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The latest News and Information on APIs, Mobile, AI, Machine Learning, IoT, Open Source and more!

Video: Cloud Native Traffic Replay

With the introduction of new application platforms like Kubernetes, oftentimes the DevOps tooling around it needs to evolve. Cloud Native technology is powerful but complex. This 5 minute demo video shows how Speedscale provides production simulation capabilities so you can check for resiliency, quality and scalability in your Kubernetes clusters. You can record data and traffic in production and replay sanitized traffic on the fly against a new cluster.

FOSS for Dummies: What Is Free and Open-source Software?

Are you looking for affordable software? Want to know precisely what an application is doing behind the scenes, or maybe you just want to stop giving massive corporations your hard-earned money? There is a ton of software and web platforms available online. Some cost money and infringe on your privacy, while others are open-source, transparent, and run by nonprofit organizations. The term “FOSS” is usually associated with the latter.

Top API Metrics for Different Teams That You Should Monitor

Building and utilizing modern applications now essentially requires APIs. They are a crucial component of every company's automated workflow, and as more customers depend on your APIs to power their applications, the demand for them to be trustworthy is growing. Your business will suffer if its performance, availability, or health degrades, thus proactive API monitoring is essential to ensure its dependability. We'll go through the most important API metrics in this article.

Self-hosted versus cloud-based mobile app testing

Testing is a vital part of the mobile app development process. Your team can use testing to evaluate the quality, security, and reliability of mobile apps before releasing them to your users. Users who expect their applications to be highly performant and intuitive. There are two ways DevOps teams can perform testing for mobile apps: on-premise (also called self-hosted) or in the cloud. But which of these is the best option for your team?

Strategies to Align AI Data Collection and Management with DevOps Practices

DevOps is characterized by the acceleration of processes to ensure continuous delivery without compromising high software quality. Balancing speed and quality is quite a challenging task, though. Data issues are among the most significant problems encountered by DevOps teams. These can be worse in the context of AI development, where massive amounts of data play a crucial role in machine learning.

Restrict API Access with Client Certificates (mTLS)

An application programming interface (API) provides access to the features of a business application, but with the visual elements stripped away. By using APIs, devices like tablets, self-service kiosks, point-of-sale terminals, and robotic sensors can connect up to apps running on servers in a datacenter or in the cloud. Because they give access to the heart of your business applications, it should come as no surprise that there are some APIs that the general public should not have access to.

Mobile Device Management (MDM) Overview

The use of mobile devices within organizations is steadily increasing, and it’s not slowing down anytime soon. Zippia reported that around 3 out of every 4 employees use their personal cell phones for work. Mobile devices in the workplace can provide many benefits, but can also bring more risks, which is why mobile device management is needed.

Using Grafana and machine learning to analyze microscopy images: Inside Theia Scientific's work

At GrafanaCONline 2022, Theia Scientific President, Managing Member, and Lead Developer Chris Field and Volkov Labs founder and CEO Mikhail Volkov — a Grafana expert — delivered a presentation about using Grafana and machine learning for real-time microscopy image analysis. Real-time microscopy image analysis involves capturing images on a microscope using a digital device such as a PC, iPad, or camera.

Machine Learning At The Forefront Of Telemental Health

Michael Stefferson received his PhD in Physics from the University of Colorado before deciding to make the jump into machine learning (ML). He spent the last several years as a Machine Learning Engineer at Manifold, where he first started working on projects in the healthcare industry. Recently, Stefferson joined the team at Cerebral as a Staff Machine Learning Engineer and hopes to leverage data to make clinical improvements for patients that will improve their lives in meaningful ways.