[unable to retrieve full-text content]Here is a treasure trove of analysis and predictions from 17 leading companies in AI, Big Data, Data Science, and Machine Learning: What happened in 2017 and what will 2018 bring?
Original Post: Industry Predictions: Main AI, Big Data, Data Science Developments in 2017 and Trends for 2018
[unable to retrieve full-text content]Download your free guide to predictive analytics in media and entertainment for a look at the landscape and use cases, from Dataiku.
Original Post: Your guide to predictive analytics in media and entertainment
[unable to retrieve full-text content]Recommendation engines are effective because they expose users to content they may not have otherwise found. For a step-by-step guide on building an effective recommendation engine from the ground up, check out our latest guidebook.
Original Post: Recommendation Engines and Real-time personalization – download guidebook
[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!