A Hybrid Approach to Customer Segmentation: Combining Machine Learning and Rules-Based Methodologies Author: Evie Fowler Customer segmentation refers to the process of dividing customers into subgroups with similar buying habits and needs. It helps businesses understand their customers better so that they can market existing products more effectively and even develop new products to meet […]
MLOps: Building Machine Learning Systems Author: Ani Madurkar The importance of thinking larger when designing effective and ethical machine learning systems MLOps is taking the data science and machine learning landscape by storm as organizations struggle to realize the value promised by their data. This is partly due to the difficulty in productionizing machine learning
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Author: Ani Madurkar Industry Data Science has diverged into a few dominant verticals as the field has matured, but the primary specialty has been Enterprise Machine Learning (with Product-centric Data Science as a close second). The realm of Enterprise Machine Learning, so far, has looked quite similar to how Kaggle looks — hacky ML notebooks
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From business managers, to data scientists, to UX developers — anyone who works with data knows anomalies can be a chore to find and an even bigger chore to resolve. Incorrect or faulty data can cause a business to miss revenue opportunities or potentially make poor business decisions based on erroneous analysis. For organizations whose
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Artificial intelligence, or “AI”, is a buzzword that has been around for decades… but what does itreallymean? For most people, it can be hard to differentiate between the futuristic sounding AI and its less glamorous sounding counterpart, machine learning, sometimes called “ML”. Even Fulcrum’s Data Scientists agree, that while the average person interacts with some
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Business decision makers rarely crave more reams of data. They really want well-defined, actionable insights from data that they can evaluate and consider for future actions. They also want confidence that they aren’t missing something critical in the data, either because the data is not being analyzed thoroughly or correctly, or worse… because the data
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