Latest posts
- Debugging AI Applications Pipeline
- We’ll analyze major sources of errors during the four steps of building AI applications: data labeling, feature engineering, model training, and model evaluation. ... - How To Overcome Practical Challenges for AI in the Public Sector
- AI is already transforming the business of government. But the positive impacts of this transformation, from increasing the efficiency of public services to enhancing the effectiveness of tax dollars, are still in the earliest stages. Public sector organizations generally have access to the same talent, software models, and hardware infrastructure… ... - How To Overcome Practical Challenges for AI in Finance
- Advancements in artificial intelligence promise efficiency gains for financial institutions. AI-powered applications can revolutionize an organization’s risk management, fraud detection, compliance monitoring, and other processes. Financial services companies have smart data scientists and good infrastructure needed for deploying AI. But their ability to rapidly develop and deploy AI applications is… ... - Machine Learning Production Myths
- Takeaways from MLSys Seminars with Chip HuyenIn November, I had the opportunity to come back to Stanford to participate in MLSys Seminars, a series about Machine Learning Systems. It was great to see the growing interest of the academic community in building practical AI applications. Here is a recording of… ... - Meet a Snorkeler at an Upcoming Event
- We love meeting people in the data science and machine learning community. Here are a few upcoming events where you can meet Snorkelers. ... - How to Overcome Practical Challenges for AI in Healthcare
- There’s a lot of excitement about the potential for AI to improve healthcare. This is driven by compelling advances across a wide range of applications including drug discovery, radiology, pathology, electronic medical record (EMR) intelligence, clinical trials, and more. There are also many challenges for development and deployment of AI… ... - Every Vote Counts - Our mission at Snorkel AI is to make artificial intelligence practical with an end-to-end machine learning platform that focuses centrally on training data. Our technology has been used for incredibly important efforts – from solving medical challenges with Stanford Medicine to identifying prejudiced language in social media. We’re just getting… ...
- Snorkel AI Welcomes Devang Sachdev as Vice President of Marketing
- We are inventing a new way to build enterprise AI applications. Taking a data-centric approach, we are making machine learning iterable, faster to deploy, and ultimately more practical.That is a fantastic opportunity, but it also presents one of our biggest challenges - figuring out how to bridge the gap between… ...
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