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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A Framework for Building Customer Segmentation Models Author: Yifei Zheng If someone asked you to describe your customer base or audience, how would you do it? Would you describe a generalized persona? Perhaps a list of demographic features? Or maybe you would list several interests that align with your core mission or brand. While this
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Author: Igor Pshenychny Machine Learning and mathematical rigor are tools at data scientists’ disposal. But we all know that having a better calculator won’t exactly result in better test scores, and having a more powerful computer won’t necessarily make you a better programmer. To develop a strategy that doesn’t underperform, a data scientist will need
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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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Author: Igor Pshenychny Jupyter Notebook has become the de facto standard for data analysis. An acronym of the three main coding languages it supports: Julia, Python, and R, Jupyter Notebook provides a user-friendly integrated development environment (IDE) and has been evolving over time to become a go-to tool for data scientists. Jupyter Notebook facilitates a
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While marketing in general is capable of bringing additional sales, effectiveness of any particular campaign, channel, touch point etc. may vary substantially. To evaluate performance, historically there have been two approaches – Marketing Mix Modeling (MMM) and Attribution modeling. Here we examine differences, advantages and disadvantages of both approaches. Marketing Mix Modeling: Cost and Sales
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Hiring in data science has become very competitive for both companies and professionals in recent years. Fueled by the Great Reshuffle, data science positions are in high demand which can greatly benefit new and seasoned data science professionals alike. But in order to get hired in such a competitive landscape, candidates need to be prepared
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The traditional data science team has undergone a period of drastic change in the past two years due to COVID-19 and the “Great Reshuffle” that has followed. This period has witnessed relocation of staff to remote locations, changing business models in response to the pandemic, and challenges in attracting and retaining talent. At a time
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The goal of predictive analytics is to combine past and current data to obtain actionable insights. Data-driven decision making has been proven time and time again as the best way ahead for any business, regardless of the sector. We wanted to know how ready the nation’s largest Financial, Healthcare, and Retail organizations are for an
During our recent industry survey of 50 of the nation’s best funded institutions, we discovered that even industry-leading organizations aren’t maximizing their data science capabilities when it comes to evaluating the impact of business decisions to the bottom line. Among top retail, banking, and healthcare organizations, we uncovered that: In addition to Fulcrum Analytics providing