Earlier this month (April 2021), I released version v1.1 of the Machine Learning Canvas. It’s grown more stable in the last couple of years, so last November, instead of updating from v0.4 to v0.5, I decided to go straight to v1.0. After a couple of months of experience with it, coaching a few companies in their usage of MLC v1.0, I made a couple of tweaks and ended up with v1.1. Let's have a look…
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,…
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…
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…
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…
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…
Immediately after PAPIs.io '14 I spent a couple of days at Strata in Barcelona. Strata has several tracks and I ended up going mostly to “business” sessions, but this synthesis of things I heard at the conference will be of interest to technical people as well. Actually there was one business session that had more code in it than another data science session I went to! Here is my selection of key…
Big data startup Qucit released this month the world’s first bikeshare predictive API, tightly integrated in the popular mobile app for Bordeaux bikes. This is an inspiring example of Machine Learning usage in the real world. One of the value propositions of Predictive is better resource management. Here, in the "smart city" context, it impacts our everyday lives.
Last week I visited the Import.io offices in London and did a webinar with them in which I showed: * how to use their tool to easily scrape real estate data from the web * how to clean that data with the Pandas library in Python * how to build a real-estate pricing model by sending the clean data to BigML. Check out the video recording and the write-up they did!
I will be chairing the PAPIs.io conference taking place on 17-18 November 2014 in Barcelona, right before Strata. It will be the first ever international conference dedicated to Predictive APIs and Predictive Apps. If you're interested in presenting your work in this space, a Call for Proposals is open until 8 October 2014.
As machine learning and predictive analytics services become more widely embraced in the business world, predictive APIs are starting to open up. When evaluating this class of API, it is useful to have a common set of questions — the answers to which will help determine whether prediction APIs are a good fit for your needs and to steer you toward the best product for your organization.
Open data is a way to increase transparency into what happens in our society. When coupled with predictive modelling, it becomes a way to interpret why things happened. Even though it sounds complex, these techniques have become accessible to the masses. Let's see how this works with elections data. Co-authored with Alexandre Vallette
I am proud to announce that I have teamed up with HumanCoders to set up a groundbreaking Machine Learning training program which is based on Prediction APIs and brings you up to speed in 3 days. At this occasion, they interviewed me and I gave them 3 copies of my book, Bootstrapping Machine Learning, to give away — here's your chance to snatch one of them!
Churn prediction is one of the most popular Big Data use cases in business. It consists of detecting customers who are likely to cancel a subscription to a service. Although originally a telco giant thing, this concerns businesses of all sizes, including startups. The problem of churn prediction can be tackled with machine learning techniques. Now, thanks to prediction services and APIs, this sort…
What is a Data Scientist? What is it that they do that is now being automated? What are the new solutions out there that are bringing Data Science directly to domain experts? What is it changing?
Is it possible to build a business around Prediction APIs such as Google's or BigML's? And if so, how can you differentiate yourself from others who could use just the same APIs?
Today, I am releasing the first edition of Bootstrapping Machine Learning. Prediction APIs are making Machine Learning accessible to everyone and this book is the first that teaches how to use them.