Ridesharing company Lyft has open sourced Flyte, its distributed processing platform for machine learning workflows that is being used in different Lyft teams including Pricing, Data Science and Fraud. Let’s see how the open source tool can benefit ML workflows.
The popular data science library pandas just turned twelve, and now it’s headed for version 1.0.0. The first release candidate shows that pandas will receive a new scalar for missing values, a new deprecation policy following semantic versioning, a redesigned website and more.
ALBERT was developed by a group of research scientists at Google Research as an “upgrade to BERT.” The NLP model is designed to optimize the performance of natural language processing tasks as well as their efficiency, and now it has been made publicly available. Let’s take a closer look.
TensorFlow 2.1.0 has been released, following two release candidates. The final version of the machine learning platform includes new features and breaking changes. Meanwhile, Python 2.7 has reached its end of life and is no longer supported by TensorFlow. Let’s take a look at what else has changed.
Manifold, Uber’s model-agnostic visual debugging tool for machine learning, is now open source and available as a demo version and a GitHub repository. Manifold is built with TensorFlow.js, React, and Redux and is part of the Michelangelo machine learning platform. The open source version includes a few new features that will make for an easier user experience.
Take a look into the crystal ball. What does Gartner predict for 2020? Here are ten strategic trending technologies that tech leaders should have on their radar in the coming years. Augmented and virtual reality might train employees in the future, voting might become based on a blockchain, and AI security might become the most important factor in risk management.
Manual efforts to gather such a huge amount of information could eat up a lot of time. Hence, AI is leveraged to automate the stage and deliver flawless results while saving a lot of time and resources. Embedded AI and ML can help security testing teams in delivering greater value through automation of audit processes that are more secure and reliable.
Saving lives with deep learning, creating smarter chatbots and more: 10 takeaways from ML Conference 2019
ML Conference 2019 had lots of exciting talks and insights to offer. How can we make chatbots smarter and provide machines with abilities such as ethical values or emotional intelligence—and how can deep learning help save lives? We’ve collected 10 takeaways to share some highlights of our Berlin conference.
At ML Conference in Berlin, we caught up with keynote speaker Dr. Janina Loh. Watch the video to learn about the ethical issues surrounding robots and autonomous cars—and why she believes universal guidelines for robots ethics can and should not be established.
Both machine learning and the use of cloud-native environments built on containers are becoming more commonplace in the enterprise. Luckily, Kubernetes and containers are a perfect match for ML. The cloud-native model has many advantages that can be brought over to machine learning and other forms of artificial intelligence for more effective, practical business strategies.
How can we detect deepfakes that have been created with deep learning methods such as GANs? Facebook, AWS and Microsoft joined forces to launch the Deepfake Detection Challenge (DFDC) that should encourage developers to research this issue. Winners can receive up to $500,000 USD.
We interviewed ML Conference speaker Christoph Henkelmann in Berlin. The natural language processing expert shared some insights on Google’s model BERT, OpenAI’s recently fully released model GPT-2, and what the future may hold for NLP.
Generative Adversarial Networks (GANs) have recently sparked an increasing amount of interest, as they can generate images of faces that look convincingly real. What else are they capable of, what risks could they pose in the long run, and what do they have in common with the emerging internet in the 1990’s? We interviewed ML Conference speaker Xander Steenbrugge.
What is the current state of machine learning and data science in enterprises? For the second year in a row, Algorithmia have published their State of Enterprise Machine Learning report. It shows the top machine learning use cases in enterprises and what trends to look out for in the future.