Overcoming the PoC phase becomes easy and immediate and quickly brings ML and AI projects into a productive environment
Machine Learning (ML) algorithms are increasingly being used, and in recent times a whole range of technologies has been developed, enabling their rapid development without having to deal with custom solutions for each model every time.
AI/ML projects are proliferating in all sectors with the aim of gaining competitive advantages given by innovative products and services.
However, while it is relatively easy to set up a prototype ML algorithm, it is quite a complicated and difficult thing to bring/integrate this model into a production environment inside the industry.
In fact, this is the reason why a considerable proportion of ML projects fail inside companies and disappoint the high expectations from the client.
To avoid this issue within eXact lab we are developing an integrated solution based on container-based MLOps logic.
The idea behind it is to create an integrated development environment to uniquely manage the different needs coming from the various professional profiles (from ML engineers, Data Scientists and Data Engineers) involved in a project complete with all its phases: design, training and testing, deployment.Lorem ipsum dolor sit amet, consectetur adipiscing elit. Ut elit tellus, luctus nec ullamcorper mattis, pulvinar dapibus leo.
The deployment in production can then occur extremely quickly with significant benefits for the adoption of Machine Learning and Artificial Intelligence models in the production environment and not only at the PoC (Proof of Concept) level.
Transfer ML and AI models to the production environment quickly, securely, and scalably
Our integrated solution based on container-based MLOps logic enables ML prototyping and subsequent transfer to the production environments within the industry.
AI/ML projects proliferate in all sectors with the aim of gaining competitive advantages given by innovative products and services. The greatest difficulty that arises, however, is the transfer of prototypes to production environments.
It is precisely for this reason and to simplify and accelerate the operationalization of projects that we have developed an integrated development environment that allows us to uniquely manage the different needs coming from the various professional profiles (from ML engineers, Data Scientists and Data Engineers) involved in a project complete with all its phases: design, training and testing, deployment.
It is exactly this method that allows the model to be tested and monitored as early as the development phase, with the consequence of encouraging rapid portability to production together with the monitoring and analysis tools that, by the way, are already available in the development environment.
The production environment has been designed paying special attention to the reliability and safety of data income and outcome. In addition, thanks to the scalability policy, it is possible to adjust the distribution of workload over available resources, avoiding performance degradation.
The deployment in production can occur extremely quickly, mainly because it is already in the development phase that its validity and applicability can be tested, with significant advantages for the adoption of Machine Learning and Artificial Intelligence models in the production environment and not only at the PoC (Proof of Concept) level. This approach was developed to highlight the main critical issues that may emerge in the finalization stages of the project while still being in the development phase. In this way, a reduction in the number of machine learning models abandoned precisely because of their lack of “operationalization” is expected.
From an infrastructural point of view, deployment can take place on HPC systems in public cloud, but also on-premise or in hybrid environments. In all cases, we always guarantee full support at the technical level for the development of the ML and AI models, as well as for the preparation of the HPC environments that will host the different phases.
Developers will have immediate access to the latest machine learning tools for building PoCs, benefiting from the tools needed to optimize the ML model and its testing, with the actual deployment within a secure and scalable environment.
