Productionizing your forecast model

Concevoir des pipelines de prévision pour la production

Rami Krispin

Senior Manager, Data Science and Engineering

Introduction

• Senior manager - data science and engineering
• Time series, forecasting & MLOps
• Author
• Open source contributor

Forecasting in Production

• Forecasting pipeline architecture
• Set automation
• Monitor the pipeline

Motivation

• Automation - a recurring forecast task
  • Example: forecasting the hourly temperature
• Scale - large amount of series
  • Example: many series or high computing needs

General architecture

General architecture

General architecture

General architecture

General architecture

General architecture

Course outline

Chapter 1

• Review data source
• Pull the data from the EIA API

Chapter 2

• Experimentation process
  • Train, test, and log multiple forecasting models

Chapter 3

• Deployment process
  • Data automation, model refresh & capturing logs

Chapter 4

• Post-deployment steps
  • Monitoring & alerts
  • Best practices

Course prerequisites

Advanced course, prior knowledge required:

• Time series analysis and forecasting
• Orchestration systems
• APIs
• Python

Course tools

Let's practice!

Concevoir des pipelines de prévision pour la production