DriftLog - Automated ML Feature Distribution Drift Monitor for Solo Data Scientists

Your model was great in January. It is now quietly wrong and you have no idea. DriftLog watches your production feature distributions, compares them to training baselines using statistical tests, and Slacks you before your accuracy tanks. No MLOps team required.

𝕏 Post Reddit HN

Difficulty

intermediate

What is it?

Solo data scientists and ML engineers at small companies deploy models and then pray nothing changes upstream. Feature drift — when production data starts looking different from training data — is the silent killer of ML model performance. Existing tools like Evidently AI and Arize are either self-hosted nightmares or enterprise-priced. DriftLog is a lightweight Python SDK that captures feature snapshots from your inference pipeline, runs PSI and KS-test drift checks on a cron, and pushes alerts to Slack or email when a feature drifts beyond threshold. It is a Data and ML Pipelines idea with a two-file integration. Why buildable now: scipy stats, pandas profiling, and Supabase are all production-stable, and the April 2026 vibe-coding wave has thousands of solo ML engineers deploying models on Railway and Fly.io with zero monitoring.

Why now?

The April 2026 vibe-coding wave has created thousands of solo-deployed ML models on Railway and Fly.io with zero monitoring, and scipy plus Supabase make a hosted drift monitor shippable in a weekend for the first time.

▸Two-line Python SDK integration that logs feature snapshots to Supabase on every inference batch
▸Automated PSI and KS-test drift scoring on configurable cron schedule (scipy)
▸Slack and email alert with feature distribution chart when drift exceeds threshold
▸Next.js dashboard showing feature drift trends over 90 days per model

Target Audience

Solo ML engineers and data scientists at startups with 1-5 person teams, estimated 50,000+ based on Kaggle's active competition base and ML subreddit size.

Example Use Case

Priya deploys a churn prediction model, integrates DriftLog in 10 minutes with two lines of Python, and gets a Slack alert 3 weeks later when an upstream ETL change shifts her tenure_months distribution — catching the drift before her model's AUC drops below SLA.

User Stories

▸As a solo ML engineer, I want drift alerts before my model degrades, so that I catch upstream data changes before users notice.
▸As a data scientist, I want a two-line SDK integration, so that I do not spend days configuring MLOps infrastructure.
▸As a startup CTO, I want a 90-day feature distribution dashboard, so that I can prove model stability to stakeholders.

Done When

✓SDK Integration: done when pip install plus 2 lines logs feature stats to Supabase without errors
✓Drift Detection: done when KS-test correctly flags a synthetic distribution shift in unit tests
✓Slack Alert: done when Slack message arrives within 10 minutes of drift threshold breach
✓Dashboard: done when feature trend chart loads with real snapshot data in under 2 seconds.

Is it worth building?

$39/month × 80 solo ML engineers = $3,120 MRR at month 3. $99/month team plan × 30 teams = $2,970 MRR. Combined $6k MRR at month 5 assumes cold outreach at 4% conversion from 200 contacted.

Unit Economics

CAC: $15 via LinkedIn outreach (2h founder time). LTV: $936 (24 months at $39/month). Payback: under 1 month. Gross margin: 92%.

Business Model

SaaS subscription

Monetization Path

Free tier: 2 models, 7-day history. Pro $39/month: unlimited models, 90-day history, Slack alerts. Team $99/month: 5 seats, custom thresholds.

Revenue Timeline

First dollar: week 4 via first Pro upgrade. $1k MRR: month 3. $5k MRR: month 7. $10k MRR: month 14.

Estimated Monthly Cost

Supabase: $25, Vercel: $20, Resend: $10, Fly.io for cron worker: $10. Total: ~$65/month at launch.

Profit Potential

Full-time viable at $8k-$12k MRR. Acqui-hire potential from MLOps platforms.

Scalability

Add model performance correlation, multi-cloud connector, and anomaly detection on output distributions.

Success Metrics

Week 2: 20 SDK installs. Month 1: 10 paying users. Month 3: 70% 90-day retention.

Launch & Validation Plan

Post in r/MachineLearning and r/mlops with a demo GIF showing drift catch, collect 50 GitHub stars before charging, DM 20 ML engineers from LinkedIn offering free Pro.

Customer Acquisition Strategy

First customer: DM 25 solo ML engineers on LinkedIn who post about model monitoring complaints, offer 6 months free Pro in exchange for a case study. Ongoing: PyPI install README badge, r/mlops, GitHub Sponsors, DevRel content on X.

What's the competition?

Competition Level

Medium

What's the roadmap?

