10:37
Leveraging AWS and Snorkel Flow for AI Model Improvement
56:29
See how AWS and Snorkel Flow integrate to build valuable enterprise LLMs faster than ever
25:35
The future of enterprise AI: insights from leading researchers
40:25
AI business value: strategies for success in financial services
27:06
PDF extraction in business workflows: a guide
36:10
Customer success in AI: common pitfalls and how to avoid them
32:00
Graduating from data labeling to AI data development
29:50
How to optimize RAG for business applications (retrieval-augmented generation)
37:23
The essentials of evaluating LLM systems
43:50
Systems thinking in AI: the key to unlocking enterprise value
26:13
Data as the programming interface: the future of AI development
00:57
GenAI Evaluation: See How Snorkel Flow's New Tools Make GenAI Eval Easier and More Powerful
00:38
Easy PDF Annotation in Snorkel Flow—Just, Click, Drag and Choose Your Label
00:56
Label PDFs at Scale: Powerful Labeling Function Capabilities for Scalable PDF Annotation
00:49
Generative AI Annotation Features In Snorkel Flow
00:52
Annotate Training Data According to Multiple Label Schemas in One Pane
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
Integrating Structured and Unstructured Data for Better AI Solutions
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
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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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