LO - AI – 101 Ai-OPs Koios for Process Monitoring and Control- Online
Participants will be prepared to use Machine Learning & Reinforcement Learning with process data, first principles models, & common process control approaches to create Python model objects that deploy in the Koios software platform. Topics include Digital Twins, Reinforcement Learning techniques for training process control models, & preparing time series process data for Machine Learning & Reinforcement Learning. Hands-on sections involve learning to code in Python and use the Ai-OPs Software Dev. Kit.
Description
Learning Outcomes: Participants will learn the theory of Machine Learning and Reinforcement Learning for process monitoring and control as well as the skills needed to use the Ai-OPs Software Development Kits and common Python libraries such as PyTorch and Stable-Baselines3 to develop custom models that deploy in the Koios software platform.
Deliverables: Participants will receive all presented material as well as all Python code, data, and models created during the course.
Course Structure
Day 1 – Background
Section 1: Python
• Data Engineering
• Functions and Classes
Section 2: Machine Learning
• Regression
• Neural Networks
Section 3: Reinforcement Learning
• Environments
• Agents
Day 2 – Application
Section 1: Ai-OPs Software Development Kits
• Koios Components
• Koios Models
Section 2: Process Monitoring
• Process Analytics with Koios Components
• Process Prediction with Neural Networks
Section 3: Process Control
• Digital Twins
• Reinforcement Learning Agents
Pricing
Standard – Generalized course material
Online: $1,300 per participant
Custom – Your data, custom code dev
Online: $1,625 per participant
Prerequisites
Prerequisites
Participants must have a computer with Python ≥3.12 installed.
Participants’ computer accounts must have the level of
permissions needed to install common open-source Python
libraries including the Ai-OPs SDKs. Participants should have a
basic understanding of numerical methods. Python experience is
not required.