Approximate Inference for a Topic Model on Customer Tickets
Overview
What this challenge is about.
Approximate Inference for a Topic Model on Customer Tickets. Intermediate challenge in analysis. Analyzing real datasets and building models that drive decis...
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.
This is not a data exercise. It is the work an analyst does when stakeholders need answers from messy data. That distinction matters to every hiring manager who has seen candidates describe statistical methods and none who have extracted insight from messy, real-world data.
When you finish, you will have something most graduates do not: a real-world deliverable, verified by Ewance, that you can show to a hiring manager and say "I did this. Here is the proof."
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