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We’re thrilled to introduce the latest release of our data-centric AIdevelopment platform, Snorkel Flow. If you want to see Snorkel Flow in action, sign up for a demo. Schedule a demo with one of our Snorkel Flow experts. Autosuggest labeling functions enhancements. Advanced SDK tools. Annotation enhancements.
We are thrilled to announce the latest edition of Snorkel Flow, our platform to rapidly build, manage, and deploy predictive AI applications (e.g., classification, information extraction) using programmatic labeling, fine-tuning, and distillation. Here’s what we’ve rolled out: LF Filtering : Looking for a particular LF?
We are thrilled to announce the latest edition of Snorkel Flow, our platform to rapidly build, manage, and deploy predictive AI applications (e.g., classification, information extraction) using programmatic labeling, fine-tuning, and distillation. Here’s what we’ve rolled out: LF Filtering : Looking for a particular LF?
We are thrilled to announce the latest edition of Snorkel Flow, our platform to rapidly build, manage, and deploy predictive AI applications (e.g., classification, information extraction) using programmatic labeling, fine-tuning, and distillation. Here’s what we’ve rolled out: LF Filtering : Looking for a particular LF?
Simulation of consumption of queue up to drivers estimated position becomes an easy simple algorithm and results in wait time classification. They describe a visual programming platform for rapid and iterative development of end-to-end ML-based multimedia applications. They refer to this as our “demand” model.
We plan for multiple rounds of iteration to improve performance through error analysis, and the Snorkel Flow platform provides tools to enable this kind of iteration within the data-centric AI framework. Traditional, model-centric AIdevelopment focuses its iteration loop on the model itself. Auto-generated tag-based LFs.
We plan for multiple rounds of iteration to improve performance through error analysis, and the Snorkel Flow platform provides tools to enable this kind of iteration within the data-centric AI framework. Traditional, model-centric AIdevelopment focuses its iteration loop on the model itself. Auto-generated tag-based LFs.
But I want to at least give our perspective on what motivated us back in 2015 to start talking about this and to start studying it back at Stanford, where the Snorkel team started: this idea of a shift from model-centric to data-centric AIdevelopment. This could be something really simple. Often, this is internet or web data.
But I want to at least give our perspective on what motivated us back in 2015 to start talking about this and to start studying it back at Stanford, where the Snorkel team started: this idea of a shift from model-centric to data-centric AIdevelopment. This could be something really simple. Often, this is internet or web data.
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