Who said that predicting the future is difficult? All you need is a Prophet (and some reliable infrastructure)

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 Facebook’s Prophet, and thought that it would be helpful to share our insights!


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.

Photo by Magnet.me on Unsplash

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). The models clearly show how important gender is in determining the salary of a Data Scientist, a profession that was not once referenced as “the most sexy of our time”.

It’s important to mention that the team that published the report didn’t intend to perform research on income inequality, hence…


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 car’s interface. Little did they know that we were about to turn it into a raging bull that will learn to stampede towards objects!

The transformation required three steps: First, we had to discover the API running the car. Second, we had to develop a deep reinforcement learning environment and…


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.

Photo by Adi Goldstein on Unsplash

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 tools (see Wix for example). The exact same process occurs these days with machine learning — only it advances extremely faster. In this blog post we will write down a simple script that will generate a full machine learning pipeline.

Truly codeless?

This post contains two types of code. The first is…


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. This blog post describes our solution to the competition, that won us the 3rd place.

Predicting Sales of LMVH’s Luxurious Products

Challengers were provided with data from the first seven days of Louis Vuitton products after their launch on www.louisvuitton.com. The goal was to forecast sales on each of the three months following the launch. …


Building and deploying production-grade machine learning models can be somewhat tricky. Even with technologies like Google Cloud AutoML, Cloud ML Engine and other out-of-the-box machine learning tools, training models and using them in production systems commonly requires a vast set of skills that can include some advanced Python programming, understanding complex models, SQL and DB technologies. This blog post demonstrates how to build a prediction system for shared cars/bikes/scooters using very simple tools!

BigQuery GIS adds new capabilities to Google BigQuery that enable the ingestion, management and analysis of geospatial data. BigQuery ML facilitates the creation and execution of machine…


Both XGBoost and TensorFlow are very capable machine learning frameworks but how do you know which one you need? Or perhaps you need both?

In machine learning there are “no free lunches”. Matching specific algorithms to specific problems often outperforms the “one-fits-all” approach. However, over the years the data science community has gained enough experience to generate thumb rules for matching between certain algorithms and typical tasks.

In this short post I will try to cover some of these rules to help you decide between Gradient Boosting Machines using XGBoost and Neural Networks using TensorFlow.

XGBoost vs TensorFlow Summary

In 2012 Alex Krizhevsky and his colleagues astonished the world with a computational model that could not only learn to tell which object is present in a given image…


TL;DR: Amazon SageMaker offers an unprecedented easy way of implementing machine learning pipelines, significantly shortening the time to market for data scientists and engineers.

“photo of black digital alarm” by John Cobb on Unsplash

The NYC Taxi and Limousine Commission publishes detailed information regarding taxi rides in the metropolitan area. These data have been the subject of many data-science projects and several Kaggle competitions. In this tutorial, I am going to build a service that predicts future ride fare based on the origin, destination, and time of pickup. …


With Google BigQuery ML you can now predict your Google Cloud spend in just a few minutes and without leaving your BigQuery Console UI.

Introduction

Linear Regression, although very simple, can be used to generate accurate predictions for various real world problems efficiently. Due to its simplicity, linear regression training is easy to configure and benefits from fast convergence.

In this post, I will explain how to analyze Google Cloud billing data and build a simple prediction model to estimate the expected overall monthly expenditure. …

Gad Benram

ML Tech Lead @DoiT International Machine Learning Google Developer Expert.

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