[unable to retrieve full-text content]On November 30th 2017, there’s a new kind of data science & analytics conference: EGG2017, Dataiku’s first large-scale data science and analytics conference in New York, NY.
Original Post: EGG2017: Innovate. Get Ahead. Disrupt. And Embrace Non-Conformity.
[unable to retrieve full-text content]Read “Analyst of the Future” guidebook to discover 3 emerging analyst roles and what they encompass, 4 trends transforming the world of data, and more.
Original Post: The Role of the Data Analyst in a Predictive Era
[unable to retrieve full-text content]The validation step helps you find the best parameters for your predictive model and prevent overfitting. We examine pros and cons of two popular validation strategies: the hold-out strategy and k-fold.
Original Post: Making Predictive Models Robust: Holdout vs Cross-Validation
[unable to retrieve full-text content]Learn how to deploy your Data Science work in production, both in batch and real-time environments, where people and programs can use them simply and confidently.
Original Post: Get Out of the Sandbox – Put Your Models in Production, Aug 10 Webinar
[unable to retrieve full-text content]Learn how predictive maintenance differs from and better than traditional one; Use cases and potential data sources; and next steps for getting started.
Original Post: Free Guidebook: Build a Complete Predictive Maintenance Strategy
[unable to retrieve full-text content]For over a year we surveyed thousands of companies from all types of industries and data science advancement on how they managed to overcome these difficulties and analyzed the results. Here are the key things to keep in mind when you’re working on your design-to-production pipeline.
Original Post: Your Checklist to Get Data Science Implemented in Production
Tweet Previous post Next post Tags: Data Science, Dataiku, Machine Learning, Scala Introducing Dataiku DSS 3.1, with new visual machine learning engines that allow users to create incredibly powerful predictive applications within a code-free interface. Dataiku DSS 3.1 Now with 5 ML Backends & Scala! Dataiku DSS 3.1 introduces new visual machine learning engines that allow users…
Original Post: Dataiku DSS 3.1 – Now with 5 ML Backends & Scala!
Interview: Florian Douetteau, Dataiku Founder, on Empowering Data Scientists Previous post Next post Tweet Tags: Ajay Ohri, API, Data Science Tools, Dataiku, Florian Douetteau, Python, R Here is an interview with Florian Douetteau, founder of Dataiku, on how their tools empower data scientists, and how data science itself is evolving. comments By Ajay Ohri, Author. Dataiku develops…
Original Post: Interview: Florian Douetteau, Dataiku Founder, on Empowering Data Scientists
Tweet Previous post Next post Tags: Dataiku, HPE, Retail, Vertica The retail industry has been data centric for a while. With the rise of loyalty programs and digital touch points, retailers have been able to collect more and more data about their customers over time, opening up the ability to create better personalized marketing offers and promotions.…
Original Post: Predicting purchases at retail stores using HPE Vertica and Dataiku DSS
Survey: Why Companies Still Fail to Get Full Value From Big Data Previous post Next post Tweet Tags: Big Data, Dataiku, Production, Survey Any company that has decided to put efforts in data has to face bringing these projects from the design and development phase to the production phase at some point. So tell us how you…
Original Post: Survey: Why Companies Still Fail to Get Full Value From Big Data