Latest posts
- How NVIDIA Omniverse bolsters AI with synthetic data
- NVIDIA's Nyla Worker presented “Leveraging Synthetic Data to Train Perception Models Using NVIDIA Omniverse Replicator” in 2022. ...
- Google experts on practical paths to data-centricity in applied AI
- Google experts Abhishek Ratna and Robert Crowe discuss practical paths to data-centricity in applied AI at The Future of Data-Centric AI '22. ...
- How to build reusable data cleaning pipelines with scikit-learn
- State Farm senior data scientist Jason Goldfarb presented “Reusable Data Cleaning Pipelines in Python” at the Future of Data-Centric AI 2022. ...
- Arize AI on How to apply and use machine learning observability
- Jack Zhou, product manager at Arize, on “How to Apply Machine Learning Observability to Your ML System” from The Future of Data-Centric AI ...
- The future of large language models is faster and more robust
- Snorkel and affiliated academic labs have been hard at work reducing how computationally expensive large language models are. ...
- Claypot AI CEO on why you should deploy models the hard way
- Claypot AI CEO Chip Huyen presented “Platform for Real-Time Machine Learning” at Snorkel AI’s Future of Data-Centric AI 2022. ...
- McKinsey QuantumBlack on automating data quality remediation with AI
- Jacomo Corbo and Bryan Richardson with QuantumBlack present “Automating Data Quality Remediation With AI” at The Future of Data-Centric AI. ...
- Sambanova on using LLMs to squeeze value from business data
- Stefano Lindt presents “Leveraging NLP to Extract Value From Business Data” at Snorkel AI’s The Future of Data-Centric AI Summit in 2022. ...
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