Building an Enterprise-Grade NLP Pipeline for Financial Services using Pachyderm, Seldon, and FinBert
What will be covered…
1. Building an enterprise-grade NLP Pipeline for financial services using Pachyderm, Seldon, and FinBert.
You’ll go through a complete end-to-end sentiment analysis pipeline from scratch including automated data labeling, model training, visualization, and more.
2. Combine multiple data sources into one workflow.
Models need constant tweaking and improvement. For that, we need to build a pipeline that is aware of new data, how to process it automatically in the most efficient manner possible.
3. Combine new human-labeled data via LabelStudio.
Configure our workflow so that we can automatically handle newly labeled data. This will ensure that the model never drifts in terms of accuracy
4. Automatically train a new model.
Using newly labeled data, we’ll train a new version of the model automatically.
5. Use data lineage to perform basic model validation.
You’ll explore how Pachyderms unique data lineage capabilities provide documentation that verifies exactly which data was used, when the model was created, and more.
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See Pachyderm In Action
Watch a short 5-minute demo which outlines the product in action