Snorkel AI

APRIL 24, 2024

Snorkel AI is excited to announce several new features released in Snorkel Flow, the latest version of our programmatic AI data development platform. It enables users to utilize the latest FM models for immediate, intuitive PDF labeling and analysis, streamlining the extraction of valuable insights from complex documents.

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LLM Computer Vision Large Language Models Machine Learning 52

Snorkel AI

APRIL 11, 2024

Combining our programmatic data development platform, Snorkel Flow, with hands-on support from our team of AI experts, Snorkel Custom engagements start with co-development of custom, use-case specific evaluation benchmarks, and end with a production quality LLM tuned on your unique data, and optimized for your unique use case.

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LLM Chatbots AI Developer AI Development 52

Snorkel AI

JANUARY 24, 2024

Snorkel AI has long championed the idea that AI teams can get better results faster by replacing highly manual data annotation with programmatic approaches that more efficiently capture and apply subject matter expertise. With programmatic alignment, we use a hybrid approach aimed at getting the best of both worlds.

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Snorkel AI

APRIL 24, 2024

In addition to easing the transition from prompting to programmatic labeling, this release also takes advantage of Snorkel Flow’s existing document intelligence features, such as integrated document preprocessing. The new FM-first workflow makes it easy to start labeling data at scale with a prompt.

Data Scientist 52

Data Scientist Large Language Models Explainability Machine Learning 52

Snorkel AI

APRIL 24, 2024

In addition to easing the transition from prompting to programmatic labeling, this release also takes advantage of Snorkel Flow’s existing document intelligence features, such as integrated document preprocessing. The new FM-first workflow makes it easy to start labeling data at scale with a prompt.

Data Scientist 52

Data Scientist Large Language Models Explainability Machine Learning 52

Snorkel AI

SEPTEMBER 29, 2023

Data labeling remains a core requirement for any organization looking to use machine learning to solve tangible business problems, especially with the increased development and adoption of LLMs. This is where data labeling fits in. What is data labeling? This approach applies across all data modalities.

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Large Language Models Machine Learning Data Science Data Scientist 59

Snorkel AI

JANUARY 24, 2024

Snorkel AI has long championed the idea that AI teams can get better results faster by replacing highly manual data annotation with programmatic approaches that more efficiently capture and apply subject matter expertise. With programmatic alignment, we use a hybrid approach aimed at getting the best of both worlds.

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LLM AI AI 52

Snorkel AI

OCTOBER 31, 2023

Labeling data by hand demands a lot of time and focus. Asking internal subject matter experts to label data imposes significant opportunity costs. To do so, data scientists prompt the large model to classify unlabeled data and use those labels as training targets. We then used those labels to train a RoBERTa-based model.

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Data Scientist Large Language Models LLM Machine Learning 72

Snorkel AI

FEBRUARY 9, 2024

The importance of data (and programmatic data development) Data is the new frontier in AI. A lack of properly prepared data presents the biggest blocker for enterprise AI projects, and programmatic data development presents the most practical answer to clearing this bottleneck.

AI Developer 52

AI Developer AI Development LLM Data Scientist 52

Snorkel AI

FEBRUARY 9, 2024

The importance of data (and programmatic data development) Data is the new frontier in AI. A lack of properly prepared data presents the biggest blocker for enterprise AI projects, and programmatic data development presents the most practical answer to clearing this bottleneck.

AI Developer 52

AI Developer AI Development LLM Data Scientist 52

Snorkel AI

OCTOBER 23, 2023

However, supplier-provided tags can be incomplete and inconsistent, and manually labeling products is time-intensive and difficult to scale. With over 10,000 product tags across 40 million products, creating and managing labeled data is an enormous and time-consuming effort. Manually labeling training data was prohibitively slow.

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Automation Computer Vision Machine Learning Data Scientist 64

Snorkel AI

SEPTEMBER 26, 2023

AI-powered surveillance systems require large amounts of data to train and operate, which can be difficult and expensive to obtain and label. By combining labeling schemas with human expertise, users can rapidly transform large unlabeled datasets into accurate and useful training sets. Interpretability. Use case customization.

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Snorkel AI

OCTOBER 23, 2023

However, supplier-provided tags can be incomplete and inconsistent, and manually labeling products is time-intensive and difficult to scale. With over 10,000 product tags across 40 million products, creating and managing labeled data is an enormous and time-consuming effort. Manually labeling training data was prohibitively slow.

