Future-Proof Your Mainframes With Ai/Ml

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

What you'll learn

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

Requirements

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.

Description

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.