No :) However, back in 2016 I was trying to explain to software engineers how to think about machine learning models from a software design perspective; I told them that they should think of a database. …

Google Cloud offers an excellent Jupyter Notebook managed service called Vertex AI Workbench which is relatively pricy. For some cases, I suggest using an old trick that can get you up to a 91% discount: hosting Jupyter Notebooks on Compute Engine Spot Instances using standard templates. This easy solution yields almost the same experience as Google’s managed notebook service, allowing you to be equally productive and considerably reduce costs. In fact, for short ML tasks like EDA or model experimentation, it can be an optimal solution on GCP. However, please mind the cost-SLA tradeoff here.

A quick tutorial on how to configure your PyCharm to run on a remote GCP instance using an ssh remote interpreter — What are we trying to achieve? When developing a Python application, it’s often convenient to run the code on a GCP instance (AI Notebook / Google Compute Engine). This is beneficial especially since running Machine Learning (ML) or data processing code often requires a high spec machine with multiple CPUs and RAM.

I like that you published the container image and the swagger API! also, the Snowflake UDFs seem very powerful.

My team and I recently built a cloud-spend control system that helps users more-easily analyze their cloud costs. But we also wanted to go further and give users a glimpse into the future with one-click forecasts. To accomplish this, we based our solution on very capable forecasting tools, including Mata’s…

An AI model that was built on income survey data exposed more than what’s used to be thought of gender income inequality. Not only that on average female employees earn less than their male counterparts but also gender is one of the key differentiators for determining one’s salary. — Israel’s Machine & Deep learning community has conducted an extensive survey regarding employment trends and salaries. Omri Goldstein and Uri Eliabayev revealed not only average salaries but also included in their work a visualization of two Decision Tree models that were built using the survey’s data (see here and here)…

AWS released an awesome tool to teach Reinforcement Learning to beginners, but only exposed a limited interface for controlling it. We’ve hacked it and turned it into a Deep Q-Learning Raging Bull, compatible with OpenAI Gym and powered by TensorFlow. — Together with: Nir Malbin and Aviv Laufer When AWS released its AWS DeepRacer, it was intended to teach the basics of reinforcement learning through an interactive and physical autonomous car. The car could only be controlled by specific types of models, trained using the AWS console and uploaded through the…

Advances in AI frameworks enable developers to create and deploy deep learning models with as little effort as clicking a few buttons on the screen. Using a UI or an API based on Tensorflow Estimators, models can be built and served without writing a single line of machine learning code. — 70 years ago, only a handful of experts knew how to create computer programs, because the process of programming required very high theoretical and technical specialization. Over the years, humans have created increasingly higher levels of abstraction and encapsulation of programming, allowing less-skilled personnel to create software with very basic…

Kaggle Days is (almost) officially the most interesting event to meet, learn and compete against the most talented data scientists worldwide. And this is how we won it. — The city of Paris hosted this January (2019) the 2nd ever Kaggle Days event. More than 200 data scientists from all around the world gathered to learn, share knowledge and eventually compete against each other in a 11 hours in-class Kaggle competition that took place during the conference. …