Tapping edge computing and IoT devices for real-time analytics holds great promise, but designing analytical models for edge deployment presents a challenge.
Spoiler alert! The honest answer is that you can’t mandate agility, but you can achieve it through consensus by focusing on the benefits.
Balance the trade-offs between innovation and reliability by keeping code stable, delighting users, and avoiding tech for tech’s sake.
Low-code platforms for enterprise developers integrate with the devops toolchain to speed the delivery of applications and modernisations.
Regarding the end-user support staff as stakeholders with their own insights and needs creates better deployments for everyone.
As enterprise embraces edge computing, the big three clouds are ponying up a surprising array of edge options for a broad range of needs.
Automating integrations, repeat tasks, or multistep workflows can improve productivity and data quality.
It’s important for everyone working in IT to accept critical feedback and advice on improving processes, quality, and collaboration.
Once machine learning models make it to production, they still need updates and monitoring for drift. A team to manage ML operations makes good business sense.
Just deploy your new application, microservice, or machine learning model to the public cloud? Well, maybe not so fast.
Graph databases are proven architectures for storing data with complex relationships. Why aren't more companies using them?
A brief guide to the analytics lifecycle, the expanding array of tools and technologies, and selecting the right data platform for your needs.
For the most business value, develop a testing program based on personas, best practices, and agile principles.
Just about every organisation is trying to become more data-driven, hoping to leverage data visualisations, analytics and machine learning.
AWS Lambda’s serverless functions shine for event-driven data processing and machine learning, connecting cloud services and external APIs.
