This emerging technology merges the physical and digital worlds in ways that have the potential to transform many industries.
Embedding analytics in apps is a smart way to expose decision-making capabilities directly in employee workflow and customer-facing apps.
The three big cloud providers want developers and data scientists to develop, test, and deploy machine learning models on their clouds.
Automation and integration are key for companies hoping to modernise dev, ops and security workflows.
Feature flags boost integrations with analytics, provide feature controls to product owners, and improve app rollouts.
Better testing means better software. Using NLP, test data generation, and optimised testing can quickly improve applications.
Best practices such as code refactoring and using microservice design patterns help software developers working at high velocity.
Embedding analytics in applications is a smart way to expose insights and decision-making capabilities directly in employee workflow and customer-facing apps.
Multiple options make machine learning available to professional data scientists, citizen data analysts and software developers.
When teams are split between home and office, establishing some ground rules will keep projects moving efficiently.
As your data evolves, you need a way to track the who, what, when, why, and how of those changes. You need a data lineage system.
So little time, so many ways tech decisions can go wrong. To make wise choices, don’t let these decision-making anti-patterns get in the way.
Once upon a time, business sponsors pestered development teams about when a feature would be done or a release ready for deployment.
Virtualisation of APIs and application services supports robust and earlier testing, an important part of application modernisation.
Multi-cloud architecture can be expensive and complex. These tools can facilitate provisioning, automation and resiliency.
