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Rapid prototyping quant research ml models using the qu sandbox
Quantuniversity offers training and consulting in data science, quantitative finance, and machine learning, having trained over 1,000 students. The document introduces QuSandbox, a platform for algorithmic auditing of AI and machine learning workflows, detailing its components for building reproducible models. It also discusses the challenges and frameworks in machine learning, emphasizing the importance of standardizing workflows and model management.
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Rapid prototyping quant research ml models using the qu sandbox
- 1. QUANTUNIVERSITY SUMMER SCHOOL RapidPrototyping Quant Research ML Models for Algorithmic Auditing using the QuSandbox 2020 Copyright QuantUniversity LLC. Presented By: Sri Krishnamurthy, CFA, CAP sri@quantuniversity.com www.quantuniversity.com 08/26/2019 QU SPEAKER SERIES 7 https://quspeakerseries7.splashthat.c om/
- 2. 2 Speaker bio • Quant,Data Science & ML practitioner • Prior Experience at MathWorks, Citigroup and Endeca and 25+ financial services and energy customers. • Columnist for the Wilmott Magazine • Teaches Data Science/AI at Northeastern University, Boston • Reviewer: Journal of Asset ManagementSri Krishnamurthy Founder and CEO QuantUniversity
- 3. 3 About QuantUniversity • Boston-basedData Science, Quant Finance and Machine Learning training and consulting advisory • Trained more than 1000 students in Quantitative methods, Data Science, ML and Big Data Technologies • Building a platform for operationalizing AI and Machine Learning in the Enterprise
- 4. Machine Learning Workflow DataScraping/ Ingestion Data Exploration Data Cleansing and Processing Feature Engineering Model Evaluation & Tuning Model Selection Model Deployment/ Inference Supervised Unsupervised Modeling Data Engineer, Dev Ops Engineer Data Scientist/QuantsSoftware/Web Engineer • AutoML • Model Validation • Interpretability Robotic Process Automation (RPA) (Microservices, Pipelines ) • SW: Web/ Rest API • HW: GPU, Cloud • Monitoring • Regression • KNN • Decision Trees • Naive Bayes • Neural Networks • Ensembles • Clustering • PCA • Autoencoder • RMS • MAPS • MAE • Confusion Matrix • Precision/Recall • ROC • Hyper-parameter tuning • Parameter Grids Risk Management/ Compliance(All stages) Analysts& DecisionMakers
- 5. 5 Source: Sculley etal., 2015 "Hidden Technical Debt in Machine Learning Systems" Challenges
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- 10. 10 • Understanding sentimentsin Earnings call transcripts Goal
- 11. 11 • Interpreting emotions •Labeling data Options • APIs • Human Insight • Expert Knowledge • Build your own Challenges 93
- 12. 12 NLP pipeline Data Ingestion fromEdgar Pre-Processing Invoking APIs to label data Compare APIs Build a new model for sentiment Analysis Stage 1 Stage 2 Stage 3 Stage 4 Stage 5 • Amazon Comprehend API • Google API • Watson API • Azure API
- 13. QuSandbox The platform forAlgorithmic Auditing of Data Science and AI workflows in the Enterprise 2019 Copyright QuantUniversity LLC.
- 14. 14 QuSandbox research suite ModelAnalytics Studio QuSandboxQuTrack QuResearchHub Prototype, Iterate and tune Standardize workflowsProductionize and share Track experiments
- 15. 15 The four componentsthat need to be encapsulated for reproducible pipelines Code Data Environment Process
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- 18. Sign up forUpdates at www.qusandbox.com srikrishnamurthy www.QuantUniversity.com www.analyticscertificate.com Information, data and drawings embodied in this presentation are strictly a property of QuantUniversity LLC. and shall not be distributed or used in any other publication without the prior written consent of QuantUniversity LLC. 18