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Presentation: Panel: First Steps with Machine Learning

Track: Solving Software Engineering Problems with Machine Learning

Location: Cyril Magnin III

Duration: 2:40pm - 3:20pm

Day of week: Wednesday

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Throughout the day, we'll have speakers cover how they've adopted applied machine learning to software engineering. The day wraps with a discussion from the speakers on taking an applied, pragmatic approach to adding ML to you systems and how they solved challenges. Eager to deploy ML and have questions? This is a forum to discuss, learn, and help crystalize that roadmap. Join Cliff Click, Soups Ranjan, and the track speakers as they discuss first principles adding ML to your systems.

Speaker: Nischal Harohalli Padmanabha

VP of Engineering and Data Science at @Omnius

Nischal HP is currently the VP of Engineering and Data science at Berlin based AI startup omni:us, which operates in the building of AI product for the insurance industry. Previously, he was a cofounder and data scientist at Unnati Data Labs, where he worked towards building end-to-end data science systems in the fields of fintech, marketing analytics, event management and medical domain. Nischal is also a mentor for data science on Springboard. During his tenure at former companies like Redmart and SAP, he was involved in architecting and building software for ecommerce systems in catalog management, recommendation engines, sentiment analyzers , data crawling frameworks, intention mining systems and gamification of technical indicators for algorithmic trading platforms. Nischal has conducted workshops in the field of deep learning and has spoken at a number of data science conferences like Pycon Canda 2018, Oreilly strata San jose 2017, PyData London 2016, Pycon Czech Republic 2015, Fifthelephant India (2015 and 2016), Anthill, Bangalore 2016. He is a strong believer of open source and loves to architect big, fast, and reliable AI systems. In his free time, he enjoys traveling with his significant other, music and groking the web.

Find Nischal Harohalli Padmanabha at

Speaker: Shengyu Fu

Principal Data Scientist Manager @Microsoft


I lead an applied science team in Microsoft Cloud & AI division. Our mission is to infuse ML & AI into Microsoft developer platforms and tools. My team initiated the Visual Studio IntelliCode project and is responsible for the ML models powering the smarter intellisense for multiple programming languages in Visual Studio and Visual Studio Code. I have extensive experience in machine learning, big data mining and software enrginnering. I'm very passionate to solve tough problems through smart data and software.

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Speaker: Soups Ranjan

Financial Crime Risk @RevolutApp

Soups Ranjan heads Financial Crime Risk at Revolut, the fastest growing challenger bank in Europe. He leads the team in charge of preventing financial crime on Revolut’s platform using data science and machine learning. Soups has 14 years of experience applying machine learning to domains ranging from network security to advertising and cryptocurrencies. Prior to Revolut, Soups was the director of data science and risk at Coinbase, one of the largest cryptocurrency exchanges in the world. At Coinbase, Soups built many engineering teams from the ground up including data, risk and identity. Soups is the co-founder of, a roundtable forum for risk professionals in San Francisco and Seattle to share ideas on stopping financial crime. Soups holds a PhD in ECE focused on network security from Rice University. Soups currently lives in Berkeley with his family.

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Speaker: Cliff Click

CTO of @CratusTech

Cliff Click was the CTO of Neurensic (now successfully exited), and CTO and Co-Founder of (formerly 0xdata), a firm dedicated to creating a new way to think about web-scale math and real-time analytics. He wrote his first compiler when he was 15 (Pascal to TRS Z-80!), although his most famous compiler is the HotSpot Server Compiler (the Sea of Nodes IR). Cliff helped Azul Systems build an 864 core pure-Java mainframe that keeps GC pauses on 500Gb heaps in the micro-second range, and worked on all aspects of that JVM. Before that he worked on HotSpot at Sun, and is, at least, partially responsible for bringing Java into the mainstream.  Cliff is invited to speak regularly at industry and academic conferences and he holds a PhD in Computer Science and more than 20 patents.

Find Cliff Click at

2019 Tracks

  • ML in Action

    Applied track demonstrating how to train, score, and handle common machine learning use cases, including heavy concentration in the space of security and fraud

  • Deep Learning in Practice

    Deep learning use cases around edge computing, deep learning for search, explainability, fairness, and perception.

  • Handling Sequential Data Like an Expert / ML Applied to Operations

    Discussing the complexities of time (half track) and Machine Learning in the data center (half track). Exploring topics from hyper loglog to predictive auto-scaling in each of two half-day tracks.

    Half-day tracks