The adoption of AI/ML in financial services is increasing as companies seek to drive more robust, data-driven decision processes as part of their digital transformation journey. For global banking, McKinsey estimates that AI technologies could potentially deliver up to $1 trillion of additional value each year. But productionising machine learning at scale is challenging.

With more and more applications moving to the cloud, an increasing amount of telemetry data (logs, metrics, traces) is being collected, which can help improve application performance, operational efficiencies, and business KPIs. However, analyzing this data is extremely tedious and time consuming given the tremendous amounts of data being generated. Traditional methods of alerting and simple pattern matching (visual or simple searching etc) are not sufficient for IT Operations teams and SREs.

MLOps (short for machine learning operations) is slowly evolving into an independent approach to the machine learning lifecycle that includes all steps – from data gathering to governance and monitoring. It will become a standard as artificial intelligence is moving towards becoming part of everyday business, rather than an innovative activity.

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.