Turn Data Chaos into AI Strategy with Programmatic AI Data Development
Discover how programmatic data development is revolutionizing enterprise AI specialization in this 27-minute talk by Elena Boiarskaia, Head of Applied Machine Learning at Snorkel AI. Learn about the transformation from disconnected, manual data tasks to…
Weak Supervision and Next-Gen AI-Based Threat Detection
Explore weak supervision for efficient AI-based threat detection in network security, leveraging analyst heuristics to build predictable models and overcome traditional machine learning challenges.
How AI Impacts the Insurance Industry Today
Explore AI's impact on insurance, addressing data challenges and LLM applications. Learn how Snorkel's approach enhances datasets and satisfies regulators in enterprise AI implementation.
Leveraging Data to Unlock AI's True Potential in Enterprise Projects
Unlock AI's potential through data development. Learn to fine-tune LLMs for domain-specific tasks, leverage expert knowledge, and accelerate high-quality dataset creation using Snorkel Flow.
How Data Transforms As It Moves Through An Enterprise
Explore data's journey across enterprises, unraveling its transformations and impact on decision-making, efficiency, and AI innovation through data-centric workflows.
How Data Can Close the Foundation Model Performance Gap
Explore data-centric development's role in bridging the gap between AI's power and its practical value for companies, focusing on Foundation Models.
How ML-Powered Data Cleaning Streamlines the AI Pipeline
Explore ML-powered data cleaning techniques to streamline AI pipelines, automate labor-intensive tasks, and overcome impediments in data preparation for efficient data science workflows.
Transforming Call Center Operations with AI - A Financial Institution Case Study
Explore how a major financial firm enhanced customer service using Snorkel Flow's AI solution. Learn about implementation challenges, impacts, and future plans for integrating all customer touchpoints.
Supercharging LLM Performance Without AI Training
Discover techniques to enhance large language model performance without additional training, focusing on innovative approaches and collaborative research findings.
Modeling Human Preference to Improve LLM Performance
Explore techniques for aligning language models with human preferences, including reward modeling and scalable tools to improve LLM performance with minimal end-user guidance.
Why GenAI Needs Careful Training Data Management
Explore the critical role of training data management in GenAI, including harmonization across stages and its impact on model effectiveness and safety in various domains and languages.
When, Why and How to Fine-Tune LLMs for Enterprise Applications
Explore techniques for customizing large language models in enterprise settings, including when and why to align LLMs and key methods for domain-specific adaptation.
How Numbers Station Combines LLM, RAG, and Domain Knowledge for AI-Driven Data Analysis
Leverage AI for comprehensive data analysis by combining structured and unstructured data, using multi-agent systems, and building AI-powered assistants for actionable insights.
How Ford Uses AI to Develop Better AI
Explore Ford's AI development lifecycle, focusing on data extraction, annotation, and augmentation for training. Learn about iteration's role in enhancing AI systems at Ford Motor Company.
Why Fixing Your Data Can Fix Your Model in Machine Learning
Explore how addressing data issues can improve machine learning models, focusing on cost-effective solutions and identifying crucial data problems to optimize resources.
How to Improve LLM Factual Accuracy and Reliability
Explore techniques for improving LLM factual accuracy, including DSP framework and MLflow LLMOps tools, to create reliable AI applications for production use.
Fine-Tuning and Customizing LLMs for Enterprise Tasks
Explore LLM fine-tuning techniques for enterprise-specific tasks. Learn about data quality, customization methods, and the trend towards task-specific models in organizational AI implementation.
Scaling Language Model Tuning with Snorkel AI - A Case Study
Explore Snorkel AI's innovative approach to scaling language model tuning using programmatic labeling and guiding models for efficient data curation and improved performance.
Digging Into RAG and the Future of AI - With Douwe Keila
Explore RAG applications, AI's enterprise future, and the role of data in improving AI systems with industry experts Alex Ratner and Douwe Keila.
LLM Evaluation for Production Enterprise Applications
Explore LLM evaluation for enterprise applications, covering metrics, approaches, and tools to ensure accuracy, compliance, and suitability for organizational needs.
Optimizing RAG Implementations for Specialized Enterprise Tasks
Optimize RAG implementations for enterprise tasks. Learn to fine-tune components, apply AI data development techniques, and accelerate production delivery for increased accuracy and precision.