Letting your kids grow up and leave home is a pain every parent must know. And that's kinda how we feel today, watching our data scientists go out into the wild and creating their own data science...
Alivia Smith
Recent Posts
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Your Checklist to Get Data Science Implemented in Production
Building a data science project and training a model is only the first step. Getting that model to run in the production environment is where companies often fail.
Introducing Dataiku 4.0.1 with the "Power" of Apache Slug
We are very pleased to announce that the latest version of Dataiku (4.0.1) now fully sports Apache Slug, giving users the capability to execute code in microbatches. Dataiku and Apache Slug...
Top Trends: Putting Data Science in Production Today
We wondered why some companies successfully implement machine learning models into their operational system, and others don't.
Top Trends from Big Data World 2017
I've just come back from an awesome/exhausting two days at Big Data World 2017. It was a whirlwind of interesting talks with interesting people, new product discoveries, awesomely weird goodies,...
Want to Make your Data Science Team Efficient? Focus on Production.
By "efficient" data science team, I mean "data science that brings maximum productivity."
Infographic: How Does Your Data Science Production Process Compare?
Deploying your data science models into production often requires complex processes and resources that can lead to the failure of your data project. How do companies handle this process?
Professor Dan Adelman (University of Chicago) on How He Uses Dataiku to Teach
Here’s how Dan Adelman, Professor of Operations Management, uses Dataiku for his classes at The University of Chicago Booth School of Business. Professor Adelman joined the Dataiku Academic Program
The Dataiku Academic Program: Resources for Academics and Researchers
It’s about time we sat down and told you a little bit about our Academic Program.
How to Really Grow your Data Science Team
If you’re looking to intensify your data science efforts, it’s pretty fundamental for you to sit down with your current team and really ask yourself how you’re all going to make this work.
From Small Data to Large - What Does Big Data Really Change
We live in the world of big data. We all know this. I do, you do, heck, I even picked up Cosmopolitan the other day and it had a definition of big data in it!
7 Fundamental Steps to Complete a Data Project
It’s hard to know where to start once you’ve decided that yes, you want to become more data driven. Just looking at all the technologies you have to understand and all the languages you’re...
Tips & Tools To Find the Best Data Science News
Data science is a world that doesn’t seem to ever stay put. In the blink of an eye dozens of open source projects are launched, companies develop new data products or find new use cases and...
How Do You Really Get Data Science Projects Into Production?
Articles everywhere keep hailing the end of big data and criticizing the hype. They are right, to some extent. The revolutionary world where data answers all our questions before we even ask them...
Why you Need DataOps to Organize Your Data Science Projects
The DevOps movement tries to reconcile development and operations departments to encourage them to develop IT projects together. Today, we need to make that same transition in data, by introducing...
Tragedy Strikes at Dataiku - the Cat Ate the Bird
The whole team is extremely saddened to announce the death of its mascot, our beloved blue bird, in a tragic accident involving local cat terror, Fluffy.
Why Data Visualization and Dashboards Matter
Data scientists spend days and days (if not weeks and weeks) cleaning and preparing data, building and training models, and eventually trying to come up with interesting insights or delivering...
13 Tips to Not Win a Data Hackathon, Every Time
Hackathons are the coolest. Where else do you get to thrive amongst your peers in an intensely competitive environment, where you can have fun, build a useful product, learn, meet people, and get...
SQL, R, and Python: Why Data Wrangling in ONLY Code is Inefficient
So everyone knows the oh-so-popular statement that a data scientist spends 50 to 80% of his time cleaning and preparing his data before he even starts looking for insights in it. I mean everyone’s...
