Ignio Ai.Workload Management – Intermediate

Published 5/2023
MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz
Language: English | Size: 3.24 GB | Duration: 7h 48m

Learn how ignio makes predictable, agile and silent batch operations

What you’ll learn
Explain the concepts of product overview
Demonstrate the user journey of all key functions of the product
Describe the concepts of the user journey
Explain and show how ignio AI. Workload Management makes the batch from reactive to proactive

100% completion of ignio Overview course
100% completion of ignio AI. Workload Management – Foundation course

This course provides an detail study on ignio AI.Workload Management. It first covers product overview, then does a deep-dive into 6 user journies. This course does a deep dive into each of these journeys explaining the core concepts through a product demo.————————————————————————————————————————————Digitate™ leverages machine learning and AI (artificial intelligence) to intelligently manage IT and business operations. Our award-winning product, ignio™, is a cognitive automation solution that helps IT rapidly identify and remediate outages in minutes. ignio’s unique pre-built knowledge allows customers to realize the value of AI in significantly less time than other solutions. It binds together disparate but interconnected business applications, processes, and the underlying infrastructure to drive smart decisions and perform actions autonomously. Our suite of product includes: – ignio™ AIOps – Map business applications and the underlying infrastructure to identify the root cause of a problem and perform autonomous remedial actions – ignio™ AI.WorkloadManagement – Minimize time needed for planning and operational activities with risk assessment, preventive recommendations & prediction of future batch job impacts with over 90% accuracy to reduce SLA violations – ignio™ AI.ERPOps – Provide functional and technical relationship mapping, rapidly identify and rectify typical issues impacting business and stay on top of compliance issues – ignio™ AI. Digital Workspace – A self-healing, end-user experience management product – ignio™ Cognitive Procurement – An AI based oversight mechanism into the Procure-to-Pay function that detects and predicts different types of procurement anomalies on time – ignio™ AI.Assurance – Enables enterprises to align quick delivery of quality software with some of its distinguished features such as autonomous test suite generation, impact analysis, and self-healing capabilities Digitate was founded in 2015 and is headquartered in Santa Clara, California, and Pune, India.

Section 1: Product Overview

Lecture 1 WLM Product Overview – Concept

Section 2: User Journey 1 – Monitor and Notify Anomaly

Lecture 2 Concept

Lecture 3 Implementation Journey

Lecture 4 Prepare and Ingest Data

Lecture 5 Data Analysis, Mine Intelligence and View Results

Lecture 6 Scheduling Analysis

Lecture 7 Configure and Validate Notifications

Lecture 8 Measuring Effectiveness and Scaling Implementation Scope

Section 3: User Journey 2 – Predictions and Look Ahead of SLAs

Lecture 9 Concept

Lecture 10 Implementation Journey

Lecture 11 Prerequisites

Lecture 12 Prepare and Ingest

Lecture 13 Run Analysis

Lecture 14 How to Configure

Lecture 15 How to view results – Conclusion

Lecture 16 Best Practices

Lecture 17 Scale

Section 4: Plan for Growth and Change

Lecture 18 Concept

Lecture 19 Overview

Lecture 20 Run Analysis

Lecture 21 How to view results

Lecture 22 Other Information

Section 5: Drive Continous Improvements

Lecture 23 Concept

Lecture 24 Overview

Lecture 25 Run and Schedule Analysis

Lecture 26 How to Configure

Lecture 27 View Results

Lecture 28 Scale Best Practices

Section 6: Diagnose and Resolve Failures

Lecture 29 Concept

Lecture 30 Overview

Lecture 31 Prerequisites

Lecture 32 Model Extensions

Lecture 33 Configure and Validaiton

Lecture 34 Best Practices & Effectiveness

Section 7: Manage Alerts

Lecture 35 Concept

Lecture 36 Overview

Lecture 37 Run Analysis

Lecture 38 Configure Policies

Lecture 39 How to view

Lecture 40 Conclusions

Batch Operators, Batch Architect


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