At Londogard we find order in chaos by creating structures and understanding out of unstructured data. Our belief is that by creating understanding of data new possibilities open up and a lot of automation is possible where today manual tedious work is applied.
Our goal is to deploy Machine Learning models that makes sense and provide value rather than being made to tick of a check-box. The aim is to provide Efficient1, Performant2, Measurable3 & Understandable4 models.
Through our blog & demos we try to share powerful models and concepts that we find interesting or exciting!
Who are we?
Hampus Londögård
Senior Machine Learning Engineer & AI Cloud Lead @ Verisure
Have worked with multiple technologies across the Data & AI space. Currently enjoying working with highly efficient local tools such as Polars and training models using PyTorch.
Dennis Londögård
Software Developer (Big Data) @ Apple via AFRY
Has worked a lot with Scala, both backend (Play) and Data (Apache Spark).
Contact us
See social buttons or email us at [email protected].
Footnotes
Efficient should truly run on a single small machine (the edge).↩︎
Performant achieving very close to State-of-the-Art performance, striking a good balance of performance & efficiency.↩︎
Measurable in a way that makes sense for your personal use-case. Not random metrics that no-one understands and doesn’t map to real-world performance.↩︎
Understandable to the level where you understand why a prediction was made as it was.↩︎