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

K
Kaspersky official blog
Engineering at Meta
Engineering at Meta
D
DataBreaches.Net
Stack Overflow Blog
Stack Overflow Blog
Microsoft Security Blog
Microsoft Security Blog
Y
Y Combinator Blog
B
Blog RSS Feed
GbyAI
GbyAI
P
Proofpoint News Feed
aimingoo的专栏
aimingoo的专栏
MyScale Blog
MyScale Blog
D
Docker
阮一峰的网络日志
阮一峰的网络日志
Cyber Security Advisories - MS-ISAC
Cyber Security Advisories - MS-ISAC
Recorded Future
Recorded Future
美团技术团队
The Register - Security
The Register - Security
V
Visual Studio Blog
H
Hackread – Cybersecurity News, Data Breaches, AI and More
T
Tailwind CSS Blog
爱范儿
爱范儿
freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More
T
The Blog of Author Tim Ferriss
博客园 - 司徒正美
量子位
B
Blog
F
Fortinet All Blogs
Martin Fowler
Martin Fowler
博客园 - 【当耐特】
MongoDB | Blog
MongoDB | Blog
A
About on SuperTechFans
I
InfoQ
钛媒体:引领未来商业与生活新知
钛媒体:引领未来商业与生活新知
有赞技术团队
有赞技术团队
雷峰网
雷峰网
大猫的无限游戏
大猫的无限游戏
J
Java Code Geeks
L
LangChain Blog
Latest news
Latest news
S
SegmentFault 最新的问题
Exploit-DB.com RSS Feed
Exploit-DB.com RSS Feed
Blog — PlanetScale
Blog — PlanetScale
Threat Intelligence Blog | Flashpoint
Threat Intelligence Blog | Flashpoint
Cisco Talos Blog
Cisco Talos Blog
F
Full Disclosure
C
Cisco Blogs
D
Darknet – Hacking Tools, Hacker News & Cyber Security
W
WeLiveSecurity
T
Tenable Blog
T
Tor Project 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 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 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 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
Role-based access controls in Snorkel Flow secure enterprise data
Daniel Xu · 2024-05-14 · via Snorkel AI

Our customers have made clear that they need role-based access controls, and we’ve listened. As a product lead at Snorkel, I’m thrilled to share that we’ve released a new feature in our 2024.R1 product update specifically focused on data upload access controls.

This development is a direct response to feedback from our enterprise customers who need greater control over what data enters and leaves their systems. We’re confident this new capability will significantly enhance our enterprise readiness.

Let’s take a look at how it works.

Data access controls: an enterprise necessity

Enterprises (especially banks and firms in other highly regulated industries) need to control what data leaves their organization and how. Administrators must gate and approve the egress of proprietary data to prevent conflicts with the company’s security guidelines and federal regulations. If they allow employees and teams to arbitrarily access data and bring it onto an external platform, they could quickly run into a problem.

This, of course, includes moving data from the enterprise environment into the Snorkel Flow AI data development platform.

The top-level dashboard for role-based access controls in Snorkel Flow

Snorkel’s response: role-based access controls

To address our customers’ data access control needs, we have begun developing and deploying new features that allow admins to restrict what types of data can be imported into Snorkel Flow. Admins can now disable or enable different data connectors, effectively locking down what data your users can import into the platform.

Administrators can configure ingress controls both by role and by workspace. For example, they can enable access for SQL for data annotators in one workspace, Snowflake for data scientists in another workspace, and lock down local file upload for all roles across all workspaces.

Editing a rule using Snorkel Flow's Role Based Access Control utilities.

Control down to the configuration level

These features allow administrators to further refine access controls. For each role, each workplace, and each data upload method, administrators can decide which roles can interact with connection configurations and how. For example, they can choose to only allow administrators and super administrators to add or edit configurations for the company’s SQL database. They can allow developers to view and use existing configurations without modifying them. Still others, like annotators, could be locked out of all data ingress entirely.

This multi-level configuration control allows specificity in managing data access, ensuring that only the right people access the right data, enhancing both security and operational efficiency.

These granular controls are a testament to Snorkel’s commitment to providing robust and flexible solutions for enterprise data management. We understand that every enterprise has unique needs and challenges, and we are dedicated to providing tools that can be tailored to meet these specific requirements.

Showing the results of controlling upload settings using Snorkel Flow's role-based access control tools.

Role-based access control impact on enterprise AI

These new features provide several benefits. First, it significantly improves data security by ensuring that only authorized users can import data into Snorkel Flow. Second, it ensures data integrity by controlling what data can come in and out of the system. Lastly, it can improve efficiency in workflow, by preventing Snorkel Flow users from stumbling down the wrong path with the wrong data.

This release is just the start. Over the next year or two, we plan to offer the ability to gate both data ingress and egress. We also plan to investigate improvements to feature access control. This will lead us to a world where admins can define their own custom roles that align closely with their active directory representation of their entitlements.

RBAC in Snorkel Flow: a step forward

Snorkel Flow’s new data upload access control features represent a significant step forward in enhancing our enterprise readiness. It gives our customers much-needed control over what data comes in and out of their systems, improving data security and integrity while also enhancing workflow efficiency.

We encourage all our customers to explore this new feature and see how it can benefit their operations. As always, if you have any questions or feedback, feel free to reach out to us. We’re excited to hear from you and look forward to continually improving Snorkel Flow to better serve your needs.

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