Meetup
Baselines and Bites Meetup: MLOps for text and image models
With
and
Thursday, November 3, 2022
,
4:00 pm
PT
Fiddler HQ - 291 Lambert Ave, Palo Alto, 94306
Thank you for your interest in attending the event. This registration is now closed.
About this event
Natural Language Processing (NLP) and Computer Vision (CV) solutions and services have experienced unprecedented acceleration over the last few years with a projected growth of over 25% through this decade. However, monitoring and explainability of ML models with unstructured data remains a challenge, which prevents ML teams from fully realizing the benefits of operationalizing models.
Join us to hear about the growing importance of MLOps for complex, unstructured models. Experts from Hugging Face and Fiddler will share:
- Why MLOps is imperative throughout the ML lifecycle
- How the complexity in NLP and CV models affects MLOps
- Real-life use cases NLP and CV models solve
Date and location
Thursday November 3, 2022 @ 4:00 PM PT
Fiddler HQ - 291 Lambert Ave, Palo Alto (view map)
Schedule
4:00 PM – 4:30 PM: Networking
4:30 PM – 5:15 PM: Fireside Chat and Q&A
5:15 PM – 6:00 PM: Appetizers, Beer, and Networking
Speakers
Josh Rubin
Director of Data Science
at
Fiddler AI
Josh has been a Fiddler for three years and currently manages its Data Science team. During this time he developed a modular framework to extend explainability to complex model form-factors, such as those with multi-modal inputs. He previously applied deep learning to instrument calibration and signal processing problems in the biotech tools space after outgrowing a career as an experimental nuclear physicist.
Nazneen Rajani
Robustness Research Lead
at
Hugging Face
Nazneen is a Research Lead at Hugging Face, a startup with a mission to democratize ML, leading data-centric ML research which involves systematically analyzing, curating, and automatically annotating data. Before HF, she worked at Salesforce Research with Richard Socher and led a team of researchers focused on building robust natural language generation systems based on LLMs. She completed her Ph.D. in CS at UT-Austin with Prof. Ray Mooney.Nazneen has over 30 papers accepted at ACL, EMNLP, NAACL, NeurIPs, ICLR and has her research covered by Quanta magazine, VentureBeat, SiliconAngle, ZDNet, and Datanami. She is also teaching a course on interpreting ML models with Corise.
