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Fine-tuning adapts a pre-trained machine learning model to perform well on new tasks. Data scientists call these "foundation models." Foundation model providers train these enormous neural networks on millions of documents. This diversity of training data allows foundation models to perform many tasks that they were not specifically trained for—though at low accuracy.
Instead of starting from scratch, fine-tuning allows data scientists to build upon these foundations. They feed the models additional input and output examples to adapt them to specific business use cases.
This approach reduces the time and resources needed to deploy AI solutions while maintaining high performance.
All articles on Fine-Tuning
Data-centric Foundation Model Development: Bridging the gap between foundation models and enterprise AI
Introducing new capabilities for Data-centric Foundation Model Development in Snorkel Flow Powerful new large language or foundation models (FMs) like GPT-3, Stable Diffusion, BERT, and more have taken the AI space by storm, going viral—even beyond technical practitioners—thanks to incredible capabilities around text generation, image synthesis, and more. However, enterprises face fundamental barriers to using these foundation models on real,
November 17, 2022
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Better not bigger: How to get GPT-3 quality at 0.1% the cost
We created Data-centric Foundation Model Development to bridge the gaps between foundation models and enterprise AI. New Snorkel Flow capabilities (Foundation Model Fine-tuning, Warm Start, and Prompt Builder) give data science and machine learning teams the tools they need to effectively put foundation models (FMs) to use for performance-critical enterprise use cases. The need is clear: despite undeniable excitement about
November 17, 2022
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Building AI models for financial document processing best practices
Highlighting the best practices for building and deploying AI models for financial document processing applications AI has massive potential in the financial industry. Building AI models to automate information extraction, fraud detection, and compliance monitoring can provide efficient and faster responses and support repurposing domain experts’ labor to more meaningful tasks. Developing AI models is not just about having models
June 15, 2022
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Uncovering the unknowns of deep neural networks by Sharon Li
Learning about the challenges and opportunities behind deep neural networks In this talk, Assistant Professor in Computer Science Sharon Li shares some exciting work about uncovering the unknowns of deep neural networks. She also shares some exciting challenges and opportunities in this domain. If you would like to watch Sharon’s presentation, we have included it below, or you can find
June 8, 2022
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Using few-shot learning language models as weak supervision
Utilizing large language models as zero-shot and few-shot learners with Snorkel for better quality and more flexibility Large language models (LLMs) such as BERT, T5, GPT-3, and others are exceptional resources for applying general knowledge to your specific problem. Being able to frame a new task as a question for a language model (zero-shot learning), or showing it a few
May 3, 2022
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Algorithms that leverage data from other tasks with Chelsea Finn
The Future of Data-Centric AI Talk Series Background Chelsea Finn is an assistant professor of computer science and electrical engineering at Stanford University, whose research has been widely recognized, including in the New York Times and MIT Technology Review. In this talk, Chelsea talks about algorithms that use data from tasks you are interested in and data from other tasks.
March 31, 2022
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Epoxy: Using Semi-Supervised Learning to Augment Weak Supervision
Machine Learning Whiteboard (MLW) Open-source Series We launched the machine learning whiteboard series (MLW) was launched earlier this year as an open-invitation forum to brainstorm ideas and discuss the latest papers, techniques, and workflows in artificial intelligence. Everyone interested in learning about machine learning can participate in an informal and open environment. If you are interested in learning about ML,
December 16, 2021
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Artificial Intelligence (AI) Facts and Myths
ScienceTalks with Abigail See. Diving into the misconceptions of AI, the challenges of natural language generation (NLG), and the path to large-scale NLG deployment In this episode of Science Talks, Snorkel AI’s Braden Hancock chats with Abigail See, an expert natural language processing (NLP) researcher and educator from Stanford University. We discuss Abigail’s path into machine learning (ML), her previous
November 23, 2021
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Design Principles for Iteratively Building AI Applications
Enabling iterative development workflows with Snorkel Flow’s Application Studio. Consider this scenario— we’re AI engineers, and we’re building a social media monitoring application to track the sentiment of Fortune 500 company mentions in the news.
November 8, 2021
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Snorkel’s Journey to Data-Centric AI, with Chris Ré
The Future of Data-Centric AI Talk Series Background Snorkel co-founder Chris Ré is an associate professor of Computer Science at Stanford University and an award-winning researcher in data-based theory and machine learning. He has co-founded four companies based on his research in machine learning systems. Chris recently presented at the Future of Data-Centric AI virtual event in September, where he
November 3, 2021
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Forager: Rapid Data Exploration for Rapid Model Development
Machine Learning Whiteboard (MLW) Open-source Series We started our machine learning whiteboard (MLW) series earlier this year as an open-invite space to brainstorm ideas and discuss the latest papers, techniques, and workflows in the AI space. We emphasize an informal and open environment to everyone interested in learning about machine learning.In this episode, Fait Poms, a Ph.D. student at Stanford
October 14, 2021
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