Agile Data Science - Achieving Salesforce-Scale Machine Learning in Production

Overview

This course teaches learners how to achieve Salesforce-scale machine learning in production through an Agile data science approach. The course covers tools such as the open-source autoML library TransmogrifAI, experimentation, deployment, and monitoring. The teaching method includes a detailed overview of Salesforce's platform and Agile process. The intended audience for this course includes data scientists, software developers, and individuals interested in deploying machine learning models at scale.

Syllabus

Introduction
Why are AI Machine Learning and Data Science still out of reach
What does it mean to move beyond giving your data scientists access
Salesforces approach to AI
Agenda
Building ML apps
No company is building one app
We need a third data scientist
Different degrees of skill set
Different data sizes
Classification
Language
Customization
Trust
Fixing leaks
Traditional AI process
Automation
Data Science Journey
Building Models
Getting Access to Data
Shipping Your App
Everyone Needs a Data Scientist
Data Scientists and Software Developers
Data Scientist
Building a Platform
Working Together
Finding opportunities for reuse
Transmogrify
Automated Pipeline
Data Sampling
Text Data
Stop Words
Learning Opportunities
Model Selection
The Job is Never Done
Metrics to Drive Agility
What Happens After Deployment
Minimum Viable Product
Agile Process
Agile Data Science
Monitoring
Model Monitoring
Investigate
Backlog
Focus
Key takeaways
Join the open source community
Thank you
Getting started in data science
ACM resources
Open source components
Platform secured experimentation
Latency considerations