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.

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.

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.

At AgroScout, we’re taking on a massive challenge with some correspondingly exciting upside, both for us and for our customers: We’re creating an automated, AI-driven scouting platform for early detection of pests and disease in vast agricultural areas.

Congratulations on creating a clean(ish) dataset to use for training! Now while the dataset is stored where it’s accessible to everyone, the distribution itself is a hassle! Local workstations, local GPU machines, and cloud machines (that may be spun up and down without disk persistence) are getting data everywhere. …and to say it is annoying is an understatement!

Everyone wants to manage their data, and if it’s a feature store, even better! But for optimal data management, we must first discuss lightweight zero upfront setup costs and maximizing utility with ClearML-data. ClearML-data mimics the light weightiness of git for data (who doesn’t know git?) and gives it a spin. It is an open-source dataset management tool which is extremely efficient and conveys how we view DataOps and its distinction from git-like solutions, including.

What can we say: Research is non-linear, there are tests, and adjustments, and more tests, and more adjustments, and then we add more data, and test some more, and… you know the story.

We’re excited to announce ClearML’s elevated recognition by NVIDIA as an Inception Premier Member.

I think it’s safe to say that one of the worst things in Machine Learning is the terminology. The maths and statistics are definitely part of the learning curve, but more than that, it feels like you are learning a new language. In some ways, you are. DataStore and FeatureStore are two of the current buzzwords that people are trying to understand. To be fair, DataStore and FeatureStore feel like family rather than strangers.

Few things in life are certain, least of all roadmaps. There is a saying I love, apart from the one above, which says “if you want to hear God laugh, tell him/her your plans”. Nowhere is that more true than in software development in a startup. There are grand ideas put forward, people often vie with one another, in short, life happens.