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Practicing MLOps in your organization: tools, frameworks and governance Do the Right Thing: Fast Development of ML applications using Sub-ML Feature StoresSpeaker Venkata Pingali, Co-Founder & CEO, Scribble Data more
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Ten years of Hasgeek  Congratulations and best wishes for Hasgeek 2.0From friends at Scribble Data more
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CoE Revised Report on Non-Personal Data (NPD Version 2) Thoughts on Report by the Committee of Experts on Non-Personal Data Governance Framework (Dec 24, 2020)Overall Overall proposal is looking reasonable and feasible, even though a lot of details have to be worked out. more
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India's Non-Personal Data (NPD) framework Metadata directory and High Value Datasets (HVDs)Place holder for notes and comments on this session. more
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Data Governance Meetups PDP Checklist (Opensource Project)PDP compliance requires a range of tools. One of them is a simple checklist derived from the law. We took (with permission) the checklist provided by legal firm Ikigai law into a machine readable form (JSON, Markdown) and made it available as opensource. more
Session type:: Crisp talk/demo - 20 mins
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Privacy Engineering Conference ML Feature Store Enhancements for PDP ComplianceThe new PDP (Personal Data Protection) Law, which is similar to GDPR and CCPA, will be passed in the near future and will go into effect immediately. All enterprise data services including analytics and data science within the scope of the law are required to comply with the same. more
Session format: Talk - 40 mins
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The Fifth Elephant 2016 Increasing Trust and Efficiency of Data Science using dataset versioningAs data science grows and matures as a domain, harder questions are being asked by decision makers about trust and efficiency of data science process. Some of them include: more
Section: Crisp talk
Technical level: Intermediate
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The Fifth Elephant winter edition 2019 Reducing Cost of Production AI: Feature Engineering Case StudyThe number and complexity of datasets, usecases, and models are rapidly growing. However, the number of ML/AI applications in production are growing much more slowly. AI in production is suffering from a multiple challenges that vary by domain. We focus on a common activity - machine learning feature engineering involving textual data. It accounts for 40-80% of time and contributes significantly … more
Section: Crisp talk
Technical level: Intermediate
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The Fifth Elephant 2019 Anatomy of a production ML feature engineering platformThis talk addresses the following questions: What should a production ML feature engineering platform have and why? more
Session type: Full talk of 40 mins
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The Fifth Elephant 2019 BoF on ML platformsOn machine learning platforms, journeys in building them, and managing infrastructure for ML platforms more
Session type: BOF session of 1 hour
Session type: Birds of a Feather session of 1 hour
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The Fifth Elephant 2020 edition Privacy Law-Aware ML Data PreparationThe new PDP (Personal Data Protection) Law, which is similar to GDPR and CCPA, is being implemented in India. All enterprise data services including analytics and data science within the scope of the law are required to comply with the same. In this talk we will share how the bill impacts us at Scribble as a data processor, and mechanisms we are building to cope with the same. more
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MLOps Conference Past and Future of Feature StoresAudience Level: Intermediate Nature: Conceptual https://drive.google.com/file/d/1WpKUXeC0i93f72vC8yXtUR6unPEaB4ma/view?usp=sharing more
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MLOps Conference Scribble Enrich - 2nd Generation Feature Engineering PlatformScribble Enrich - 2nd Generation Feature Engineering Platform more
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Testimonials for Privacy Mode  Peer to Peer and Grounded Conversation Around PrivacyPrivacy is not easy to execute at the business or technical level. And we are discovering the challenges and opportunities as we go along. It is essential that we share these learnings in a timely and a transparent manner. That is where a platform like Privacy Mode comes into picture. more
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Venkata Pingali
@pingali