1. Machine Learning / AI Research, Engineering and Strategy, MLOps
Deploying machine learning applications to production is not just about serving those models from microservices. Your organization needs to be prepared in managing the lifecycle of ML development, understand the risks of probabilistic models and consider how to keep those models up-to-date when the world around changes. To get an idea of the considerations, see the checklist we are developing here.
Aapo’s broad experience in working with machine learning engineers and bringing up ML into products, in both small scrappy startup environments and Facebook-scale, can be useful for various projects. He has a strong end-to-end understanding of machine learning / AI development lifecycle and understands best practices from a strong theoretical and practical point of view.
Aapo’s broad experience in working with machine learning engineers and bringing up ML into products, in both small scrappy startup environments and Facebook-scale, can be useful for various projects. He has a strong end-to-end understanding of machine learning / AI development lifecycle and understands best practices from a strong theoretical and practical point of view.
Example Projects:
- Company wants to explore using Machine Learning (ML) for their product.
- Performance of the ML operations or team is not satisfactory.
- Taking exploratory or prototype models to production.
- Kick-starting ML development team in a company.
- Due diligence of MLOps for a company being acquired or invested in.