Approximate Inference for a Topic Model on Customer Tickets
Overview
What this challenge is about.
Compare LDA topic models on B2B SaaS tickets via SVI and Gibbs sampling, then automate the winner — earn a verifiable certificate.
The scenario
The SaaS company (around 220 staff, EUR 35M Annual Recurring Revenue) ships product updates every two weeks; the product team wants a topic dashboard that reflects yesterday's tickets, not last quarter's.
The Brief
What you'll do, and what you'll demonstrate.
Compare variational and Gibbs inference for a weekly-refreshed LDA topic model on support tickets, and recommend one with documented trade-offs.
Earning criteria — what you'll demonstrate
- Implement and compare stochastic variational inference vs. collapsed Gibbs sampling
- Measure topic-model quality with held-out perplexity and stability metrics
- Diagnose and explain topic drift in production
- Translate a probabilistic-inference choice into a business-readable note
Program Fit
Where this fits in your program.
Sharpens the same skills your degree expects you to demonstrate.
Aligned coursework coming soon.
Skills
Skills you'll demonstrate.
Each one shows up on your verified credential.
- Variational Inference
Apply variational inference to solve real industry problems and demonstrate production-level capability.
- Latent Dirichlet Allocation
Apply latent dirichlet allocation to solve real industry problems and demonstrate production-level capability.
- Approximate Inference
Apply approximate inference to solve real industry problems and demonstrate production-level capability.
- Python
Write clean, efficient Python for data processing, automation, and backend services.
- Model Evaluation
Apply model evaluation to solve real industry problems and demonstrate production-level capability.
- Text Processing
Apply text processing to solve real industry problems and demonstrate production-level capability.
Careers
Career paths this challenge builds toward
Completing this challenge demonstrates skills that transfer directly to these roles:
Machine Learning Engineer
Choosing an inference algorithm under real production constraints (weekly refresh, stability, latency) is the kind of MLE judgement call hiring managers look for.
This challenge sharpens
- variational-inference
- python
- model-evaluation
NLP Engineer
Topic modeling on real support text plus text preprocessing at scale is core NLP-engineer territory at any product-led SaaS.
This challenge sharpens
- latent-dirichlet-allocation
- text-processing
- model-evaluation
Data Scientist
Diagnosing why a probabilistic model drifted week-over-week and communicating the fix is exactly what data scientists do when dashboards lose trust.
This challenge sharpens
- approximate-inference
- model-evaluation
- text-processing