About The Speaker
David is a cloud farmer- developing ecosystems for widely distributed computation resources.
Before his career with Vaisala, he worked at various data centers, learning FLOSS coding at LUGs in his spare time.
In 2019, Vaisala hired him as a system administrator to migrate a Network Operation Center from Tucson AZ to Boulder CO.
Spurred by the Covid pandemic, Vaisala devised a new network monitoring path based on decentralized global network operations.
Curious about deterministic ML systems, David initiated a project to infer weather sensor and station exceptions.
6 months later GPT3 changed the public perception of AI. Ever since, David’s focus has been planting AI gardens on the banks of data lakes that withstand the storm of progress.
Applying ML Classifiers to Service Loops & the Critical Response
Using deterministic machine learning techniques as a precursor to agentic capabilities keeps operations teams in control, and is a force multiplier of their capabilities.
At Vaisala we use Nagios to keep our network performance at our fingertips. When our weather stations alert telemetrically, having a context report ready to understand the problem improves timely restorative actions. These reports can collect data from a wide range of sources, sort based on relevance, and even present recommended actions.
What I Hope You Learn
Let’s discuss how to identify which classification tools can be most effective for critical response, and how to prepare data from monitoring and CRM tools for training deterministic classifiers, and how to implement them in the operations work flow.
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