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Automation Computer Vision Machine Learning Data Scientist 59

Snorkel AI

JULY 14, 2023

classification, information extraction) using programmatic labeling, fine-tuning, and distillation. This new release eases the process of creating new applications in Snorkel Flow and tightens the iteration loop with new tools to sort, edit and understand labeling functions. It’s all about giving you more control!

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Auto-classification Machine Learning Data Platform LLM 52

Snorkel AI

JULY 14, 2023

classification, information extraction) using programmatic labeling, fine-tuning, and distillation. This new release eases the process of creating new applications in Snorkel Flow and tightens the iteration loop with new tools to sort, edit and understand labeling functions. It’s all about giving you more control!

Auto-classification 52

Auto-classification Machine Learning Data Platform LLM 52

Snorkel AI

SEPTEMBER 26, 2023

AI-powered surveillance systems require large amounts of data to train and operate, which can be difficult and expensive to obtain and label. By combining labeling schemas with human expertise, users can rapidly transform large unlabeled datasets into accurate and useful training sets. Interpretability. Use case customization.

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Natural Language Processing AI AI Large Language Models 59

Machine Learning Research at Apple

OCTOBER 8, 2023

Currently, most models rely on datasets that are collected and labeled by human crowd-workers, a process that is costly and surprisingly error-prone for certain tasks. a view hierarchy), but one way to know for certain is to programmatically tap the UI element and…

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Metadata Machine Learning Automation 59

Snorkel AI

NOVEMBER 16, 2023

The Snorkel Flow AI data development platform enables you to leverage data-centric techniques and programmatic data development to adapt LLMs to your needs. Our data-centric tools allow users to create an iterative, programmatic loop. Then, through programmatic labeling and the iteration loop, you train your chosen model from there.

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LLM Large Language Models ML NLP 59

Snorkel AI

MAY 1, 2024

The alternative: programmatic data development with Snorkel Flow Snorkel takes a data-centric approach to complex document and image classification challenges. Snorkel Flow enables machine learning teams to collaborate with internal experts to develop data operators that perform labeling, filtering, and sampling operations as code functions.

AI Developer 52

AI Developer AI Development Machine Learning AI 52

Snorkel AI

JULY 19, 2023

We used weak supervision to programmatically curate instruction tuning data for open-source LLMs. Because this data curation was performed programmatically versus manually, this was accomplished by two developers in a day , instead of hundreds of annotators over the course of weeks or months of labeling.

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Categorization ChatGPT Large Language Models Generative AI 52

Snorkel AI

JULY 19, 2023

We used weak supervision to programmatically curate instruction tuning data for open-source LLMs. Because this data curation was performed programmatically versus manually, this was accomplished by two developers in a day , instead of hundreds of annotators over the course of weeks or months of labeling.

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Categorization ChatGPT Large Language Models Generative AI 52

AWS Machine Learning Blog

NOVEMBER 6, 2023

Adapters can be created via the console or programmatically via the API. This post details the console experience; however, if you’d like to programmatically create the adapter, refer to the code samples in the custom-queries-checks-blog.ipynb Jupyter notebook (Option 2). What is the account name/payer/drawer name? What is the memo?

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Auto-complete Data Extraction ML Machine Learning 100

Snorkel AI

NOVEMBER 16, 2023

The Snorkel Flow AI data development platform enables you to leverage data-centric techniques and programmatic data development to adapt LLMs to your needs. Our data-centric tools allow users to create an iterative, programmatic loop. Then, through programmatic labeling and the iteration loop, you train your chosen model from there.

LLM 52

LLM Large Language Models ML NLP 52

Flipboard

JULY 3, 2023

The second category of options turns to outside sources of data, such as hiring human reviewers to verify the answer or using huge amounts of labeled data to create assessment models. They were motivated by earlier efforts in programmatic supervision and the wealth of Pareto optimization research.

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LLM Large Language Models AI Tools NLP 104

Snorkel AI

JANUARY 31, 2024

At our recent Enterprise LLM Summit, I gave a talk entitled “Programmatically Scale Human Preferences and Alignment in GenAI” that focussed on how to align the output of large language models to enterprise users’ expectations. Historically, the labeling process made it prohibitively expensive to involve SMEs in aligning model performance.

