The majority of the conversation around machine learning and big data pertains to well-structured and cleaned data sets. Unfortunately, that is just a small percentage of the information that is available, so the rest of the sources of knowledge in a company are housed in so-called “Dark Data” sets. In this episode Alex Ratner explains how the work that he and his fellow researchers are doing on Snorkel can be used to extract value by leveraging labeling functions written by domain experts to generate training sets for machine learning models. He also explains how this approach can be used to democratize machine learning by making it feasible for organizations with smaller data sets than those required by most tooling.
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- Your host is Tobias Macey and today I’m interviewing Alex Ratner about Snorkel and Dark Data
- How did you get involved in the area of data management?
- Can you start by sharing your definition of dark data and how Snorkel helps to extract value from it?
- What are some of the most challenging aspects of building labelling functions and what tools or techniques are available to verify their validity and effectiveness in producing accurate outcomes?
- Can you provide some examples of how Snorkel can be used to build useful models in production contexts for companies or problem domains where data collection is difficult to do at large scale?
- For someone who wants to use Snorkel, what are the steps involved in processing the source data and what tooling or systems are necessary to analyse the outputs for generating usable insights?
- How is Snorkel architected and how has the design evolved over its lifetime?
- What are some situations where Snorkel would be poorly suited for use?
- What are some of the most interesting applications of Snorkel that you are aware of?
- What are some of the other projects that you and your group are working on that interact with Snorkel?
- What are some of the features or improvements that you have planned for future releases of Snorkel?
- From your perspective, what is the biggest gap in the tooling or technology for data management today?
- Christopher Ré
- Dark Data
- Training Data
- National Library of Medicine
- Empirical Studies of Conflict
- Data Augmentation
- Generative Model
- Discriminative Model
- Weak Supervision