5 Best Practices to Adopt for A Data Science Project
Once you decide to develop Data Science projects, you will break new ground, and you will need to get it right from the start. Of course, there are many technological challenges on this path, but you will also find out about cultural issues that will make collaboration hard. Today, we will share some best practices of […]
What is Natural Language Processing?
Have you ever wondered how your phone could possibly be able to understand what you are saying? Has this brainless pile of metal and plastic acquired the ability to talk with humans? If you already spend time playing with Siri, OK Google or Cortana, trying to fool them with some convoluted questions, you got an […]
What are the Differences Between Data Scientist and Developer?
According to the latest trends, data science and software engineering skills are among the most in-demand on LinkedIn. However, data scientists sometimes have a hard time explaining what their job consists of, trying to explain that their typical day of work is made of mathematics and code. At some point in the conversation, one may often conclude “well, […]
How to Deploy a Machine Learning Model?
This article invites you on a short tour of how to go from exploration to production when working with Machine Learning models. What are the major stages of ML models life cycle? In the last part of the article, we will show an example of architecture based on Docker compose and hosted in the cloud to deploy your […]
4 Tips to Go From Machine Learning use case to Operational Business Pilot
This article offers suggestions that can help you take your Machine Learning POCs a step further and develop a real operational business pilot. Often, we need to remind ourselves that our ultimate goal is not to train the best possible model but to harvest its potential value in the real world. We will provide here a […]
How to Take Your First Steps into Docker!
Docker or containerization is one of the most trending topics in both software development and Data Science right now. Understanding the concepts underlying this technology is as important as knowing how to read and write. After a short introduction and why you should use it, we’ll explore its terminology such as Dockerfile, Docker image and […]
What is Quantum AI?
“AI” and “quantum”. Here you go, two buzzwords for the price of one! More seriously, although I am aware that it is difficult to sum up two such big fields with a few articles, I wanted to try to expose, as faithfully as possible, the pros and cons of quantum computing applied to artificial intelligence, […]
DataOps: Devops Applied to Data Projects
Only half of all AI projects have been deployed today. Why? Because it often takes longer than it looks – between 12 and 18 months. That is what comes out of the many studies of Gartner, Capgemini or BCG: the need to industrialize. A new emerging concept called DataOps was born from the complexity to […]
How to Unlock the Public Cloud Potential with Kubernetes?
January tends to be a good time for European senior executives to get inspiration from innovative startups by visiting CES in Las Vegas. They often accept at the same time invitations from large software companies in Silicon Valley and Seattle delivering a smoothly packaged marketing message on the advantages of a Cloud strategy. The invariable […]
Agile Data Science: the Way to Meet Business Success!
Applying methods from Agile software development to Data Science projects, is it only possible? This is a question we want to explore in this article. To set the scene, let’s consider the following cartoon as an example: In this typical situation, the Data Scientist is excited and focused on improving the predictive power of his models while the […]
Put Open Source in your Data Projects
It has become impossible to talk about Data without mentioning open source. Just take a look at the different platforms that offer Big Data solutions, the vast majority of which are open source oriented. For good reason, technologies such as Cassandra, Hadoop, Apache Spark, Talend and many others now offer high quality services for building […]
How to Deploy Analytics Programs Avoiding Bad Organizational Patterns?
Artificial Intelligence, Analytics, Machine Learning… these words are on every lip – every day, tens of articles on these topics are published on the web. Most leaders know that business must become more analytics-driven. Not only to embrace the new digital era, but also to make more educated decisions. Nevertheless, most organisations struggle to deliver this […]
