Intro to ML In Production Coursera course

Machine Learning Engineering for Production (MLOps) Specialization

Created: by Pradeep Gowda Updated: Jan 30, 2023 Tagged: machine-learning · mlops · coursera

Course URL:

Co instructors from the Tensorflow team:

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


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


  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.


  • 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.