Quantyca Data Science Lab use case image


In a modern data-driven company, data represents a fundamental asset and all actions and strategic directions are dictated by the insights gained from the analysis of data coming from a multitude of different sources.
Data science represents the set of methods, processes, algorithms and technologies that enable the extraction of useful knowledge from the multitude of structured and unstructured data that the company has at its disposal within the data warehouse, data lake or, more generally, the data platform.
In this way, artificial intelligence (AI) and machine learning (ML) techniques are redefining entire market sectors, from the world of online retail to transport services, from domotics to the insurance and banking fields, enabling the understanding of correlations and trends concerning complex phenomena such as consumer preferences, the evolution of demand for a specific product or service, and the analysis of market competition.
Over the last ten years, these technologies have spread not only in big companies, but increasingly also in SMEs; both have dedicated these years to experimentation, alternating between promising results and costly failures.


The main problems of data platforms on prem are:

  • Inability to scale resources elastically. At times of high load the platform is often in trouble while at times of low load it pays for idle resources.
  • Inability to scale storage and computation independently. If you have to increase one of the two, you also have to increase the other. The scaling unit is the server within the cluster.
  • High operating costs to configure and manage complex, distributed architectures often consisting of multiple technologies developed by different vendors.
of data science projects
never make it into production
VentureBeat AI
of data scientists
have a reproducibility production issue
of AI research
focuses on ML and neglects Data Preparation
Andrew Ng


The entire solution is based on an infrastructure capable of automating the data processing process for calculating the features required for ML models, the training and execution of ML models and their exposure via APIs.

There are also tools for isolating environments and projects, provisioning the development environment, and versioning code, data and models.

The Data Science Lab environment reconciles the data scientist’s need for agility with IT’s need for stability and maintainability, thus accelerating the release time of new models.

The complete route

1. Inception
Mapping the previous skills of the data scientist team and overcoming technological and methodological gaps through the provision of training (face-to-face workshops, e-learning, ...)
2. Foundation
Set up of the infrastructure and import and production release of the first ML models starting from the priority use cases
3. Expansion & Optimization
Implementation of new use cases by expanding the coverage of the Data Science Lab, automating training and production release procedures...


Overcoming technological and methodological gaps for Data Scientists
Process automation
Acceleration of testing times and transition into production
Facilitation of team collaboration and reduction of effort
Introduction of a robust monitoring system
Knowledge sharing among all stakeholders
Compliance with privacy and security standards

Success Stories

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