Netflix often releases its internal tools to the public as open source code. The latest project to join the fray is Metaflow, a “deceptively simple” Python library for data scientists. Metaflow features integration with Amazon Web Services and includes a built-in capability to snapshot all code and data into Amazon Simple Storage Service.
The ML service Amazon CodeGuru has been released as a preview version. It provides automated code reviews—and is designed to help you find the most expensive bits of code and improve performance. Let’s see how that works and what features the new service offers.
This talk from the Machine Learning Conference gives a fun history of mining examples and presents some of the available tooling. Some of the topics we’ll be going over include embeddings, dynamic time warping, seriation, and HDBSCAN. Watch Vadim Markovtsev’s ML Conference session and come away knowing more about software development.
This down-to-earth machine learning talk from Daniel Molnar is for the underdog. What choices should you make in the vast world of machine learning and deep learning when there are so many options? Don’t base your choices on a gut feeling or product hype; use real world experience based on practical applications.
In this article, explore how a combination of artificial intelligence and machine learning can act as the brains of a smart city while simultaneously considering how a smart city experience can become more personalized without compromising the privacy of its residents. Read on to see what the advantages and disadvantages of an ML and AI-powered smart city are.
The machine learning platform TensorFlow, currently in version 2.0, is making its way toward the minor release 2.1.0: TensorFlow 2.1.0-rc0 is the first release candidate and includes some breaking changes. The upcoming version will be the last to support Python 2.7.
Chatbots are among the most popular applications of artificial intelligence, machine learning, and natural language processing, and many people are already familiar with them. Various companies are developing first prototypes to improve their customer communication and support functions. How do we begin to implement them into an industrial context?
Modern technology can help free yourself from data sampling. Current computing power has made scalability vastly and available and machine learning algorithms have made the discovery of data quality issues automated and easy. Move on from the old ways of data sampling and learn how to enter the new world of big, smart data.
As machine learning technologies become more prevalent, the risk of attacks continues to rise. Which types of attacks on ML systems exist, how do they work, and which is the most dangerous? ML Conference speaker David Glavas answered our questions.
Take a tour of ycrash in this article by Ram Lakshmanan. ycrash helps capture critical artifacts, including garbage collection logs, thread dumps, core dumps, heap dumps, disk usage, and more when the problem happens. It applies machine learning algorithms and generates a report which gives you a complete view of the problem, down to the lines of code that caused it.
Can AI play and complete a game? Juantomás Garcia Molina’s session from the Machine Learning Conference looks at developing artificial intelligence that can complete the first 3-D RPG, created in 1987. Many people had difficulty completing this technological wonder, so how will artificial intelligence fare?
Machine learning can help predict things dependent on time such as taxi demand. Time series forecasting has always been an important field in machine learning and statistics, as it helps us to make decisions about the future. A special field is spatio-temporal forecasting, where predictions are not only made on the temporal dimension, but also on a regional dimension.
In this session, speaker Michael Kieweg will discuss data and AI and the relationship between the two. Get comfortable and watch his session from the Machine Learning conference where he discusses how to tackle challenges related to data quality and how to use data for better artificial intelligence performance.
Increasingly large and diverse data sets allow us to form complex insights. With all this data, why would we limit ourselves by using data sampling instead? Sampling only works when it is put in the hands of data science specialists. In this article, learn about some of the downsides of using data sampling and how it limits and undermines business decisions. Read part one of the case against data sampling.