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Go from Hype to your Roadmap

Go from Hype to your Roadmap

Practical approach to applying Machine Learning to your product

Chandi Kodthiwada

December 10, 2018
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  1. Comprehend Systems Helping Life Science Companies Accelerate New Treatments to

    Market • Founded in 2010 • Life Science and Enterprise Software Expertise • Focused exclusively on Clinical Intelligence • Top Silicon Valley Investors - Sequoia and Lightspeed Chandi Kodthiwada Product Leader, Life Sciences Analytics • Analytics over 10 Years & Product Management – past 5 years • Previous BPMA Mentee
  2. SIMPLIFIED AI LANDSCAPE SOPHISTICATION MASS ADOPTION Deep AI (continually learning/aware)

    Narrow AI (basic/routine tasks) Chat Bots Natural Language Processing Personal Assistants (Siri, Alexa) Natural Language Processing Speech Processing Machine Learning Tay by Microsoft Natural Language Processing Machine learning Automated Insights Machine Learning Natural Language Processing (with structured data) Autopilot by Tesla Machine Learning (with Unstructured data, Computer Vision/Situational awareness) Alpha Go (Neural Network) Deep Dream Machine Learning (Neural Network) Einstein (with Structured data) Watson Machine Learning + Speech Processing (with structured & unstructured data) Reference
  3. “The Usual” LAB CLINICAL OPERATIONS GENOMI C EHR SENSORS &

    DEVICE DRUG SAFETY EDC 3RD PARTY Staging Processed Repository Analytics & Reporting Sources + (1 to 7 days) + (7 to 14 days) + (1 to 7 days)
  4. When & How? The User Experience Information Highways Apply Machine

    Learning to Data Transformation Apply Machine Learning to Insight generation Deliver Insights Feedback Loop Enable Insights to Actions Framework