As you may know the data market is evolving very fast.
Until recently, data engineering platforms were dedicated to processing information and routing data. Today, more than half of our customers are looking for a global platform which iintegrates data science layer with data lakes and data pipelines. The industrialisation of tools for data scientists and data engineers, to help them prepare models and put them into production, is now a strong market trend.
Data lakes are now an asset whose best practices are known and mastered. The situation is different for data science, where what might be considered state of the art is not yet set in stone. On these platforms, our work sits between R&D and industrialisation. Our aim is recognise best practices and apply them within such platforms alongside supporting our clients adopt. This is a transitional stage before further industrialisation.
In five years’ time, machine learning operations (ML Ops) platforms will be deployed in an automated manner on AWS; practices will be standardised, and there will be no more “how to” unknowns.
In this ebook we bring you an overview of data challenges:
- Data lake best practices
- Emergence of ML Ops
- Feedback from our customer experience – Olympique de
Marseille case study
- Anomaly detection use case
- Focus on the data scientist profession
If you are interested download here