LLM 52

LLM Data Scientist Large Language Models AI 52

Snorkel AI

JANUARY 31, 2024

At our recent Enterprise LLM Summit, I gave a talk entitled “Programmatically Scale Human Preferences and Alignment in GenAI” that focussed on how to align the output of large language models to enterprise users’ expectations. Historically, the labeling process made it prohibitively expensive to involve SMEs in aligning model performance.

LLM 52

LLM Data Scientist Large Language Models AI 52

Snorkel AI

MARCH 24, 2023

Fries, et al This paper finds that only 13% of biomedical datasets are available via programmatic access and 30% lack documentation on licensing and permitted reuse, highlighting the dataset debt in biomedical NLP. A Survey on Programmatic Weak Supervision J. Binary Classification with Positive Labeling Sources J. Roberts, X.

NLP 52

NLP Natural Language Processing Machine Learning Automation 52

Snorkel AI

MARCH 24, 2023

Fries, et al This paper finds that only 13% of biomedical datasets are available via programmatic access and 30% lack documentation on licensing and permitted reuse, highlighting the dataset debt in biomedical NLP. A Survey on Programmatic Weak Supervision J. Binary Classification with Positive Labeling Sources J. Roberts, X.

NLP 52

NLP Natural Language Processing Machine Learning Automation 52

Snorkel AI

DECEMBER 12, 2023

Snorkel’s programmatic approach to labeling data allows a typical team to process and annotate as many conversations as a team ten times its size, and address a long-tail of hundreds of possible intents or rapidly changing responses. A quick overview of the workflow is as follows. Typically data is exported in a comma delimited *.csv

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Chatbots AI AI Machine Learning 64

Snorkel AI

APRIL 19, 2023

Uniphore , a conversational AI and automation leader, has chosen Snorkel’s data-centric AI platform to scale data labeling and acclerate ML model development. In 2022 we started looking for solutions that radically accelerate data labeling and model development to improve Time to Value (TTV) for new customers, a company-wide initiative.”

Conversational AI 52

Conversational AI Data Scientist Natural Language Processing AI 52

Snorkel AI

APRIL 19, 2023

Uniphore , a conversational AI and automation leader, has chosen Snorkel’s data-centric AI platform to scale data labeling and acclerate ML model development. In 2022 we started looking for solutions that radically accelerate data labeling and model development to improve Time to Value (TTV) for new customers, a company-wide initiative.”

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Conversational AI Data Scientist Natural Language Processing AI 52

Snorkel AI

SEPTEMBER 21, 2023

However, manually labeling and analyzing data at any stage of a clinical trial is time-consuming, labor-intensive, and difficult to scale. For clinical trials, LLMs customized with Snorkel Flow can: Provide an initial source of label signal. With Snorkel Flow, trial clinicians can: Quickly and programmatically label training sets.

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BERT Data Scientist AI AI 64

Snorkel AI

OCTOBER 4, 2023

Credit-scoring AI tools require high-quality representative data to learn from, but manually labeling training data is slow, expensive, and can easily bottleneck AI development. Lenders and credit agencies can use Snorkel to: Quickly and programmatically develop training data for credit scoring models.

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AI AI Data Scientist Neural Network 64

Snorkel AI

DECEMBER 12, 2023

Snorkel’s programmatic approach to labeling data allows a typical team to process and annotate as many conversations as a team ten times its size, and address a long-tail of hundreds of possible intents or rapidly changing responses. A quick overview of the workflow is as follows. Typically data is exported in a comma delimited *.csv

Chatbots 59

Chatbots AI AI Machine Learning 59

Snorkel AI

SEPTEMBER 21, 2023

However, manually labeling and analyzing data at any stage of a clinical trial is time-consuming, labor-intensive, and difficult to scale. For clinical trials, LLMs customized with Snorkel Flow can: Provide an initial source of label signal. With Snorkel Flow, trial clinicians can: Quickly and programmatically label training sets.

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BERT Data Scientist AI AI 59

Snorkel AI

AUGUST 24, 2023

Data quality is essential for the success of any AI project but banks are often limited in their ability to find or label sufficient data. Banks cannot send their sensitive customer data to crowd labelers or to third-party models without compromising security. Model explainability.

Large Language Models 52

Large Language Models AI AI Natural Language Processing 52

Snorkel AI

AUGUST 24, 2023

Data quality is essential for the success of any AI project but banks are often limited in their ability to find or label sufficient data. Banks cannot send their sensitive customer data to crowd labelers or to third-party models without compromising security. Model explainability.

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Large Language Models AI AI Natural Language Processing 52