Mastering data governance For countless organizations, data stands as a priceless treasure. Yet, it’s data governance that serves as the compass guiding one to extract the true worth of data. Envision data governance as a holistic amalgamation of principles, methodologies, and tools designed to oversee the entire life cycle of your data, ensuring it’s in […]
Data management: crafting the bedrock At the heart of a compelling data governance strategy lies proficient data management. This sphere encompasses the meticulous aggregation, fusion, orchestration, and retention of trustworthy datasets, acting as a catalyst for businesses to harness maximum value. With the contemporary business landscape rapidly evolving, an organization’s merit hinges significantly on its […]
Data integration – from collection to delivery Data, in its raw form, originates from a myriad of sources, demanding integration to unlock its full potential. The integration might unfurl in batch sequences or be streamed in real time, aiming for prompt insight derivation. But integration isn’t a standalone task—it demands orchestrated actions to curate, ingest, […]
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 […]
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. […]
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 […]
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 […]
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 […]
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, […]
Understanding the task/outlining success The core of our investigation is binary classification. Our mission can be encapsulated in the question: “Considering various attributes of an individual, can we predict the likelihood of them re-offending, both efficiently and impartially?” The notion of efficiency is straightforward. We have an arsenal of metrics such as accuracy, precision, and […]
