When applying Machine Learning in your company, the 1st challenge is to formalize a Prediction Task that connects to a Value Proposition. I’ve created a new tool to help do this.

There are many opportunities to create value with ML: increasing productivity, avoiding undesirable events, automating repetitive tasks... But there are also many sources of cost and uncertainty. ML projects can feel like games of poker: you need to pay to see if you've got a winning idea, and you should avoid going all-in without strong odds in your favor! How can you figure out your odds, maximize chances of success, and minimize costs? By thinking like an investor! Here are 9 steps…

A framework to connect the dots between data collection, machine learning, and value creation

Something is wrong in the way ML is being taught to developers...

Artificial Intelligence has become increasingly present in our lives in the form of tools like smartphone apps. It can also be found in high-stakes autonomous systems where it makes decisions that involve the lives of human beings — such as Autonomous Vehicles (e.g. the “Google Car”) — or that involve important amounts of money — such as automated investment systems. AI can increase our productivity and creativity, or it can replace human intervention altogether by making better decisions, both in everyday life and in business. There is strong potential in AI-powered automation, but also important issues to address such as control, morality, and market uptake. Let’s dive in…

Two years ago, Mike Gualtieri of Forrester Research coined the term “predictive applications” and pitched it as the “next big thing in app development”. Today, some people estimate that more than 50% of the apps on a typical smartphone have predictive features. Predictive apps were defined by Gualtieri as “apps that provide the right functionality and content at the right time, for the right person, by continuously learning about them and predicting what they’ll need.” For that, they use Machine Learning (ML) techniques and data. APIs such as the ones provided by Amazon Machine Learning, BigML, Google Prediction API and PredicSis all promise to make it easy for developers to apply ML to data and thus to add predictive features to their apps, but it’s not obvious how these APIs differ from one another and how to choose the right one based on your apps’ needs...

Amazon Machine Learning made a lot of noise when it came out last month. Shortly afterwards, someone posted a link to Google Prediction API on HackerNews and it quickly became one of the most popular’s posts. Google’s product is quite similar to Amazon’s but it’s actually much older since it was introduced in 2011. Anyway, this gave me the idea of comparing the performance of Amazon’s new ML API with that of Google. For that, I used the Kaggle “give me some credit” challenge. But I didn’t stop there: I also included startups who provide competing APIs in this comparison — namely, PredicSis and BigML. In this wave of new ML services, the giant tech companies are getting all the headlines, but bigger companies do not necessarily have better products. Here's how I compared them and which results I got… 

There's a number of ways you could be using Data Science (DS) in your business. To manage your DS projects efficiently and have them deliver real value to your business, you should have a good overview of what DS can help you with and how. I've listed 9 things that I've grouped in 3 areas:

- I. Increasing the number of customers
- II. Serving customers better
- III. Serving customers more efficiently

Data Science can provide help in each area with the use of Machine Learning techniques. The idea is to map situations to outcomes by analyzing data, so we can then predict outcomes in new situations. Let’s see how this is used concretely...