Data Science Salon: Kaggle 1st Place in 30 minutes: Putting AutoML to Work with Enterprise Data
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The document discusses the application of automated machine learning (AutoML) in enterprise data contexts, highlighting its potential to improve decision-making processes and scalability. It references various case studies, including a 90% reduction in false alarms in video analytics and successful competitions on platforms like Kaggle. The text emphasizes the importance of overcoming barriers such as lack of talent and management support for effective implementation of machine learning technologies.
Data Science Salon: Kaggle 1st Place in 30 minutes: Putting AutoML to Work with Enterprise Data
- 1. Kaggle 1st Place in 30 Minutes: Putting AutoML to Work with Enterprise Data HILA LAMM Chief Strategy Officer at Firefly.AI
- 5. • The printing press was a factor in the establishment of a community of scientists who could easily communicate their discoveries through widely disseminated scholarly journals, helping to bring on the scientific revolution. • Because the printing process ensured that the same information fell on the same pages, page numbering, tables of contents, and indices became common. • The arrival of mechanical movable type printing introduced the era of mass communication, which permanently altered the structure of society. The relatively unrestricted circulation of information and revolutionary ideas transcended borders. https://courses.lumenlearning.com/suny-hccc-worldhistory/chapter/the-printing-revolution/
- 6. Two Types of Applied ML Mimic human ability Automation = Faster, cheaper, more consistent Improve on human computation ability Better decision making
- 7. • To lend or not to lend? • When should I service the equipment? • Is she about to leave me? What can I do to make her stay? • Is this a cyberattack or are you just happy to see me? • Is there someone on the train tracks or is it just a cloud? Which Decision Can I Superpower?
- 8. Great! Let’s do ML!
- 9. Kaggle 2017 The State of Data Science & Machine Learning What barriers are faced at work? 7,376 responses, showing top 15 responses Lack of data science talent Lack of management support Lack of clear questions to ask Reality Check
- 10. 1. Find the questions behind key daily decisions 2. Evaluate the impact of taking the decisions faster or in more accurate way 3. Go fetch the data Lack of clear questions to ask
- 11. Lack of management support > What do executives want? Business value (ROI) Predictable time to delivery Easy to scale up Machine learning techniques Research project A new research project FOCUS ON THE BUSINESS SHORT TIME TO DELIVERY SCALABLE AND AUTOMATED FAST, COST EFFECTIVE EXPERIMENTS
- 12. Lack of data science talent • Liberate talent from routine work • Use tools to make machine learning accessible to more people
- 13. AutoML features to look for: • Algorithm availability • Preprocessing capabilities • Search methods • Ensembles • Explainability • Enterprise readiness Automatically build in parallel multiple models to select the best Open-Source/Paid AutoML tools • AutoWEKA • Auto-sklearn • TPOT • Google Cloud AutoML • H20 AutoML Apply data preprocessing Research to pinpoint the right ML algorithm Optimize hyperparameters for selected algorithms Golden ML ensemble Automate the design of machine learning models: AutoML
- 14. Data Import & Analysis Regression Classification Recommendation Time Series Generate solution: Ensemble of best algorithms and models Meta learning Preprocessing Firefly API Firefly Lab - Model building Model exports AutoML Platform Anomaly Detection Report results and model insights Algorithm selection, Hyperparameter optimization Firefly Predict Deploy on premises in operational system Firefly user interface Upload dataset for batch predictions Real-time predict requests Batch predict requests
- 15. Target: Reduce false alarm rate of existing video analytics system Data: Feature extraction from moving objects in the videos - a series of ellipses indicating areas of change Solution: Model per camera/sensor location Results: Reduced by 90% false alarms Case Study: Homeland Security
- 16. Case Study: Homeland Security
- 17. Target: Identify cyberattacks based on behavioral indicators Data: Hundreds of features of network IoT data Need: Fast experiments to identify relevant features per environment Solution: Highly accurate, dedicated models per environment Case Study: Cybersecurity
- 18. Predict the time it takes to pass testing for different permutations of Mercedes-Benz car features Mercedes-Benz reliability prediction 1st place Of 3835 teams 377 30 min Data Scientist time Features
- 19. Predicting customer satisfaction using customer features Santander Bank Kaggle challenge 1st place Of 5123 teams 370 Features 20 min Data Scientist time