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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 AI data development: a guide for data science projects SnorkelCon 2024: Inaugural Snorkel AI user conference gathers leaders from 30+ Fortune 500 companies Snorkel Flow 2024.R3: Supercharge your AI development with enhanced data-centric workflows 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 AI joins the AWS ISV Accelerate Program and launches Snorkel Flow Availability in AWS Marketplace
Matthew Casey · 2024-11-19 · via Snorkel AI

Enterprises are under pressure to demonstrate value from their AI investments, and success depends on the quality of the data used to train models. Raw datasets are rarely enough. Data must be curated—cleaned, labeled, and refined—to build and evaluate custom AI systems. This requires human expertise often with domain-specific knowledge. That’s why at Snorkel AI, we believe data is the true differentiator, a vision we share with Amazon Web Services (AWS).

In June, Snorkel and AWS signed a multi-year strategic collaboration agreement to help enterprises build, deploy, and evaluate custom, production-ready AI models. Today, we’re excited to announce a significant milestone in our partnership. After completing a comprehensive architectural and security review, we’ve joined the AWS Independent Software Vendor (ISV) Accelerate Program. Additionally, Snorkel Flow is now available in AWS Marketplace, allowing AWS customers to leverage our platform for use cases such as RAG optimization for legal contracts and LLM evaluations.

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AWS ISV Accelerate Program

Joining the AWS ISV Accelerate Program enables Snorkel AI to deliver more powerful, scalable AI solutions to AWS customers. This program provides us with enhanced technical support, co-selling opportunities, and deeper integration with AWS services, ensuring that we can bring Snorkel Flow to market more quickly and efficiently. This means faster, more reliable deployment for customers using our platform for complex AI and machine learning tasks.

For Snorkel, the program strengthens our collaboration with AWS sales and technical teams, ensuring customers rapidly realize value. By aligning technical and business efforts, we accelerate time-to-value, helping customers fully leverage Snorkel Flow to optimize workflows and drive impactful AI outcomes—all in AWS infrastructure.

Snorkel Flow availability in AWS Marketplace

For the first time, and in tandem with joining the ISV Accelerate Program, Snorkel Flow is officially available for purchase in AWS Marketplace

Snorkel Flow is the world’s first programmatic AI data development platform. It enables enterprise data science and AI teams to label and curate data by writing code, leading to faster data development for more specialized enterprise AI. It also includes enterprise-ready security and compliance features, such as SOC 2 Type II certification and HIPAA compliance for handling Protected Health Information (PHI).

If your organization participates in the AWS Enterprise Discount Program (EDP), you’re already benefiting from the savings and flexibility of your fixed-term contract. However, many organizations don’t fully utilize their committed spend, leaving money on the table.

By purchasing Snorkel AI through AWS Marketplace, you can apply 100% of your committed spend as credit toward Snorkel Flow. This allows you to accelerate machine learning initiatives, automate data labeling, and streamline model development—unlocking faster, more accurate insights while maximizing the value of your AWS investment.

Employ Snorkel Flow for Your Organization

Through the AWS ISV Accelerate Program and the availability of Snorkel Flow in AWS Marketplace, we’re able to offer AWS customers easier and faster ways to build AI applications tailored to their unique requirements, helping them uplevel their AI capabilities.

For more information, book a meeting at AWS re:Invent 2024 or a virtual demo to learn more about what Snorkel Flow can do for your organization.