Together with Silo AI, you are able to modernize your machine learning operations, including continuous model development, testing, serving, and monitoring.
We help you apply AI and take it into use even in the most complex environments with efficient MLOps processes and tooling.
With Silo AI, you get to work with some of the brightest AI Scientists and AI Engineers that have a strong track record in scaling AI to bring value for various business needs.
With our experience from 100+ production-level AI projects, we’re your trusted partner for
The biggest challenge in production-level AI is not finding the best ML model, but building a reliable, scalable, and maintainable system for operating it in production and developing it continuously.
We put our learnings from 100+ production-level AI projects into one easily digestible and visual eBook about scaling AI with MLOps.
The transformation into a truly AI-driven company requires meticulous engineering and a proper machine learning infrastructure. With that, you’ll ensure that the core solutions work optimally and deliver measurable results in a transparent way, while being connected to the rest of the digital systems of the company.
MLOps is a way-of-working that puts your people working around the machine learning solutions to the core of the process.
Machine Learning is defined by a need for rapid experimentation. To achieve an environment of iterative and low-risk experimentation, both hard aspects (tools and platforms) and soft aspects (culture, ways of working) need to be aligned. Ville Tuulos, the driving force behind Netflix’s Metaflow platform, explains how Netflix has managed to tackle both sides of the coin.
I worked as a Solution Architect in building an MLOps platform that is offered as a service and aims to be the backbone for most of the ML operations in one of the biggest financial institutions in Sweden.
The overall goal of MLOps is to make the process of productizing ML models smoother. MLOps applies the DevOps techniques, concepts and practices to machine learning systems with an increased demand to take care of aspects around the activities, such as data versioning, data lineage and data quality.
We asked our Head of AI Solutions Alexander Finn to sum up the signs that indicate the need to start building a full-blown MLOps infrastructure and processes. Alexander is an expert in creating complete software development lifecycles for machine learning projects.
Considering elevating your business with a robust MLOps solution? We recommend you get in touch early on to get the best value of our expertise and collaboration.
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