Fiddler at Plug and Play Expo

Fiddler Labs was selected to present at Plug and Play’s Fall Expo on October 24th, 2019. As part of their Fintech batch, we were very pleased to see so many attendees in the audience! From investors and corporations to startups innovating in the space.

Plug and Play is the ultimate innovation platform, bringing together the best startups, investors, and the world’s largest corporations. PnP’s fintech arm has partnered with over 60 corporations and allowed us to have access to a large number of partners.

Fiddler’s CEO, Krishna Gade, (pictured below) spoke about explainability and compliance for banks today deploying AI/ML models.

At Fiddler, our mission is to enable businesses of all sizes to unlock the AI black box and deliver trustworthy and responsible AI experiences. 

We had a great time meeting with people in the industry and spreading the word about explainability! 

Join us next week at TwiML Con

Fiddler will be at the very first TwiML conference next week on October 1 & 2! It’s a new conference hosted by the amazing folks at TwiML, and we can’t wait to explore and learn about the latest and greatest for AI in the enterprise.  

At Fiddler, our mission is to enable businesses to deliver trustworthy and responsible AI experiences by unlocking the AI black box. 

Where to find us

1) October 1, 11.20 -11.45am, Robertson 2

Session: Why and how to build Explainability into your ML workflow

Join our CEO & Founder, Krishna Gade to learn how Explainable AI is the best way for companies to deal with business risks associated with deploying AI – especially in regulation and compliance heavy industries. Krishna comes from a data and explainability background having led the team that built Explainable AI at Facebook.

2) October 1 & 2, Community Hall, Booth #6 (see our location on the map below)

Come chat with us about: 

  • Why it’s important to provide transparent, reliable and accountable AI experiences
  • Risks associated with lack of visibility into AI behavior
  • How to understand, manage, analyze & validate models using explainability 

Schedule a time to connect with us

If you’d like to set up a meeting beforehand, fill out this meeting form and we’ll be in touch to finalize dates & times. We’re excited to chat with you!

See you next week!

Fiddler at O’Reilly AI Conference Sept 11 & 12

The San Jose O’Reilly Artificial Intelligence conference is almost upon us. From top researchers and developers to CxOs innovating in AI, we’ll hear about the latest innovations in machine learning and AI. 

Fiddler’s very own Ankur Taly, Head of Data Science, will be speaking on September 12 on Explaining Machine Learning Models. Ankur is well-known for his contribution to developing and applying Integrated Gradients  — a new interpretability algorithm for Deep Neural Networks. He has a broad research background and has published in several areas including Computer Security and Machine Learning. We hope to see you at his session!

At Fiddler, our mission is to enable businesses of all sizes to unlock the AI black box and deliver trustworthy and responsible AI experiences. Come chat with us about: 

  • Risks associated with not having visibility into model outputs
  • Most innovative ways to understand, manage, and analyze your ML models
  • Importance of Explainable AI and providing transparent and reliable experiences to end users

Schedule a time to connect with us

If you’d like to set up a meeting beforehand, then fill out this meeting form and we’ll be in touch. We’re excited to chat with you!

Where to find us

September 11 & 12

We’ll be in the Innovator Pavilion: Booth #K10, so stop by and say hi! 

September 12

Join Ankur Taly, our Head of Data Science, at his session on Explaining Machine Learning Models – 2:35pm – 3:15pm, Sep 12 / LL21 A/B

As machine learning models get deployed to high stakes tasks like medical diagnosis, credit scoring, and fraud detection, an overarching question that arises is – why did the model make this prediction? This talk will discuss techniques for answering this question, and applications of the techniques in interpreting, debugging, and evaluating machine learning models.

See you next week!