Intro to ML In Production Coursera course

Course URL: https://www.coursera.org/learn/introduction-to-machine-learning-in-production/

Co instructors from the Tensorflow team:

• Robert Crowe
• Laurence Moroney (AI Advocacy; “AI and machine learning for coders”)

Issues:

• Data drift - distribution of data has changed.
• Deployment

Courses:

1. overview of entire lifecycle of production ml project - scoping, development, deployment
2. evolution of data pipelines. TFX. data provanance
3. ml models in production; minimize costs, analytics, explainability
4. deployment : serve users request. deployment pipelines. diff infras. best practices.. continuously operating pipelines.

Course 1

Week 1

• concept drift or data drift
• holdout test set
• proof of concept
• jupyter notebook
• POC to production gap; sheer amount of work. the doughut around the hole.
• D Sculley et. al NIPS 2015: Hidden technical debt in machine learning systems.
• “Other pieces of software” for production Ml systems.
• Framework - systematically planout the lifecycle.

STEPS OF FULL LIFECYCLE ML PROJECTS

• scoping – define project
• data – define and establish baseline, label and organize data
• modeling – select and train model, platform error analysis. (iterative process)
• Deployment – deploy in prod, monitor and maintain system.

(diagram) Learning AI’s Steven Layett and Daniel Pipryata.

LandingLens - MLOps -

Week 2

• Key challenges in building production ready MODELS
• model centric ai dev vs data centric ai dev
• AI system = code + data
• model development = model + hyperparameters + data
• LandingLens - Error Analysis

Week 3

Data Definition

Define data and establish baseline. eg: iguana labeling, phone scratch/pitmark.

Label Ambiguity - combinatorially different ways to transcribe.