While AI seems to be the topic of the moment, especially in the tech industry, the need to make it happen in a reliable way is becoming more obvious. MLOps, as a practice, finds itself in a place where it needs to keep growing and remain relevant in view of the latest trends. Solutions like ChatGPT or MidJourney dominated internet chatter last year, but the main question is…What do we foresee in the MLOps space this year and where is the community of MLOps practitioners focusing their energy?

A little over a year ago, we released Grafana Machine Learning, enabling Grafana Cloud Pro and Advanced users to easily view forecasts of their time series. We recently enhanced Grafana Machine Learning with Outlier Detection, which allows you to monitor a group of similar things, such as load-balanced pods in Kubernetes, and get alerted when something starts behaving differently than its peers.

Amazon SageMaker makes it easy to prepare data for machine learning (ML) and then train, deploy, and modify ML models. SageMaker is a fully managed service that automates much of the ML lifecycle. So, if you want a single partner to help you through all stages of your Artificial Intelligence (AI) lifecycle, SageMaker might be the answer. Perhaps more important for this post is the promise that Amazon SageMaker can reduce your machine learning model costs. But does SageMaker pricing reflect this?

For the last few years, the entire networking industry has focused on analytics and mining more and more information out of the network. This makes sense because of all the changes in networking over the last decade. Changes like network overlays, public cloud, applications delivered as a service, and containers mean we need to pay attention to much more diverse information out there.

Outlier Detection is now available as part of the Grafana Machine Learning toolkit in Grafana Cloud for Pro and Advanced users. With this feature, you can monitor a group of similar things, such as load-balanced pods in Kubernetes, and get alerted when some of them start behaving differently than their peers. There’s supposed to be a video here, but for some reason there isn’t. Either we entered the id wrong (oops!), or Vimeo is down.

As an observability provider, we are always confronted with our clients’ goal for faster resolution of problems and better overall performance of their systems. By working on large-scale projects at Logz.io, I see the same main challenge coming up for all: extracting valuable insights from huge volumes of data generated by modern systems and applications.