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Data science

Well, the best practice that I teach in the book is actually an analytic approach. 1
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The simplest thing you can do is just cohort your customers by how much they use the product. 1You'll always see your bottom cohort churn the most when you calculate the cohort churn rate. 1It's a kind of cohort analysis, and it's always very clear that the bottom cohorts churn the most, the top cohorts churn the least. 1
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JamesJalaPayneo
Mysql, HSQLDB, and SQLite are all small databases. 1These databases have low requirements on software and hardware environment and can run on the desktop, which businesspeople can learn and use theoretically. 1This paper takes MySQL as an example to discuss the characteristics of this programming language. 1
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JamesJalaPayneo
Developing AI programs comes with moral, ethical, and legal responsibilities. 1Ignoring ethical and legal liabilities can cost millions of any country's currency to any company, government institution, and individual. 1In this article, we will focus on the moral perspective of a dataset. 1
Step one: approach data audits as an exciting opportunity, not an unwanted responsibility. 1
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JamesJalaPayneo
For example, between November 2018 to April 2020, NVIDIA hasn't updated its line of consumer graphics (GeForce) cards at all. 1Intel on the other hand has updated its desktop lines twice. 1Another thing that ended up being pretty anti climatic was AMDs new line of consumer and server-level processors (I've seen this is a pretty delicate topic so more on this later). 1
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JamesJalaPayneo
There are indeed a lot of cool self-supervised tasks that one can devise when one deals with images, such as jigsaw puzzles [6], image colorization, image inpainting, or even unsupervised image synthesis. 1
But what happens when the time dimension comes into play? 1How can you approach the video-based tasks that you would like to solve? 1
Database users meet the most important aspect of applied machine learning, which is to understand what predictive questions are important and what data is relevant to answer those questions. 1
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AI-Tables differ from normal tables in that they can generate predictions upon being queried and returning such predictions as if it was data that existed in the table. 1

1. Volume

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2. Velocity

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3. Variety

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4. Veracity

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These Vs of Big Data may be the industry standard, but data scientists increasingly recognize a fifth even more important V: value. 1
During her IoT Tech Expo 2020 presentation, "Building Machine Learning Products -- a Best Practice Approach," Jenn Gamble, data science practice lead at Very, identified the required skills to implement machine learning with IoT and how teams can adopt best practices, approach software development and handle unexpected difficulties. 1
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JamesJalaPayneo

The Problem

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The factors that scale a typical venture backed company, say in SaaS, don't apply to AI. 1
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Flight to Data

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In the consumer world, behemoths like Google and Facebook are using their data with AI to build new services for consumers. 1Partly for SMBs, too, but largely the SMB world is woefully underserved by AI. 1