Small teams, big dreams: How you can get started with AI today
summary
A mid-market product uses Kepler automated ML platform to let non-experts build end-to-end models: a session-intent classifier reaches 86% accuracy in 2–3 weeks and serves ~90,000 prediction calls per day, a text-classifier is built in 2 days with +9% accuracy and millisecond latency, and two part-time users deliver five projects in a year. The platform exposes model drift, bias and leakage checks and surfaces the single variable that explains a 10% KPI drop so business teams can monitor and act.
AI-written summary · about
chapters (7)
- 0:00Introduction and speaker background
- 1:44AI evolution over the last decade
- 7:43Automated machine learning explained
- 11:14Real-world AI success story
- 18:34Business user achieves breakthrough
- 21:00Key tips for AI implementation
- 24:09Key takeaways and conclusion
related talks
45mStart your AI/ML Projects Right
01h 05mQuick Start with AI for Businesses
54mGradient ascent for your career
22mBefore the Model: How Machine Learning Products Start...
42mAI-powered products and product-powered AI
18mSmall AI Teams with Huge Impact
36mSolve common business problems with AI/ML
47mBuilding smarter solutions without machine learning expertise