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With Mohammed Ridwanul (Product Lead, Dessa) & George Wang (Strategic Partnerships, Dessa).
Wednesday, September 25, 2019 at 06:30 PM   Absolutely Free
APAX Digital, 601 Lexington Ave, 53rd Fl
 
     
 
 
              

          
 
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EVENT DETAILS
There is no doubt that Machine Learning has already started to revolutionize the way we leverage data. With a new use case being broadcast over the news almost daily, organizations are beginning to recognize the value AI brings. However, with these new opportunities come new challenges. Machine Learning requires an entirely new workflow & approach to data science:

How do you maintain models after you put them into production? How do you handle concept drift or data skews?
How do you coordinate development between multiple data scientists using expensive resources on your infrastructure?
How do you modernize your IT infrastructure with the right tools for your scientists & engineers to maximize business impact?

The questions above raise the need for a machine learning platform that works for your team & addresses their needs. Even with so many ML platforms available, the question still remains: do you build ML platforms yourself? Or do you buy and/or partner with industry ML platform experts?
In this talk, we will help you understand a few of the major factors to consider when thinking about building your own in-house solutions. We will share our experience working with Fortune 100 organizations & give you insights on the efforts they have undertaken so it is easier for you to make your decision.

Agenda:

6:30 PM - VIP Guest Arrival + Networking
7:00 PM - Presentation & Q+A
7:30 PM - Networking
8:00 PM - Closing Remarks


Speaker:
Mohammed Ridwanul, Product Lead @ Dessa
Host:
George Wang, Strategic Partnerships @ Dessa

Special thanks to our friends at APAX Digital for sharing their beautiful space for this event!


If you would like to test drive our platform before this event, we invite you to try our free self-serve demos here:

Atlas: To scale ML development & Put models into production faster.
Orbit: To manage ML models after production to ensure models continue generating value.
 
 
 
 
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