New technologies have permanently transformed how customers communicate, interact, research and shop for goods and services. Retail data paired with retail analytics, can assist retailers in understanding and responding with actionable retail insights to the disrupted landscape and changing customer expectations.
In this webinar, MemSQL Product Marketing Manager Mike Boyarski describes the growth in popularity of time series data and talks about the best options for a time series database, including a live Q&A. You can view the webinar and download the slides here. Here at MemSQL, we’ve had a lot of interest in our blog posts on time series data and choosing a time series database, as well as our O’Reilly time series ebook download. However, this webinar does a particularly good job of explaining what you would want in a time series database, and how that fits with MemSQL. We encourage you to read this blog post, then view the webinar.
Many data teams worry that automation won’t work on their specific data and technology stack. They’ve learned the hard way that automation doesn’t always stand up to the complexity of different source data models, taxonomies and tech stack components.
Join this webinar to understand how Data Vault 2.0 is designed to focus on models and logic, not complex code, so that it’s rapidly becoming the DWH standard.
We’ll explain how Data Vault has taken the best of the more traditional modeling approaches, such as Inmon or Kimball, to provide the level of abstraction, quality and agility that automation requires.
In this latest Data Science Central Deep Learning Fundamentals Series webinar, we will cover the fundamentals behind TensorFlow and how to apply them within a convolutional neural network (CNN) example. The principles we will cover include CNN concepts and their impact to the accuracy and loss of your network. All these concepts will be brought to life by demonstrating how Databricks simplifies deep learning - letting you quickly access ready-to-use ML environments, as well as prepare data, and train models faster. After this session, if requested, you will receive the presentation and associated notebooks so you can run the samples yourself.