Watch Zepl CTO Moon Lee and Sr. Product Manager Louis Huard present “The Holy Grail of Data Science: Rapid Development and Deployment” at a recent meetup.

We were also fortunate to have Parag Shrivastava, Sr. Director of Data & Analytics, McKesson and formerly of Deloitte discuss “Leverage AI and Machine Learning To Grow New Markets and Optimize Operational Costs.”

Full abstracts here:

The Holy Grail of Data Science: Rapid Model Development and Deployment

A key step in the data science workflow is rapid model development in order to create, test, and identify the best models to put into production. However, large gaps exist in this workflow, and the data science toolset is rapidly changing to fill those gaps. Large teams and enterprises are quickly moving from using individual siloed notebooks like Zeppelin and Jupyter to wanting to share and reuse models, code and results. Challenges also exist in deploying models into production and model serving using tools like Kubeflow and Tensorflow. We will discuss real-world examples of how companies are solving these problems, and how you can use these best practices in your own workflow.
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Moon Lee
Co-Founder and CTO of Zepl
Moon Lee is the creator of Apache Zeppelin, with more than 500,000 downloads worldwide. Moon is also the co-founder and CTO of Zepl, the data science platform that provides enterprise-grade support for Zeppelin and Jupyter notebooks.

Louis Huard
Sr. Product Manager of Zepl
Prior to joining Zepl, Louis was a product manager for Cisco AppDynamics BiQ platform and the machine learning startup they acquired, Perspica. In addition, Louis has spent years as a management consultant, using forensic data science and analytics techniques to solve legal, financial, and regulatory issues at some of the world’s largest companies.

Leverage AI and Machine Learning for growing new markets, and optimize operational costs

In a shared economy, customer behavior is fast changing. Business customers (B2B) are demanding insights to better manage their operations, and individual consumers (B2C) are looking for relevant, timely, and easier access to information. AI and Machine Learning are helping to achieve these needs, while also providing ways to optimize operational costs thereby making the business profitable.

Learn how insights are incorporated into customer and operational products using design thinking, strategy, and data. Hear Healthcare and cross-industry use cases, customer-driven design, data engineering, and data sciences methodologies. Discuss the challenges in making the data usable and scalable and practical ways to make progress while keeping product goals in mind. Review making AI at scale, and bring fail-fast approaches to innovation while managing cross-functional stakeholders’ expectations.
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Parag Shrivastava
Sr. Director of Data & Analytics, McKesson, Deloitte
Parag is a data engineering and science product leader, having led teams to deliver business goals and re-define business strategy using data insights. He has executed key initiatives at McKesson including data analytics for Joint Venture between McKesson and Walmart, M&A analytics for McKesson’s large acquisition in Europe, and the customer 360 for sales and marketing. Prior to McKesson, Parag was a Manager at Deloitte Consulting, primarily focused on data and technology strategy and transformation at large Healthcare provider, payor, and medical device companies. Parag holds an MBA from Cornell University and a bachelor’s degree in engineering.