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Deep Checks has developed an open source testing framework to ensure ML models behave as expected. This framework provides built-in checks for testing and validating model behavior and performance. It allows users to get started quickly and extend the framework to meet specific needs as models evolve. By automating testing, this framework helps accelerate machine learning projects and build trust in models.
Poor data quality is a major challenge in effective ML. Galileo offers a collaborative data bench for data scientists working with natural language processing models. It allows users to programmatically inspect, fix, and track data throughout the ML workflow. By offering tools to address data quality issues, Galileo helps improve model performance and reduce labeling and procurement costs.
Snorkel AI is a data-centric development platform for ML workflows powered by programmatic data labeling techniques. It enables users to write labeling functions to programmatically label data and automates the cleaning and combination of inputs. Snorkel AI also provides a full auto-ML suite for faster iteration and offers integrations with other platforms and tools. Its goal is to make ML development a structured, efficient, and adaptable process.
Snorkel flow utilizes labeling functions generated from imperfectly labeled data as a starting point for machine learning projects. It emphasizes the importance of incorporating organizational knowledge, domain knowledge, and various sources of information into the labeling process. This includes codified sources such as knowledge bases, rule sets, and business logic, as well as existing resources like pre-trained models and language models. The overarching idea is to leverage these resources to shape the dataset and power the models, rather than relying solely on manual data labeling.
Snorkel flow enables the use of programmatic data labeling and iterative development to improve efficiency and accuracy. It allows the adaptation and repurposing of existing models that might be undergoing concept drift, complementing them with custom labeling functions to adjust for real-world operation. By treating the development process like software development, Snorkel facilitates the editing, iteration, and development of training data. This approach saves time and costs, especially when dealing with unstructured, unlabeled data, and complex problems that require expertise in labeling. The platform also emphasizes the importance of involving subject matter experts and annotators in the loop, combining both programmatic and manual approaches for optimal results.
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