Team

The Parlance team

Hamel Husain

Hamel Husain is a machine learning engineer with over 25 years of experience. He has worked with innovative companies such as Airbnb and GitHub, which included early LLM research used by OpenAI for code understanding. He has also led and contributed to numerous popular open-source machine-learning tools.

Jason Liu

Jason Liu is a distinguished machine learning consultant known for leading teams to successfully ship AI products. Jason’s technical expertise covers personalization algorithms, search optimization, synthetic data generation, and MLOps systems. His experience includes companies like Stitch Fix, where he created a recommendation framework and observability tools that handled 350 million daily requests. Additional roles have included Meta, NYU, and startups such as Limitless AI and Trunk Tools.

Jeremy Lewi

Jeremy Lewi is a Machine Learning platform engineer with over 15 years of experience and expertise in using AI to solve practical business applications. He has built platforms for YouTube, Google Cloud Platform, and Primer, enabling ML Engineers and data scientists to rapidly develop and deploy models into production. He played a pivotal role in developing systems like YouTube’s Video Recommendations and made major contributions to open-source software, including creating Kubeflow, one of the most popular OSS frameworks for ML.

Jeremy is an expert in Cloud services, Kubernetes, MLOps, LLMOps, CICD, IAC, and GitOps.

Dan Becker

Dan has been working in AI since 2012, when he finished 2nd (out of 1350 teams) in a machine learning competition with a $500,000 prize. He has contributed to open source AI tools like TensorFlow and Keras, worked as a data scientist at Google and lead the product team building AI Development Tools for DataRobot. Over 100,000 people have taken his deep learning courses on Kaggle and DataCamp.

Shreya Shankar

Shreya is a leading researcher on applied ML and AI systems. She has led extensive research on human-computer interaction for low-code tools to program complex LLM workflows including evals, monitoring and fine-tuning. Her research has been adopted widely and is incorporated into many commercial LLM tools such as Langsmith, Autoblocks, Parea, and more.

John Berryman

John has worked in technology since 2012. The first half of his career was spent building search applications. John helped build next-generation search for the U.S. Patent Office, built Eventbrite’s search and recommendation platforms, built GitHub’s code search, and co-authored a book – Relevant Search (Manning). While at GitHub, John moved into Data Science and then into Machine Learning with GitHub’s Copilot code completions and chat products. John is currently co-authoring an O’Reilly book for LLM application development.

Josh Patterson

Josh Patterson, with over 20 years in AI, has a rich history of contributions to the field. He played a key role in developing autonomous driving systems for DARPA Grand Challenge and optimizing mesh network routing with Ant Colony Optimization during his graduate studies. As a principal solutions architect and early employee at Cloudera, he significantly contributed to the company’s growth. Co-author of “Deep Learning: A Practitioner’s Approach” and “Kubeflow Operations Guide,” Josh is also a co-founder of the Eclipse Deeplearning4j project, demonstrating his expertise in generative neural networks. Currently, he focuses on Conversational AI, Automation AI, and the intersection of data and prompt engineering, driving advancements in generative AI technologies.

Zach Deane-Mayer

Zach Deane-Mayer is an AI executive and Kaggle Grandmaster with over 15 years of experience building AI products and infrastructure. He founded and scaled global AI teams that built Generative AI, Visual AI, and AutoML products at DataRobot. Zach has deep expertise in growing and scaling AI teams, building AI products, and developing AI strategies.