The COVID-19 global pandemic has added new urgency to the quest for digital transformation. The pandemic disrupted business processes and displaced and disconnected people. Organizations across the globe witnessed first-hand that the speed with which they could get new automations and applications in place could literally make or break the company. The world quickly learned what some industry leaders already knew: the ability to develop an application and quickly bring it to market is crucial.

Tapping into more compute power is the next frontier of data science. Data scientists need it to complete increasingly complex machine learning (ML) and deep learning (DL) tasks without it taking forever. Otherwise, faced with a long wait for compute jobs to finish, data scientists give in to the temptation to test smaller datasets or run fewer iterations in order to produce results more quickly.

Much has been written on the growth of machine learning and its impact on almost every industry. As businesses continue to evolve and digitally transform, it’s become an imperative for businesses to include AI and ML in their strategic plans in order to remain competitive. In Competing in the Age of AI, Harvard professors Marco Iansiti and Karim R. Lakhani illustrate how this can be confounding for CEOs, especially in the face of AI-powered competition.

Modern business applications leverage Machine Learning (ML) and Deep Learning (DL) models to analyze real-world and large-scale data, to predict or to react intelligently to events. Unlike data analysis for research purposes, models deployed in production are required to handle data at scale and often in real-time, and must provide accurate results and predictions for end-users.

Data science has come a long way, and it has changed organizations across industries profoundly. In fact, over the last few years, data science has been applied not for the sake of gathering and analyzing data but to solve some of the most pertinent business problems afflicting commercial enterprises.

We’re delighted to announce the release of the Iguazio Data Science Platform version 2.8. The new version takes another leap forward in solving the operational challenge of deploying machine and deep learning applications in real business environments. It provides a robust set of tools to streamline MLOps and a new set of features that address diverse MLOps challenges.

Recently, my colleague published a blog build on your investment by Migrating or Upgrading to CDP Data Center, which articulates great CDP Private Cloud Base features. Existing CDH and HDP customers can immediately benefit from this new functionality. This blog focuses on the process to accelerate your CDP journey to CDP Private Cloud Base for both professional services engagements and self-service upgrades.

Since Snowflake announced general availability on Azure in November 2018, increasing numbers of customers are deploying their Snowflake accounts on Azure, and with this, more customers are using Power BI as the data visualization and analysis layer. As a result of these trends, customers want to understand the best practices for a successful deployment of Power BI with Snowflake.

COVID-19 has radically altered most aspects of our social worlds and the way we do business. One of the largest impacts to organizations has gone on mostly unseen to the outside world. Companies have been quickly altering the way they manage the keys to their data kingdoms to maximize their data’s value to survive and compete.

For example, let’s say that an app that we’re working on supports multiple themes, and that we’re keeping track of the user’s currently selected theme (and other user-configurable settings) through a SettingsController — which in turn uses Foundation’s UserDefaults API to persist those settings, like this.