Customers today are in different stages of their Data modernization journey, and they need advice to fully realize the power of data based on their workflows. This article aims to offer a phased approach starting from simple data analytics to complex data workflows, machine learning models and Data Visualization insights using GCP’s Smart Analytics tools. The tool helps customers to accelerate their data adoption strategy and extract meaningful insights based on their data workflows to drive their business forward.

As your organization adopts Cloud Native architectures, you need to shift your mindset.

We are excited to announce enhanced full-lifecycle cloud-native development Professional Services capabilities to better help our customers build modern applications for the future.

Dynamics 365® Business Central, a comprehensive enterprise resource planning (ERP) application, has the following capabilities:

Rackspace empowers and enables several enterprise customers to take advantage of Adobe® Experience Manager (AEM) and the larger Adobe marketing and commerce ecosystem daily.

We designed the Rackspace Government Cloud Secure Configuration Baseline (RGCSCB) to support government cloud workloads, delivered to the customer as an Amazon® Machine Image (AMI).

If you haven’t read it already, start with Part one of this two-part series. Then read the rest of the story about the cloud operating model here.

Cloud computing has become the new normal for a lot of enterprises, start-ups, and those companies that still run legacy systems. Embarking on digital transformation by leveraging the Cloud is one of their top priorities.

As machine learning (ML) initiatives become more prominent across companies looking to leverage their data to improve future projections and decision-making, demand for frameworks that simplify ML model development has been soaring.

The value of data is tremendous, and any business model requires data to drive decisions and make projections for future growth and performance. Business analytics has traditionally been reactive, guiding decisions in response to past performance. Leading companies have begun to use machine learning (ML) and artificial intelligence (AI) to learn from this data and harness it for predictive analytics. This shift, however, comes with significant challenges.