 2: Automating Workflows with AI/ML

Lecture 24 Lesson 3: Best Practices for Deployment

Section 9: Module 8: Monitoring, Maintenance, and Continuous Improvement

Lecture 25 Lesson 1: Monitoring AI/ML-Integrated Systems

Lecture 26 Lesson 2: Retraining AI/ML Models

Lecture 27 Lesson 3: Continuous Optimization

Lecture 28 Lesson 4: Looking Ahead: The Future of Mainframes and AI/ML

Target audience would include: Mainframe Systems Managers, IT Infrastructure Directors/Leads, Cloud & DevOps Managers, Technical Support Engineers, and Developers & Programmers.

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