Raymon.ai: detect and solve quality issues in AI systems

Raymon AI

Raymon.ai helps teams detect and solve quality issues in AI systems. As such, it is a monitoring and observability platform for AI-based systems. Monitoring tools help engineers find out when something is wrong, observability tools help them find out why. Basically, observability platforms offer engineers more insight in their systems, which is crucial for being able to maintain them in a cost-effective manner. In this blog post we dig a bit deeper into why exactly observability tooling is crucial for successful adoption of AI-based systems.

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Database optimization startup Silk raises $55M

Silk AI

Silk, a platform designed to improve database performance, today announced it has raised $55 million in a series B funding round led by S Capital. The company says that the funds will be used to support its sales and marketing operations and expand its engineering team as the demand for cloud environments rises in the wake of the pandemic.

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What “Shifting Left” in Software really means for blameless DevOps

Shifting left - Blameless DevOps

A few really interesting ideas came out of this week’s panel on Enabling Smart Engineering Discussions. There’s a lot of talk these days around how to practice a “blameless culture” in software engineering, and I think it’s important to note the variety of views that make up that idea.

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Sosivio nabs $4M for container monitoring and observability

Sosivio cloud

ontainer observability startup Sosivio today announced that it closed a $4 million seed round led by Seamans Holdings, with participation from Superposition Venture Partners and Side Door Ventures. Cofounder and CEO Nuri Golan says that the proceeds will be used to support product launches and allow the company to scale over the next few years.

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ServiceNow acquires Lightstep to gain Observability Platform

Roadrunner service dashboard

ServiceNow today announced it has acquired Lightstep as part of an effort to add an observability platform to its IT operations portfolio. Terms of the deal, expected to close this quarter, were not disclosed. The Change Intelligence observability platform from Lightstep is based on a time-series database that is capable of processing a trillion events each day. It was built by the same team that created Monarch, the globally distributed in-memory time series database system that Google employs to monitor its applications and systems.

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Introduction to Observability in ITOM and AIOps

Observability ITOM AIops

First things first. Observability is inherent as a principle to a system and not something that is instilled. Here, we address observability as an open source-based solution in the context of insightful monitoring within the ITOM landscape. ITOM is now in the middle of addressing the needs of the expanding and dynamic nature of IT infrastructure as a function. It is no longer about being a monolithic computing stack. It is now beyond monitoring discrete infrastructure elements. 

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The Model’s shipped; what could possibly go Wrong?

Retrain button

In our last post we took a broad look at model observability and the role it serves in the machine learning workflow. This leads us to a natural question of: what should I monitor in production? The answer, of course, depends on what can go wrong. In this article we will be providing some more concrete examples of potential failure modes along with the most common symptoms that they exhibit in your production model’s performance.

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IBM acquires Instana for its AI-powered app performance monitoring

IBM today acquired Instana, a German-American software firm that specializes in developing application performance management software. The acquisition represents IBM’s continued investment in hybrid cloud, big data, and AI capabilities. Terms of the deal weren’t disclosed.

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