This is the second part of our series on scaling ML teams! Read on if you’re a leader in an ML team that is starting to grow beyond 10 people and scale from one team to a collection of teams working on the ML pipeline. If you’re a smaller team or you haven’t seen our first post on scaling from 0–10 people, you may want to read that first.
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So you’re doing an ML project! Maybe you want to build an object detection system for a robotics application or you want to add a recommender system to your webapp. You’ll need a team to build and improve this ML system. In the beginning, this can be a single (very stressed) engineer hacking together an MVP, but it can evolve into an entire department with highly specialized teams and hundreds of people. At each stage of developing a model pipeline, you will encounter different problems that require different team structures to overcome.
Read MoreHave you ever bought any home appliance only to find that you already have one buried somewhere deep within the basement, just under a box of Christmas lights? It is nearly as annoying as collecting data without knowing what knowledge they hide. That’s why delivering data visualization tools supports all companies and teams, be that marketing, accounting, data science, or business development.
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AI deals in pandemic, AI startups outpace s the venture capital market in the first quarter of 2020, but the coronavirus pandemic is expected to have a severe impact.
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Business-friendly it may sound; the path to implementing artificial intelligence in business is not a smooth ride. There are some typical, here are the Top 6 challenges while implementing Artificial Intelligence.
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Artificial Intelligence is radically changing daily life as the market for AI technologies is flourishing. There is a significant race to acquire them. This article discusses Top 15 Hot AI technologies that one should know.
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In a mad rush to hire Data Scientists, most companies overlook three key roles, in the false assumption that it’s a matter of hiring enough data scientists. The question is Who will deliver strategically and what are the Data Science team needs?
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Artificial intelligence (AI) isn’t just about frameworks, data sources and pipelines—it’s also about people and Building your AI team. This includes executive leadership support and the right talent in key AI roles.
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Summary of nine creative ways how to fail with Artificial Intelligence. Focus on data science and machine learning startups, but the lessons on what to avoid can easily be applied to other industries.
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