惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

V
Visual Studio Blog
Stack Overflow Blog
Stack Overflow Blog
G
Google Developers Blog
Microsoft Azure Blog
Microsoft Azure Blog
Engineering at Meta
Engineering at Meta
L
LangChain Blog
T
The Blog of Author Tim Ferriss
J
Java Code Geeks
Y
Y Combinator Blog
月光博客
月光博客
雷峰网
雷峰网
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
F
Fortinet All Blogs
A
About on SuperTechFans
让小产品的独立变现更简单 - ezindie.com
让小产品的独立变现更简单 - ezindie.com
P
Proofpoint News Feed
小众软件
小众软件
H
Help Net Security
Last Week in AI
Last Week in AI
B
Blog RSS Feed
宝玉的分享
宝玉的分享
N
Netflix TechBlog - Medium
博客园 - 叶小钗
The GitHub Blog
The GitHub Blog

Snorkel AI

Building AI-Native Systems for Federal Infrastructure: A Conversation with Rezaur Rahman Code World Models and AutoHarness for LLM Agents Benchtalks #1: Alex Shaw (Terminal-Bench, Harbor) – Building the Benchmark Factory Building FinQA: An Open RL Environment for Financial Reasoning Agents How Tool Discipline Let a 4B Model Outsmart a 235B Giant on Financial Tasks Coding agents don’t need to be perfect, they need to recover Closing the Evaluation Gap in Agentic AI SlopCodeBench: Measuring Code Erosion as Agents Iterate Introducing the Snorkel Agentic Coding Benchmark 2026: The year of environments Part V: Future Direction and Emerging Trends in Rubric-Based AI Evaluation The self-critique paradox: Why AI verification fails where it’s needed most Chat With the Terminal-Bench Team | Snorkel AI Intelligence per watt: A new metric for AI’s future Terminal-Bench 2.0: Raising the bar for AI agent evaluation Snorkeling in RL environments Introducing SnorkelSpatial: A Benchmark for LLM Spatial Reasoning Scaling Trust: Rubrics in Snorkel's Quality Process Evaluating Multi-Agent Systems in Enterprise Tool Use Evaluating Coding Agents with Terminal-Bench 2.0 Parsing isn’t neutral: why evaluation choices matter The science of rubric design The right tool for the job: An A-Z of rubrics Data quality and rubrics: how to build trust in your models Building the benchmark: inside our agentic insurance underwriting dataset Evaluating AI agents for insurance underwriting LLM observability: key practices, tools, and challenges Anthropic Claude + AWS: revolutionizing pharma data analytics with Snorkel AI Data-centric development of an enterprise AI agent with Snorkel Building the data development platform for specialized AI
Snorkel Flow 2024.R3: Supercharge your AI development wit...
Matthew Casey · 2024-10-09 · via Snorkel AI

Snorkel AI has made building production-ready, high-value enterprise AI applications faster and easier than ever. The 2024.R3 update to our Snorkel Flow AI data development platform streamlines data-centric workflows, from easier-than-ever generative AI evaluation to multi-schema annotation.

Let’s dive in.

Revolutionizing generative AI development

One of the biggest highlights of R3 is the introduction of Snorkel’s GenAI Evaluation Suite. This suite tackles a major challenge in Generative AI development: ensuring your models are ready for real-world use.

Here’s how the GenAI Evaluation Suite empowers you:

  • Specialized and flexible evaluation: Go beyond subjective assessments or off-the-shelf benchmarks. Define custom acceptance criteria and leverage ground truth data alongside automatic response evaluators to measure your model’s performance against use case and domain-specific requirements.
  • Fine-grained analysis: Snorkel Flow allows you to programmatically slice your data to focus data development on critical subsets. Tag data according to specific topics, languages, or customer scenarios.
  • Actionable insights: Snorkel doesn’t just identify errors; it empowers you to fix them. Evaluation dashboards provide clear insights, allowing you to seamlessly transition from evaluation to data development workflows within the platform.

The GenAI Evaluation Suite complements our comprehensive LLM fine-tuning workflow. This workflow guides users through five distinct steps, from connecting to your large language model inference provider (such as Amazon SageMaker or Databricks Mosaic AI) to curating high-quality training data.

2024.R3 also brings exciting new features such as:

  • Freeform LLM prompting: Safely connect to your LLM provider and leverage freeform prompting.
  • Synthetic data generation: Address data sparsity by leveraging synthetic data generation techniques directly within the SDK.
  • Enhanced LLM provider integrations: We’ve improved logging and performance with major LLM providers to ensure a smoother development experience.

Learn more about new GenAI and LLM features in 2024.R3.

Enhanced NLP workflows in Snorkel Flow 2024.R3

Snorkel Flow’s 2024.R3 release introduces significant advancements in natural language processing (NLP) capabilities, designed to streamline workflows and enhance the ability to extract insights from unstructured and structured text.

The new release includes the following:

  • Named entity recognition (NER) for PDFs (beta): Extract key information directly from your PDFs—including scanned documents. Snorkel Flow now supports word-based NER, bounding boxes, and pattern-based labeling functions, making it easier to capture the structure of your documents and enhance model performance.
  • Improved annotation suite: We’ve upgraded the annotation suite with multi-schema support for PDFs, annotation instructions, and a “Highlight-to-Label” feature for faster sequence tagging tasks.
  • Spotlight mode for focused debugging: Spotlight mode highlights and isolates incorrectly predicted entities, allowing you to identify and resolve errors faster.
  • Greater visibility of class-level metrics: See how your model performs on an entity-by-entity basis.

Learn more about the new NLP and PDF features in 2024.R3.

Strengthened and expanded enterprise-readiness features

At the beginning of the year, we introduced our first wave of role-based access control features, and we’ve built upon them in this release.

Snorkel Flow administrators can now keep their data safer than ever with:

  • Feature access control: Grant granular access to specific features, ensuring data security and scalability within your organization.
  • Audit trails and support bundles: Improved audit coverage and more robust support bundle exports enhance data privacy and system stability.

Learn more about our substantial data compliance and security upgrades here.

A streamlined user experience

Snorkel’s engineers have labored to make using Snorkel Flow a more delightful experience with the 2024.R3 release.

The platform boasts a revamped user interface (UI) with improvements to:

  • Labeling function (LF) table and suggestions: Navigate labeling functions more intuitively for faster data labeling.
  • Homepage and sidebar navigation: Enhanced navigation and organization streamline workflows and improve efficiency.

Ready to take your AI projects to the next level?

Snorkel Flow’s 2024.R3 release offers a comprehensive suite of tools to accelerate your AI development journey. From specialized GenAI evaluation to enhanced NLP workflows and a user-friendly interface, R3 empowers you to build robust, production-ready models faster.

Ready to accelerate AI development?

Deploy production AI and ML applications 10-100x faster with Snorkel’s experts, using our proprietary technology.

Request a demo