Sphere recently hosted a live talk with Alexandre Matton of Cohere on the topic of career opportunities in machine learning. In this post, we share a summary of the main portion of Matton’s talk, aimed at an audience of students, early ML career practitioners, and career transitioners. If you’re already an industry professional, you won’t want to miss Matton’s tips for small companies aiming to automate deployment and monitoring of ML models, which were elicited during the audience Q&A.
Learn how to apply multidimensional scatter plots and hexagonal binned plots to a synthetic tall dataset, NYC's Uber ride-sharing data, and the well-known Iris flower dataset.
Visualizing text helps analysts understand what is happening in a small set of related documents and clearly communicate your data to a targeted audience.
Every businesses must predict the future. Learn how to make intelligent forecasts informed by data at a larger scale than ever before with machine learning.
Companies that want to remain on the cutting edge of natural language processing will need employees who know NLP fundamentals. Which online course is right for you?
We've gathered information about online courses about causal inference so that you can determine which is best for you – and what to expect from each.
Deep learning has become an essential component of recommender systems, and anyone who wants to understand the latter must understand the former.
Search engines are more capable than ever of showing relevant results to a user's text-based queries. Doug Turnbull shares what's next in machine learning powered search.
If your data seems too good to be true, your data is probably wrong. Learn how to avoid Twyman’s Law with smarter experiments and improve your A/B testing understanding.
A message from Nick Rudder and Adrian Sarstedt about Sphere.