Model Management and Operations throughout the AI & ML
Model Management and Operations throughout the AI & ML
30 November 2021
The field of artificial intelligence (AI) is the technical field in vogue today and will be for a long time in the near future, and rightly, the models of Machine Learning (ML) do not stop progressing day by day and this applies to all fields of application.
Thereby increasing their number and versions by their designers, and leading to management problems such as concept Machine Learning Model, Machine Learning and ML management, teamwork and operational ML, specialists who do this work exist and have an effective contribution to the image of Verta.
What is a modelHub Management platform?
As its number indicates, this is previously a platform accessible on an internal network or via the Internet even offering the possibility of using VPN mode.
This Hub or management platform makes it possible to coordinate all the work required in the development of artificial intelligence and machine learning models, so it is an operational tool or Machine Learning (ML operations) platform.
What options are available on this model hub?
This model hub is a Machine Learning platform that offers you the possibility to: build high-level models, store them and above all manage them in the best way to be able to differentiate them, it allows you to monitor the performance of each version of the models filed and correct any errors that have arisen, to achieve models close to the desired reality.
Among the options available on the platform are:
Experiment Management:
This Machine Learning model hub and model management platform helps in the creation of Machine Learning models of optimal quality, these models are quickly functional and exploitable thanks to advanced functionalities of management monitoring and reproducibility of experiences; it efficiently manages the different versions of data sets and model visualization models.
Verta contributes to the creation of high-quality models based on models which have already been proven in the field.
Model Registry:
The Verta platform offers the ability to search and publish and above all to exploit Machine Learning, this functionality is similar to the DockerHub container or the Pypi package on Python, so a developer of ML model and pipelines have the possibility to publish ready-to-use components.
Model Deployment:
The model deployment and packaging function offers the possibility of deploying and publishing different versions of your models thanks to the CI/CD, the deployment carried out; the platform thanks to its scalability makes it possible to respond to various workloads and to work at large scale.
The one-click deployment option offers the possibility of launching models in test and in production in a fast way, it allows directly to integrate your models into the CI/CD pipeline which further optimizes the workflow.
Model Monitoring:
One of the interesting and important options offered by the platform is that of monitoring your models, in fact, verta allows you to have information and alerts in real time regarding the performance of your models as well as to characterize your data, it also offers the possibility of debugging and correcting anomalies and thus to have an immediate and fast interaction with your models, this is what is commonly called: being proactive.
A completely configurable and adaptive monitoring framework is offered by the platform which allows a better exploitation of whatever usable service.
The monitoring option offers great visibility, in particular on the failure of models during the test phase, performance parameters and various statistical properties.
Where does Experiment Management fit into the ML Lifecycle?
In order to arrive at a perfect ML model, or at least get as close as possible to it, you will need to test and retest your model on a large number of data, it is also necessary to perform comparative tests between each model to be able to achieve your goal, so the platform of concept model and management allows you to test your models and compare them to have a model ready to use.
The collaboration
The field of data science is an area of collaborative work par excellence, which means that the development of an artificial intelligence (AI) model, ML machine learning model or deep learning must be done on the basis collaboration on the part of several specialists at the same time, specialists in mathematics, statistics and computer science.
In summary, all the fields and experts contributing in this field, moreover this collaboration and even greater when it is a complicated model.Ainsi le travail collaboratif trouve toute sa définition dans le domaine de la science des données ; ledit travail passe impérativement par une gestion des données de travail en tout premier lieu, s’en suit une gestion des modèles, leur test et la validation finale, mais par-dessus tout il y’a lieu de fournir un moyen d’accès simple, facile et instantané pour tous les collaborateurs.
All the prerequisites and requirements are widely offered by the Hub platform; they thereby improve the productivity of the developer and his models throughout the organization of work on the platform.
So whether you are in the same office or in telecommuting mode; all you need to do is take advantage of this cloud-based collaboration option.
Conclusion
Whether you are a beginner or a professional in the field of artificial intelligence, Machine Learning or Deep Learning, the Verta Hub model is an effective management tool that will help you develop efficient models by working in particular with your respective teams and this is completely safe.
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