Ankit Rathi

Ankit Rathi is a well known blogger, author & speaker in data science & artificial intelligence field. He provides more than 14 years of unique & rich combination of Data Engineering (DB/ETL/DWH/BI)/Architecture (Data Management & Governance) & Data Science (ML/DL/AI) with more than a decade of demonstrated history of working in IT industry using Data & Analytics. His interest lies primarily in building end to end DS/AI applications/products following best practices of Data Engineering and Architecture .

Ankit's work at SITA aero has revolved around building FlightPredictor product & strengthening the CoE capability. Earlier as a Principal Consultant at Genpact HCM, Ankit architected and deployed machine learning pipelines for various clients across different industries like Insurance, F&A. He was previously a Tech Lead at RBS IDC where he designed and developed various data intensive applications in AML & Mortgages area.

Ankit is a well-known author for various publications (Data Deft, Towards Data Science, Analytics Vidhya etc) on Medium where he actively contributes by writing blog-posts on concepts & latest trends in Data Science. He is followed by 29K+ data science practitioners & enthusiasts on LinkedIn.

  • Publications: 'DS/AI Self-Starter Handbook' & 'Probability & Statistics for Data Science'

  • Blog*: https://medium.com/@rathi.ankit

  • GitHub: https://github.com/ankitrathi169

  • Kaggle°: https://www.kaggle.com/ankitrathi

  • SlideShare*: https://www.slideshare.net/ankitrathi

  • Twitter: https://twitter.com/rathiankit

  • VisualCV: https://www.visualcv.com/ankit-rathi

*all views expressed in these articles are my own & do not represent my employer

°achieved 'Kaggle Expert' level on Kaggle Data Science platform in 2017

  • Specialties: data science, data architecture, data strategy, big data, cloud computing

  • Tools/Technologies: SQL, Python, R, Spark, Azure, AWS, TensorFlow, Cassandra, Hadoop, Pig, Hive, Tableau, PowerBI, DevOps, CI/CD

Presentations

Understanding Opacity in Machine Learning Models

Saturday 12:00 PM–12:40 PM in C11

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