23:48
Knowledge Distillation Demystified: Techniques and Applications
09:05
Model Compression: Streamlining AI for Enterprises
57:02
Model Distillation: From Large Models to Efficient Enterprise Solutions
57:08
Curate training data via labeling functions— 10 to 100x faster
05:23
Databricks Integration: Streamlining Data Development with Snorkel Flow
16:40
AI Copilots for Businesses: Driving Innovation with Generative AI
19:25
How Numbers Station Combines LLM, RAG, and Domain Knowledge for AI-Driven Data Analysis
51:20
Revolutionizing Data Analysis: Combining LLM, RAG, and Domain Knowledge
40:49
Task Me Anything: Revolutionizing Multimodal Model Benchmarking
29:39
Alfred: An Open-Source Tool for Building Training Data with Foundation Models and Weak Supervision
11:36
Transforming Call Center Operations with Snorkel AI
04:58
How to Add/Remove/Manage Foundation Models (FMs) and Large Language Models (LLMs) in Snorkel Flow
01:57
How to Create Computer Vision Applications in Snorkel Flow
07:25
How to Upload Images and PDFs into Snorkel Flow
56:15
How to Optimize RAG Pipelines for Domain- and Enterprise-Specific Tasks
05:49
Three Ways to Evaluate LLMs
03:18
The Iterative LLM Development Loop in Snorkel Flow
02:00
How to activate Llama 3.1 405B in Snorkel Flow
24:35
LLM Evaluation for Production Enterprise Applications
20:09
DEMO: How to Evaluate Enterprise LLMs in Snorkel Flow
56:43
How to Evaluate LLM Performance for Domain-Specific Use Cases
03:55
Build Better LLMs Through Data Slicing
18:05
When, Why and How to Fine-Tune LLMs for Enterprise Applications
24:43
DEMO: How To Align LLMs for Enterprise Applications in Snorkel Flow
06:18
4 Ways to Align LLMs: RLHF, DPO, KTO, and ORPO
25:27
How New Research Extends Weak Supervision Beyond Classification Problems
51:28
How to Fine-Tune LLMs to Perform Specialized Tasks Accurately
08:03
Enterprise AI alignment: a pragmatic overview
03:51
What is AI alignment? A high-level overview in less than four minutes!
36:56
How to align LLMs to Enterprise Objectives and Policies
02:00
How Snorkel Flow Enables Safe and Responsible AI Development
40:49
Large Language Models Can Help Make Better Image Classifiers. Here's How.
52:26
How to Accelerate AI Training With Programmatic Data Labeling: Snorkel Flow Demo
21:42
RAG Optimization: A Practical Overview for Improving Retrieval Augmented Generation
14:44
Conquer RAG Retrieval Errors: 5-Step Tuning Process in Snorkel Flow
07:58
Beyond the Hype: Making RAG Work for Your AI Systems in Production
56:13
How to tune RAG implementations for specialized enterprise tasks
03:55
Understanding why and how to customize LLMs for specialized domains
03:55
How enterprise alignment makes AI safe and useful for businesses
04:11
AI Superalignment? First, we need to tackle enterprise alignment
02:37
#GPT-4o now available on Snorkel Flow: how we did that so fast!
46:34
Fine-tune large language models faster using Bonito to generate task-specific training data!
51:55
New in Snorkel Flow's 2024.R1 release: enhanced security, image categorization, and more!
02:43
Label image data faster and more scalably than ever in Snorkel Flow
02:25
Role-based access controls (RBAC) in Snorkel Flow protect enterprise data
05:24
See how role-based access controls in Snorkel Flow keep enterprise data secure
40:35
How Wayfair Transforms Customer Experiences with Data-Centric AI
10:43
Building a document intelligence AI application: live demo in Snorkel Flow
02:19
Document intelligence, foundation model first: easier than ever with Snorkel Flow
35:40
ROBOSHOT: better foundation model performance without fine-tuning (Stanford researcher presentation)
11:35
Direct Preference Optimization (DPO): How It Works and How It Topped an LLM Eval Leaderboard
12:26
64-point accuracy boost: how Snorkel AI built a time-saving LLM application for a top U.S. bank
11:31
AI for Content Filtering: From Zero To 96% Recall Model in 3 Days With Snorkel Flow
03:06
Fall in Love with Your Data: Transforming AI in Banking with BNY Mellon
03:34
Understand the basics of LLM training in under four minutes!
27:13
Banking’s AI Revolution: A Fireside Chat with BNY Mellon
27:03
Q&A Panel: Building GenAI with Your Data
21:38
How AI Impacts The Insurance Industry Today
29:30
How To Leverage Your Data to Unlock AI's True Potential
17:54
Why GenAI Needs Careful Training Data Management
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About
Snorkel AI pioneered data-centric AI, recognizing long before mainstream LLM availability, that enterprise AI success depends on the data used to train the model.
Snorkel built the world’s first AI data development platform, Snorkel Flow, so that enterprises can leverage their proprietary, unstructured data to create specialized AI models faster than ever before. Using Snorkel Flow, data scientists and subject matter experts collaborate on a shared platform, to iteratively develop training data at scale—allowing users to reach the point of model training up to 100X faster than traditional data development approaches.
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