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Evaluating Big Data & ML Solutions

December 15 @ 10:00 am - 11:30 am EST

* Please note the event is open for everyone (regardless of gender)
* Also note that the times listed below are based on Israel time (GMT+2)

AGENDA
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10:00 AM EST (17:00 Israel time) – Opening notes
10:05 AM EST (17:05 Israel time) – Planning a data solution: “By Failing to prepare, you are preparing to fail” – Eynav Mass @ Oribi
10:40 AM EST (17:40 Israel time) – Why do the majority of Data Science projects never make it to production? – María de la Fuente @ Databricks
11: 15 AM EST (18:15 Israel time) – Closing notes

* Both sessions will be delivered in English

Title: Planning a data solution: “By Failing to prepare, you are preparing to fail”

Abstract:
When it comes to data solutions, one-size doesn’t fit all. Choosing the right best-matching database, or data tools, can be a game-changer for your system.
How can you take such a decision effectively? The system, the company, the product, and probably your team – all are evolving, and the best solution for today may not fit tomorrow’s needs. In order to pick a data solution for longer term, you should evaluate the optional data tools according to several factors. These factors will reflect the requirements looking forward. At the session, we will discuss these factors, along with sharing some real-life stories and lessons learned, to help you properly plan & prepare your data solutions.

Bio:
Eynav Mass is the VP R&D at Oribi. She is a fan of the combination of technology and people – bringing technical visions into implementation. For the past 2 years, she’s been leading the engineering of Oribi, a big-data based product, where they handle billions of events a day, while scaling both the system and the R&D group. Her main focus is to create processes that support high scale & high standards; striving to maintain scalable R&D groups and infrastructure environments, while investing in personal growth and deployments quality.

Title: Why do the majority of Data Science projects never make it to production?

Abstract:
While most companies understand the value creation of leveraging data and are taking on board an AI strategy, only 13% of the data science projects make it to production successfully. Besides the well-known skills gap in the market, we need to level up our end-to-end approach and cover all aspects involved when working with AI. In this session, we will discuss the main obstacles to overcome and how we can avoid the major pitfalls to ensure our data science journey becomes successful.

Bio:
Maria currently leads the Solutions Architect Team for MEA at Databricks, helping data teams solve the world’s toughest problems. She has published several research papers on ANN, contributing to applying AI to biomedical research, turning her AI interest into her career path. Maria is very passionate about  democratizing Data & AI and adoption of a data-driven culture.

Click here to learn more.

Details

Date:
December 15
Time:
10:00 am - 11:30 am EST

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