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Inside recommendations: how a recommender system recommends
We describe types of recommender systems, more specifically, algorithms and methods for content-based systems, collaborative filtering, and hybrid systems.
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10 AI Project Ideas in Computer Vision
The field of computer vision has seen the development of very powerful applications leveraging machine learning. These projects will introduce you to these techniques and guide you to more advanced practice to gain a deeper appreciation for the sophistication now available.
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Deep Learning on your phone: PyTorch C++ API for use on Mobile Platforms
The PyTorch Deep Learning framework has a C++ API for use on mobile platforms. This article shows an end-to-end demo of how to write a simple C++ application with Deep Learning capabilities using the PyTorch C++ API such that the same code can be built for use on mobile platforms (both Android and iOS).
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What’s missing from self-serve BI and what we can do about it
The notion of self-service BI tools caught an expectation that they could provide a magic formula for easily helping everyone understand all the data. But, such an end-result isn't occurring in practice. To identify a better approach, we need to take a step back and determine what problem is actually trying to be solved.
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AI Infinite Training & Maintaining Loop
Productizing AI is an infrastructure orchestration problem. In planning your solution design, you should use continuous monitoring, retraining, and feedback to ensure stability and sustainability.
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 Design Patterns for Machine Learning Pipelines
ML pipeline design has undergone several evolutions in the past decade with advances in memory and processor performance, storage systems, and the increasing scale of data sets. We describe how these design patterns changed, what processes they went through, and their future direction.
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Top 5 Time Series Methods
Data that varies in time can offer powerful applications and use cases for data scientists to analyze. This overview considers the top techniques you can learn to understand and gain insight from time-series data.
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ETL and ELT: A Guide and Market Analysis
ETL and related techniques remain a powerful and foundational tool in the data industry. We explain what ETL is and how ETL and ELT processes have evolved over the years, with a close eye toward how third-generation ETL tools are about to disrupt standard data processing practices.
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Want to Join a Bank? Everything Data Scientists Need to Know About Working in Fintech
There is ample opportunity for data scientists in the financial services sector. The career experience can be very different, however, from similar roles at pure technology organizations. So, it's best to first consider if this industry is right for your interests, preferences for how you work, and long-term goals.
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How To Defeat The Machine Learning Engineer Impostor Syndrome
How many times have you taken yet another online course on machine learning or read yet another paper on a new emerging topic, to be up-to-date in this crazy fast-paced AI/ML world -- only to keep feeling like an ML engineer impostor? These three personal tips can help you overcome the classic (and common) impostor syndrome behind every emerging ML engineer who wants to be better at what you do.
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Design Patterns for Machine Learning Pipelines