Learn to be more productive through ML projects that require reproducible workflow best practices.
Overview
Syllabus
- Machine Learning Pipeline
- Learn MLOps fundamentals and dive into version data and artifacts. Write a ML pipeline component and link together ML components.
- Data Exploration & Preparation
- Execute and track the Exploratory Data Analysis (EDA). Clean and pre-process the data and segregate (split) datasets.
- Data Validation
- Use pytest with parameters for reproducible and automatic data tests. Perform deterministic and non-deterministic data tests.
- Training, Validation & Experiment Tracking
- Tame the chaos with experiment, code, and data tracking. Track experiments with W&B. Validate and choose the best-performing model. Export model as an inference artifact and test final inference artifact.
- Release & Deploy
- Release pipeline code and learn options for deployment and how to deploy a model.
- Course Project: Build an ML Pipeline for Short-Term Rental Prices in NYC
- Write a machine learning pipeline to solve the following problem: A property management company is renting rooms and properties in New York for short periods on various rental platforms. They need to estimate the typical price for a given property based on the price of similar properties. The company receives new data in bulk every week, so the model needs to be retrained with the same cadence, necessitating a reusable pipeline. Write an end-to-end pipeline covering data fetching, validation, segregation, train and validation, test, and release. Run it on an initial data sample, then re-run it on a new data sample simulating a new data delivery.
Taught by
nd0821 Giacomo Vianello
