Course Number: DATA-104

Duration: 3 days (19.5 hours)

Format: Live, hands-on

AIOps Training Overview

This in-person or online AIOps (Artificial Intelligence Operations) training class teaches attendees how to successfully deploy AI and Data Science systems at scale. Students learn how to break down a system or pipeline into functional components, scale different types of processes, and adjust for various types of Big Data requirements.

Location and Pricing

Accelebrate offers instructor-led enterprise training for groups of 3 or more online or at your site. Most Accelebrate classes can be flexibly scheduled for your group, including delivery in half-day segments across a week or set of weeks. To receive a customized proposal and price quote for private corporate training on-site or online, please contact us.

Objectives

  • Understand Data Science, including the Data Science Life Cycle
  • Understand the types of applications of Machine Learning
  • Understand what AIOps is and how it builds on top of traditional DevOps in a cloud environment
  • Understand considerations for infrastructures and topologies, including on-prem, hybrid, and micro-services variations
  • Understand the need for model explainability, both from a technical and business perspective
  • Use AutoML and other automation technologies (using AWS examples)
  • Work with intermediate data within a pipeline

Prerequisites

All students must have an analytics and/or Python background. Familiarity with AWS or other cloud environments is strongly encouraged. Students should have a familiarity with how data science and machine learning are used, at least from a business or product perspective. A general understanding of cloud DevOps is also strongly encouraged.

Outline

Expand All | Collapse All

Introduction
Data Science
  • Overview
  • Machine Learning
  • Asking the Right Questions
  • Artificial Intelligence: ML + Knowledge
  • The Data Science Pipeline
  • The Data Science Life Cycle
  • Data Science and AIOps
Machine Learning
  • ML for Analytics
  • ML for Prediction
  • ML for Regression
  • Scaling ML
AIOps
  • The Need for AIOps
  • The IT Operations Management Cycle
The Five Dimensions of AIOps
  • Data Set Selection
  • Pattern Discovery
  • Inference
  • Communications
  • Automation
Infrastructure and Topologies
  • Cloud, On-Premise, and Hybrid Cloud
  • Micro Services
  • Scaling
  • Cost Projections
  • The Failure of Traditional ITOM Technologies
  • Industry Examples
Model Explainability
  • Why are we getting these predictions?
  • Model reductions for explainability
  • Other trending techniques and solutions
Working with the Components
  • AWS
  • Data (AWS-S3)
    • Compute (EC2, deploying an API, loading data from S3)
    • AWS ML (an ML API endpoint)
  • Working Locally
    • Data Wrangler
    • Saving Intermediate Datasets
    • Flask API’s
    • Tableau for a Front End
Practical Exercise
  • Build a simple analytics app
  • Connect to data via API
  • Build a Data Science Pipeline as a middle layer
  • Connect to UI/front end (Tableau)
Emerging Trends
  • Emerging Technologies
    • Micro Services
    • Auto ML
    • NLP Trends and Techniques
    • Graph Databases and Network Graph Analysis
Conclusion

Training Materials

All AIOps training students receive comprehensive courseware.

Software Requirements

Students should have Python 3 installed with the ability to install other packages or programs (i.e., Admin Access) on their laptops. Anaconda with Python 3 is strongly recommended over the python.org installation.



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