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

Scott Helme
Scott Helme
Security Latest
Security Latest
T
Threat Research - Cisco Blogs
AWS News Blog
AWS News Blog
S
Securelist
Help Net Security
Help Net Security
T
Threatpost
C
Cybersecurity and Infrastructure Security Agency CISA
D
Docker
Simon Willison's Weblog
Simon Willison's Weblog
Microsoft Azure Blog
Microsoft Azure Blog
CTFtime.org: upcoming CTF events
CTFtime.org: upcoming CTF events
P
Privacy International News Feed
V
Vulnerabilities – Threatpost
I
Intezer
Spread Privacy
Spread Privacy
WordPress大学
WordPress大学
C
Cisco Blogs
有赞技术团队
有赞技术团队
G
Google Developers Blog
Blog — PlanetScale
Blog — PlanetScale
S
Schneier on Security
Know Your Adversary
Know Your Adversary
C
CERT Recently Published Vulnerability Notes
Y
Y Combinator Blog
S
SegmentFault 最新的问题
G
GRAHAM CLULEY
F
Fortinet All Blogs
N
Netflix TechBlog - Medium
L
LINUX DO - 热门话题
K
Kaspersky official blog
P
Proofpoint News Feed
P
Palo Alto Networks Blog
Cyberwarzone
Cyberwarzone
Threat Intelligence Blog | Flashpoint
Threat Intelligence Blog | Flashpoint
GbyAI
GbyAI
cs.CL updates on arXiv.org
cs.CL updates on arXiv.org
T
Tor Project blog
NISL@THU
NISL@THU
L
LangChain Blog
B
Blog
aimingoo的专栏
aimingoo的专栏
K
KPMG report finds enterprise disconnect between AI and its ROI | CIO
Cisco Talos Blog
Cisco Talos Blog
雷峰网
雷峰网
The Cloudflare Blog
宝玉的分享
宝玉的分享
SecWiki News
SecWiki News
L
Lohrmann on Cybersecurity
C
Cyber Attacks, Cyber Crime and Cyber Security

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 LLM-as-a-judge for enterprises: evaluate model alignment at scale Why GenAI evaluation requires SME-in-the-loop for validation and trust Research spotlight: is long chain-of-thought structure all that matters when it comes to LLM reasoning distillation? Why enterprise GenAI evaluation requires fine-grained metrics to be insightful What is specialized GenAI evaluation, and why is it so critical to enterprise AI? LLM alignment techniques: 4 post-training approaches Research spotlight: Is intent analysis the key to unlocking more accurate LLM question answering? Why enterprises should embrace LLM distillation Retrieval-augmented generation (RAG) failure modes and how to fix them What is large language model (LLM) alignment? Databricks + Snorkel Flow: integrated, streamlined AI development How LLM evaluation drives better models in Snorkel Flow Unlock proprietary data with Snorkel Flow and Amazon SageMaker LLM evaluation in enterprise applications: a new era in ML Snorkel AI joins the AWS ISV Accelerate Program and launches Snorkel Flow Availability in AWS Marketplace AI data development: a guide for data science projects SnorkelCon 2024: Inaugural Snorkel AI user conference gathers leaders from 30+ Fortune 500 companies Explore the new GenAI Evaluation Suite: Snorkel 2024.R3 New NLP features in Snorkel Flow 2024.R3 Enterprise data compliance and security review: Snorkel Flow 2024.R3 How a global financial services company built a specialized AI copilot accurate enough for production Task Me Anything: innovating multimodal model benchmarks Alfred: Data labeling with foundation models and weak supervision RAG: LLM performance boost with retrieval-augmented generation Call center AI for customer experience management: a case study New GenAI features, data annotation: Snorkel Flow 2024.R2 How data slices transform enterprise LLM evaluation Meta’s Llama 3.1 405B is the new Mr. Miyagi, now what? Meta’s new Llama 3.1 models are here! Are you ready for it? Data-centric AI with Snorkel and MinIO Weak supervision for non-categorical applications + superalignment Snorkel AI signs strategic collaboration agreement with AWS to help enterprises cross the demo-to-production chasm AI alignment made simple: innovative solutions for businesses How does the Snorkel Flow label model work? Vision language models: how LLMs boost image classification Long context models in the enterprise: benchmarks and beyond How to build production-grade RAG retrieval with Snorkel Flow How Bonito helps fine-tune specialized LLMs faster than ever Walking safely before building flying saucer seatbelts: introducing Enterprise Alignment Role-based access controls in Snorkel Flow secure enterprise data Accelerating AI development in manufacturing with Snorkel Flow and AWS SageMaker How ROBOSHOT boosts zero-shot foundation model performance Discover what’s new in Snorkel Flow: Flexible data and LLM connectivity, secure data controls, and more! Faster than ever document intelligence with new Snorkel Flow FM-first workflow The art of data development for Enterprise LLMs Crossing the demo-to-production chasm with Snorkel Custom How Snorkel topped the AlpacaEval leaderboard (and why we're not there anymore) CRFM's HELM and enterprise LLM evaluation beyond accuracy How we achieved 89% accuracy on contract question answering Five sessions not to miss at Google Cloud Next 24 Content filtering breakthrough: Snorkel client reaches 96% recall in 3 days Here's how Snorkel Flow + Google AI built an enterprise-ready model in a day Snorkel teams with Microsoft to showcase new AI research at NVIDIA GTC How Skill-it! enables faster, better LLM training Fine-tuned representation models boost LLM systems. Here's how Enterprise GenAI to surge in 2024: survey results Large language model training: how three training phases shape LLMs LoRA: Low-Rank Adaptation for LLMs LLM distillation demystified: a complete guide Enterprises must shift their focus from models to data in AI development Insurance’s GenAI revolution: a business perspective Scaling human preferences in AI: Snorkel's programmatic approach Building better enterprise AI: incorporating expert feedback in system development “Fall in love with your data”—Snorkel AI’s Enterprise LLM Summit Why QBE Ventures invested in Snorkel AI New benchmark results demonstrate value of Snorkel AI approach to LLM alignment Retrieval augmented generation (RAG): a conversation with its creator Snorkel Flow 2023.R4: enhanced UI + PDF and Databricks tools How Snorkel Flow users can register custom models to Databricks Stanford professor discusses exciting advances in foundation model evaluation
Snorkel Flow 2024.R3: Supercharge your AI development with enhanced data-centric workflows
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