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Data Science Fails – If It Looks Too Good To Be True… You've probably seen amazing AI news headlines such as: AI can predict earthquakes. Using just a single heartbeat, an AI achieved 100% accuracy predicting congestive heart failure. AI can diagnose covid19 in seconds from a chest scan. A new marketing model is promising to increase the response rate tenfold. It all seems too good to be true.

What if there was a better way? Machine Learning Operations (MLOps) will get your AI projects out of the lab and into production where they can generate value and help transform your business. In this installment of four Data Science Central Podcasts on MLOps, we explore best practices in Production Model Monitoring.

Getting it can be a challenge. A growing number of innovators are outsourcing data labeling operations so their teams can focus on strategy and innovation. Choosing a data labeling partner is an important decision that can affect your model performance and speed to market. But how do you choose the right data labeling vendor? Find all of the answers here!

Getting it can be a challenge. A growing number of innovators are outsourcing data labeling operations so their teams can focus on strategy and innovation. Choosing a data labeling partner is an important decision that can affect your model performance and speed to market. But how do you choose the right data labeling vendor? Find all of the answers here!

Editor's note: This post covers Favio's selections for the top 7 Python libraries of 2018. Tomorrow's post will cover his top 7 R packages of the year. If you follow me, you know that this year I started a series called Weekly Digest for Data Science and AI: Python & R, where I highlighted the best libraries, repos, packages, and tools that help us be better data scientists for all kinds of tasks. The great folks at Heartbeat sponsored a lot of these digests, and they asked me to create a list of the best of the best--those libraries that really changed or improved the way we worked this year (and beyond). Disclaimer: This list is based on the libraries and packages I reviewed in my personal newsletter.

One of the hardest tasks after creating your machine learning models is putting them into production. Data engineering takes care of this after the data scientist creates the models, but it's still not an easy process. Deploying machine learning data pipelines and algorithms should not be a time-consuming or difficult task. MLeap allows data scientists and engineers to deploy machine learning pipelines from Spark and Scikit-learn to a portable format and execution engine.

Ok so full disclosure, this library is like my baby. I've been working on it for a long time now, and I'm very happy to show you version 2. Optimus V2 was created to make data cleaning a breeze. The API was designed to be super easy to newcomers and very familiar for people that come from working with pandas. With Optimus you can clean your data, prepare it, analyze it, create profilers and plots, and perform machine learning and deep learning, all in a distributed fashion because on the back-end we have Spark, TensorFlow, and Keras. It's super easy to use, it's like the evolution of pandas, with a piece of dplyr, joined by Keras and Spark.

Have you secured your spot at AnacondaCON 2018? In addition to dozens of sessions on the latest in data science, machine learning, and AI, you'll learn how data scientists are using GPUs for machine learning across a variety of applications and industries. One lucky AnacondaCon 2018 attendee will receive a complimentary NVIDIA TITAN V GPU! The Duke Big Data & Data Science Certificate will arm you with the skills needed to be relevant in this booming industry! Study Machine Learning, Python and R, Tableau, Hadoop and Spark Developer at your own pace with live virtual classrooms and hands-on application in our cloud-based labs. Develop the comprehensive skillsets that are so in demand today.

Many of the popular articles can be found in our weekly digests. Our digests are archived here. The search box in the top right corner on any web page, can be used to find specific documents on our network, and in many cases, great articles that you won't find on Google. Here is a list of popular keywords (clickable links) to get you started. Popularity of an article is a score based on log(log(unique page views) because of the Zipf distribution of the raw page view counts: details are unimportant, what is important is that we turned it into a robust metric in the spreadsheet. The spreadsheet is available to our members only: click here, and check the last item in the bullet list.