Dataiku Data Science Studio (DSS) is a platform that tries to span the needs of data scientists, data engineers, business analysts, and AI consumers. In addition, Dataiku DSS tries to span the machine learning process from end to end, i.e. from data preparation through MLOps and application support. The Dataiku DSS user interface is a combination of graphical elements, notebooks, and code, as we'll see later on in the review. As a user, you often have a choice of how you'd like to proceed, and you're usually not locked into your initial choice, given that graphical choices can generate editable notebooks and scripts. During my initial discussion with Dataiku, their senior product marketing manager asked me point blank whether I preferred a GUI or writing code for data science.

Almost every action we do on the Internet or on mobile applications is recorded in files known as web logs. These logs can be very voluminous, providing a classic example of Big Data. Data science and Machine Learning algorithms can provide a way of extricating value from web logs. At the OVH Summit on the 11th of October, I presented a workshop on getting value out of web logs through Machine Learning with Dataiku DSS. In this article, I will run through the aspects of that presentation.

Almost every action we do on the Internet or on mobile applications is recorded in files known as web logs. These logs can be very voluminous, providing a classic example of Big Data. Data science and Machine Learning algorithms can provide a way of extricating value from web logs. At the OVH Summit on the 11th of October, I presented a workshop on getting value out of web logs through Machine Learning with Dataiku DSS. In this article, I will run through the aspects of that presentation.

Analysts have estimated that one third of Amazon's sales come via their recommendation system. Where do you think they got this recommendation system?? From visualization to data preparation, from the creation of machine learning models to putting these models in production -- all of this generates a lot of plumbing to connect all the parts. These pioneers broke the CLIENT-PROVIDER RELATIONSHIP that exists INSIDE companies between the business, IT and analytics teams. They found a way to create a predictive application "dream team" with all the key players focused on building the best product possible.

Hi everyone, my name is Clara and I joined Dataiku's data science team a while ago for an internship. Today I'm going to tell you about a project that was inspired by an overheard conversation during lunch: Alivia Smith (who you are already familiar with if you are an avid reader of our blog) was struggling with the schedule of the Avignon Festival, a French theater festival; struggling because there are so many plays and events happening, but no real guide or documentation to help her decide on her schedule. Since we're a great big loving family at Dataiku, and we're always enthusiastic about playing with data, a couple of us data scientists figured we could use machine learning to build a play recommender for her, so she could have insights regarding which plays she might like, based on her tastes and the theater community's appreciation. We computed several recommendations for Alivia using a method known as collaborative filtering. Essentially, she gave us several plays she had already seen and liked, and from those we deduced a list of other plays she may like, with a score (i.e., an estimation of how much she would like them).