Systems | Development | Analytics | API | Testing

ClearML

Growing AI Fast with ML-Ops: Breaking the barrier between research and production

AI models get smarter, more accurate, and therefore more useful over the course of their training on large datasets that have been painstakingly curated, often over a period of years. But in real-world applications, datasets start small. To design a new drug, for instance, researchers start by testing a compound and need to use the power of AI to predict the best possible permutation.

Building an MLOps infrastructure on OpenShift

Most data science projects don’t pass the PoC phase and hence never generate any business value. In 2019, Gartner estimated that “through 2022, only 20% of analytic insights will deliver business outcomes”. One of the main reasons for this is undoubtedly that data scientists often lack a clear vision of how to deploy their solutions into production, how to integrate them with existing systems and workflows and how to operate and maintain them.

Enabling distributed NLP research at SIL

In my main position, as a data scientist at SIL International, I work on expanding language possibilities with AI. Practically this includes applying recent advances in Natural Language Processing (NLP) to low resource and multilingual contexts. We work on things like spoken language identification, multilingual dialogue systems, machine translation, and translation quality estimation.