Future-Proof Your Mainframes with AI/ML

Name: Future-Proof Your Mainframes with AI/ML
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Introduction to AI/ML for Mainframe Professionals: Basics of AI/ML and Their Relevance to Mainframe Environments

Created byRicardo Nuqui

Last updated 1/2025

English

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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

Course content

9 sections • 28 lectures • 5h 58m total length

Introduction11:52
This is the Introduction to Future-Proof Your Mainframes with AI/ML. Here, we will present: the primary audience for the course; the overall course goal; the skills, knowledge, and abilities that students will acquire; the stages to complete in order to achieve successful mainframe containerization; and 8 modules comprising the course.

A PDF copy of the companion book of the same title is available for download from the Resources area of this lesson.

Lesson 1: What is Artificial Intelligence?10:56
Definition and key characteristics of AI.
Real-world examples of AI in action.
Lesson 2: What is Machine Learning?11:26
Definition of ML and how it differs from AI.
Overview of algorithms and models used in ML.
Lesson 3: Types of Machine Learning: An Overview10:40
Explanation of supervised, unsupervised, and reinforcement learning.
Use cases for each type in enterprise settings.
Lesson 4: The AI/ML Workflow: How It Works13:01
Steps involved in training and deploying an ML model.
Common challenges and how to address them.

Lesson 1: AI/ML Applications in Enterprise Systems14:41
How AI/ML drives efficiency and innovation in enterprises.
Examples of industries benefiting from AI/ML on mainframes.
Lesson 2: AI/ML Use Cases for Mainframes11:55
Predictive maintenance and anomaly detection.
Workload optimization and resource allocation.
Enhancing security and fraud detection.
Lesson 3: Business Benefits of AI/ML Integration12:52
Cost savings, improved reliability, and competitive advantage.
Case studies showcasing successful implementations.

Lesson 1: Conducting a Mainframe System Audit13:30
Tools and techniques for assessing system performance.
Identifying bottlenecks and opportunities for improvement.
Lesson 2: Defining Your Modernization Goals12:26
Aligning AI/ML objectives with business needs.
Prioritizing projects based on impact and feasibility.
Lesson 3: Creating a Strategic Roadmap12:41
Breaking down the modernization journey into manageable phases.
Setting measurable milestones and timelines.

Lesson 1: Understanding Mainframe Data14:07
Types of data stored on mainframes and their characteristics.
Why data preparation is critical for AI/ML success.
Lesson 2: Data Extraction and Transformation12:29
ETL (Extract, Transform, Load) processes for mainframe data.
Tools and techniques for data transformation.
Lesson 3: Ensuring Data Security and Compliance13:03
Best practices for protecting sensitive data.
Compliance considerations for regulated industries.
Lesson 4: Addressing Data Quality Issues13:05
Identifying and fixing data inconsistencies and gaps.
Strategies for ongoing data maintenance.

Lesson 1: Overview of AI/ML Tools for Mainframes13:05
Introduction to IBM Watson, TensorFlow, SAS Viya, and others.
Comparing features and compatibility with mainframe environments.
Lesson 2: Using APIs and Middleware for Integration12:30
How APIs and middleware facilitate communication between mainframes and AI/ML tools.
Best practices for choosing and implementing middleware.
Lesson 3: Hybrid Cloud Options for AI/ML12:48
Benefits of hybrid architectures for scalability and performance.
Examples of cloud platforms that support AI/ML for mainframes.

Lesson 1: Developing Your First AI/ML Model12:39
Step-by-step guide to creating a basic machine learning model.
Tools and libraries for model development.
Lesson 2: Training and Validating the Model12:23
Techniques for training models with mainframe data.
Methods for validating accuracy and performance.
Lesson 3: Testing AI/ML Models in a Controlled Environment13:45
Setting up a test environment for safe experimentation.
Evaluating model performance and identifying areas for improvement.

Lesson 1: Scaling AI/ML Solutions14:12
Strategies for transitioning from pilot projects to full-scale implementations.
Managing resource allocation and system stability.
Lesson 2: Automating Workflows with AI/ML13:16
Examples of automated processes enabled by AI/ML.
Tools for integrating automation into mainframe operations.
Lesson 3: Best Practices for Deployment12:33
Minimizing downtime and disruption during implementation.
Ensuring continuous monitoring and system stability.

Lesson 1: Monitoring AI/ML-Integrated Systems13:43
Tools and techniques for tracking performance and identifying issues.
Setting up alerts and dashboards for real-time monitoring.
Lesson 2: Retraining AI/ML Models13:08
When and how to retrain models with new data.
Avoiding model drift and maintaining accuracy.
Lesson 3: Continuous Optimization12:57
Identifying opportunities for further improvement.
Staying updated on advancements in AI/ML and mainframe technologies.
Lesson 4: Looking Ahead: The Future of Mainframes and AI/ML13:13
Emerging trends and technologies to watch.
Preparing for the next stage of innovation.

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/ML

Unlock 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.

Who this course is for:

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

Future-Proof Your Mainframes with AI/ML

What you'll learn

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Course content

Introduction1 lecture • 12min

Module 1: Understanding AI/ML Fundamentals4 lectures • 46min

Module 2: Exploring the Role of AI/ML in Mainframes3 lectures • 39min

Module 3: Assessing Your Mainframe Environment3 lectures • 39min

Module 4: Preparing Data for AI/ML Integration4 lectures • 53min

Module 5: Selecting Tools and Frameworks for Integration3 lectures • 38min

Module 6: Building and Testing AI/ML Models3 lectures • 39min

Module 7: Implementing AI/ML in Mainframe Operations3 lectures • 40min

Module 8: Monitoring, Maintenance, and Continuous Improvement4 lectures • 53min

Requirements

Description

Who this course is for: