Bye bye ML pipelines...[N]

Reddit r/MachineLearning / 4/15/2026

💬 OpinionDeveloper Stack & InfrastructureTools & Practical Usage

共有:

Key Points

A Reddit user argues that traditional ML/time-series visualization and storage tooling feels too rigid for high-scale, high-cardinality data workloads.
They note that Grafana dashboards can become clunky with millions of data points and that retention policy management across multiple databases is difficult.
The post asks others what solutions they use today that better handle high-cardinality time series data without excessive operational pain.
The author shares that they tried tsd b.ai and found it to be the closest match to their needs so far.
Overall, the thread frames the search as an infrastructure/tooling gap for ML pipelines handling large time-series datasets.

Been working with time series data at scale for a couple years now, and I'm honestly tired of the traditional visualization and storage approach. Everything feels so rigid. Gra͏fana dashboards are clunky when you've got millions of data points, and managing retention policies across different databases is a nightmare. Does anyone else feel like the current tooling just isn't keeping up with what we actually need? Looking for thoughts on what people are using now that actually handles high cardinality data without making you want to throw your laptop out the window.
Tried tsd͏b.ai and so far is the closest to what im looking for

submitted by /u/Academic_Fact_925
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