Operations | Monitoring | ITSM | DevOps | Cloud

Building successful machine learning (ML) production systems requires a specialized re-interpretation of the traditional DevOps culture and methodologies. MLOps, short for machine learning operations, is a relatively new engineering discipline and a set of practices meant to improve the collaboration and communication between the various roles and teams that together manage the end-to-end lifecycle of machine learning projects.

Globally the impact of AI is increasingly growing year on year in every industry sector. Opening new scopes in construction & architecture sector, the global AImarket is estimated to grow at a CAGR of 29.4% (around) from 2019 to 2026 and is expected to reach around US$ 2.1 Billion by 2026.

We’re excited to share that the Deep Learning Toolkit App for Splunk (DLTK) is now available in version 3.6 for Splunk Enterprise and Splunk Cloud. The latest release includes: Let’s get started with the new operational overview dashboard which was built using Splunk’s brand new dashboard studio functionality which I highly recommend checking out. You can learn more about it in this recent tech talk which you can watch on demand.

A data center typically employs between 30-200 people depending on the size and the intended project use. But only a fraction of those folks run the IT side. Besides swapping gear—the infamous “remote hands”—there isn’t much to do as long as there aren’t any outages, and rumor has it a data center is a nice place to sleep—it’s dark, cool, and the deep humming noise can have a pacifying impact on some.

Advances in artificial intelligence (AI) are among some of the most exciting technological revolutions of our time. An increasingly ubiquitous aspect of our digital environment, AI is changing how we interact with our devices and surf the internet. Most of us fall somewhere between the two poles of a utopian and a dystopian understanding of the new technology, seeing both its benefits for society and its inherent risks.

How do you choose an API style and API technology when you start a new project? Today, if you look at API technology research such as RapidAPI Developer Survey and Insights, you’ll probably conclude that REST is the dominant force in the API landscape. While REST is certainly well-known to most developers and used in a lot of production environments, it may not be the best fit for every scenario.

We recently had a hackathon at Grafana Labs. Anyone who wanted could get several work days without normal responsibilities to do whatever they found meaningful in the wider Grafana community and/or Grafana Labs commercial offerings. This allowed me to invest some time into Kraken, a project designed for reading out different sensors, and to update it for modern hardware and libraries.

InfluxDB Cloud offers a transparent usage-based pricing model that only charges users on the work performed, with no minimums or long-term commitments. This puts YOU in charge of what you spend. However, with four separate pricing vectors, it’s not always easy to see exactly where that cost is going, or how to estimate your potential spend based on your data usage.

Today we released API 2.0, the latest version of TruSTAR’s API-First Intelligence Management Platform. This new version continues our commitment to simplify and streamline intelligence for automation in enterprise security intelligence management, and breaks through long-standing industry limitations around operationalizing data orchestration and normalization.

As APIs are so important for connecting modern cloud applications, keeping an eye on their availability and speed is essential if you want to give a great user experience. A good API monitoring tool can assist you in developing dependable APIs by detecting and fixing issues before they reach your users. If you're looking for a solution like this, look no further. In this article, we looked at some of the top API monitoring tools available today.