Feature Roadmap

V1 (launch): Python SDK, drift scoring, Slack alerts, trend dashboard. V2 (month 2-3): model output drift, email digest, threshold tuning UI. V3 (month 4+): multi-model comparison, team seats, anomaly detection.

Milestone Plan

Phase 1 (Week 1-2): SDK on PyPI and FastAPI ingest live, 5 beta engineers logging data. Phase 2 (Week 3-4): drift cron, Slack alerts, and dashboard live, first drift catch confirmed. Phase 3 (Month 2): 15 paid users, 90-day history working.

How do you build it?

Tech Stack

Python SDK, FastAPI for dashboard API, scipy for drift tests, Supabase for snapshot storage, Next.js dashboard, Resend for alerts — build with Cursor for SDK and FastAPI, v0 for dashboard.

Suggested Frameworks

scipy, pandas, FastAPI

Time to Ship

3 weeks

Required Skills

Python SDK design, scipy statistical tests, FastAPI, Supabase, basic Next.js dashboard.

Resources

scipy stats docs, Evidently AI open-source for reference, Supabase Python client docs, FastAPI quickstart.

MVP Scope

driftlog SDK Python package (driftlog/client.py, driftlog/stats.py), FastAPI ingest endpoint, Supabase schema for snapshots and drift scores, cron job worker, Slack webhook integration, Next.js dashboard with feature charts, PyPI publish config.

Core User Journey

pip install driftlog -> add 2 lines to inference script -> connect Slack -> receive first drift alert -> upgrade to Pro.

Architecture Pattern

Python SDK called in inference code -> batches feature stats -> POST to FastAPI ingest endpoint -> stored in Supabase snapshots table -> cron worker runs scipy drift tests every 6h -> drift score saved -> if above threshold: Slack webhook fires -> dashboard reads Supabase for charts.

Data Model

User has many Models. Model has many FeatureSnapshots. FeatureSnapshot has many DriftScores. DriftScore triggers one Alert if above threshold.

Integration Points

Supabase for snapshot and drift score storage, Slack Webhooks for alerts, Resend for email digests, Vercel for dashboard hosting, Fly.io for cron worker.

V1 Scope Boundaries

V1 excludes: real-time streaming, model performance tracking, custom drift algorithms, team collaboration, on-premise deployment.

Success Definition

A solo ML engineer installs the SDK, connects Slack, and receives their first real drift alert without any founder help, then upgrades to Pro.

Challenges

The hardest non-technical problem is convincing solo engineers to add any SDK to their inference pipeline — they fear latency impact. Must prove sub-5ms overhead with benchmarks in the README.

Avoid These Pitfalls

Do not log raw row-level data — only aggregate stats to avoid PII liability. Do not support real-time streaming in V1, batch-only is enough. Finding first 10 paying users takes 3x longer than building the SDK — prioritize GitHub visibility first.

Security Requirements

Supabase Auth with API key for SDK, RLS on all user model and snapshot tables, rate limit 200 req/min per API key, reject raw row-level data in ingest validator, GDPR: only aggregate stats stored, deletion endpoint available.

Infrastructure Plan

Vercel for Next.js dashboard, Supabase for Postgres, Fly.io for FastAPI ingest and cron worker, GitHub Actions for CI and PyPI publish, Sentry for errors. Total: ~$65/month.

Performance Targets

200 DAU at launch, 5,000 ingest req/day. Ingest endpoint under 100ms. Dashboard under 2s LCP. Batch-only processing, no real-time streaming required in V1.

Go-Live Checklist

☐Security audit complete
☐Payment flow tested end-to-end
☐Sentry live
☐Monitoring dashboard configured
☐Custom domain set up
☐Privacy policy and terms published
☐5 beta engineers signed off
☐Rollback plan documented
☐PyPI and ProductHunt launch posts drafted.

First Run Experience

How to build it, step by step

1. Create Python package structure with driftlog/client.py and driftlog/stats.py using scipy KS and PSI functions. 2. Create FastAPI app with /ingest endpoint that receives feature stats JSON and writes to Supabase. 3. Set up Supabase schema for models, snapshots, drift_scores, alerts tables. 4. Build cron worker on Fly.io that queries recent snapshots and runs drift tests every 6 hours. 5. Add Slack webhook POST when drift score exceeds configurable threshold. 6. Run npx create-next-app driftlog-dashboard and scaffold feature trend chart pages using Recharts. 7. Connect dashboard to Supabase for live drift score reads. 8. Add Stripe billing for Pro plan gating. 9. Publish SDK to PyPI with README demo GIF. 10. Deploy dashboard to Vercel and cron worker to Fly.io.

Generated

April 17, 2026

Model

claude-sonnet-4-6

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