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    Ignio Ai.Workload Management - Intermediate

    Posted By: ELK1nG
    Ignio Ai.Workload Management - Intermediate

    Ignio Ai.Workload Management - Intermediate
    Published 5/2023
    MP4 | Video: h264, 1280x720 | 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

    Requirements

    100% completion of ignio Overview course

    100% completion of ignio AI. Workload Management - Foundation course

    Description

    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.

    Overview

    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