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.
In addition, some courses are available as live, instructor-led training from one of our partners.
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
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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.
