Understanding the path of data – AI Governance

Understanding the path of data Modern organizations grapple with an avalanche of data from diverse origins. Grasping where this data originates, as well as its consumption patterns, is crucial for assuring its quality and reliability. This is where data lineage emerges as a potent instrument, enabling a clearer overview of data within organizations. Essentially, data […]

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Troubleshooting and analysis 2 – AI Governance

We can then see the visual interpretation (Figure 14.1) of which tokens impact the final decision the most: # Sample tweet to explaintweet = “I love using the new feature!So helpful.”# Generate the explanationexp = explainer.explain_instance(tweet, predictor, num_features=5, top_labels=3)exp.show_in_notebook() In this case, num_features determines how many features the explainer should use to describe the prediction. […]

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Troubleshooting and analysis – AI Governance

Troubleshooting and analysis Regardless of stringent checks, data processes can falter. Here, data lineage shines by assisting teams in pinpointing the origin of errors in their systems, whether in data pipelines, applications, or models. Such pinpoint accuracy drastically trims down the debugging duration, translating to immense time and effort savings. In summary, the systematic mapping […]

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The many facets of ML development – AI Governance

The many facets of ML development As we’ve already touched on in our ML chapters, there are several dimensions to consider when thinking about ML at any level. Let’s recap some of the aspects of ML development as they relate to data stewardship: Beyond training – model deployment and monitoring Ensuring model accuracy doesn’t end […]

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Transformative architectural principles – AI Governance

Transformative architectural principles In order to maximize the benefits of governance, certain foundational principles must be integrated: Zooming in on architectural dimensions Let’s take another look at our five pillars of governance: Summary In our journey through the realms of data, ML, and architectural governance, we’ve underscored the paramount importance of a cohesive strategy for […]

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Introduction to the COMPAS dataset case study – Navigating Real-World Data Science Case Studies in Action

Introduction to the COMPAS dataset case study In the realm of machine learning, where data drives decision-making, the line between algorithmic precision and ethical fairness often blurs. The COMPAS dataset, a collection of criminal offenders screened in Broward County, Florida, during 2013-2014, serves as a poignant reminder of this intricate dance. While, on the surface, […]

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