Mastering Post-Deployment Data Science
Beat the 91%, learn how to manage machine learning models in production, from performance monitoring to issue resolution
Why should your learn post deployment data science?
Data science is changing. Data scientists are realising it. Join over 400 data scientists who have already started up-skilling today.
✅ Understand workflows needed to manage a machine learning model post deployment
✅ Build an end to end open source machine learning monitoring system
✅ Identify issues in model performance and learn how to resolve them
✅ Analyse historical performance of a model in production
✅ Be able to communicate with other stakeholders about the performance of your models
✅ Explain how changes in data impact model performance
✅ Understand all the algorithms needed for managing models in production (Performance estimation, multivariate drift, univariate drift, and others.)
Course Curriculum
- Lesson 8.1 - Performance Calculation (5:25)
- Lesson 8.2 - Chunking (2:59)
- Lesson 8.3 - Performance Estimation (8:56)
- Lesson 8.4 - Business Value (3:16)
- Lesson 8.5 - Deep Dive in CBPE (8:14)
- Lesson 8.6 - Deep Dive in DLE (4:15)
- Lesson 8.7 - Exercise: Performance monitoring for regression with NannyML (6:53)
- Lesson 8.8 - Exercise: Performance monitoring for classification with NannyML (8:10)
- Lesson 9.1 - What is a Root Cause Analysis of a Model Failure (0:55)
- Lesson 9.2 - Multivariate drift detection (1:52)
- Lesson 9.3 - Exercise: Multivariate drift detection (2:03)
- Lesson 9.4 - Overview of univariate drift detection methods (1:24)
- Lesson 9.5 - Jensen-Shannon Distance (3:13)
- Lesson 9.6 - Wasserstein Distance (2:03)
- Lesson 9.7 - Hellinger Distance (1:57)
- Lesson 9.8 - L-Infinity Distance (1:36)
- Lesson 9.9 - Kolmogorov-Smirnov Test (1:45)
- Lesson 9.10 - Chi-squared Test (2:14)
- Lesson 9.11 - Prediction drift and Target drift (2:22)
- Lesson 9.12 - Exercise: Univariate Drift Detection (6:14)
- Lesson 9.13 - Data Quality Checks (3:26)
- Lesson 9.14 - Exercise: Data Quality Checks (2:54)
- Lesson 9.15 - Summary Statistics (0:43)
- Lesson 9.16 - Exercise: Summary Statistics (3:27)
- Lesson 9.17 - Ranking (1:05)
- Lesson 9.18 - Exercise: Ranking (4:04)
- Lesson 10.1 - Strategies to fix model performance failures (0:29)
- Lesson 10.2 - Retraining the Model (0:50)
- Lesson 10.3 - Change Business Process (1:26)
- Lesson 10.4 - Reverting back to a previous model (0:23)
- Lesson 10.5 - Inducing the new drift to the old data (0:42)
- Lesson 10.6 - Refactor the use case (0:30)
- Lesson 10.7 - Doing nothing (0:38)