Nowadays data science has become an indispensable and essential tool in the field of marketing and business. It is the decision-making tool par excellence: its forecasting and decision-making efficiency is constantly improving to reach an optimal level of precision.

As the evolution of artificial intelligence (AI) and machine learning models continues to progress and evolve, it is becoming essential to manage them through dedicated technical platforms and model hubs such as Verta, a platform that notably offers the ModelOps option. 

What is ModelOps?

Model Operations (ModelOps) explicitly refers to the overall interaction over any operation that may exist between data scientists and implementation operations professionals. In particular, this interaction is important in having a direct impact on success. The realization of an optimal machine learning (ML) model and its execution logically should go through the use of a platform such as Verta capable of automating and therefore simplifying it.

The phases of ModelOps: 

The critical phases of ModelOps are as follows:

• Gather and collect the data deemed necessary for the concept model
• Cleaning, processing, transformation or adaptation of data
• Data analysis for the extraction of useful information to consider in the design of models
• Development of models following technical literature or innovation
• Statistical mathematical validation of the model
• Performance monitoring and testing of the
• Improvement, Adaptation and learning

How could Verta help ?

Verta was designed to provide technical and ultimate support in the phases of ModelOps mentioned above. Beyond the fact that it is a Model Hub and that it allows a high level of management of machine learning and deep learning models, this model registry tool makes it possible to optimize the life cycle of models and arrive more efficiently and above all more quickly to exploitable and marketable final models.

Data science and Business 

Data science is a technical field using mathematics, statistics, and computer tools for the study and analysis of large volumes of data in order to extract relevant information to be used for the purpose of optimizing a specific field.

Data science is a field of high technical value used in countless fields such as pure science, sport, medicine, business.

In the field of business, the main objective is to achieve profitability first to its activity, secondly to increase its income, and stabilize them at the desired level and finally to reinvest them again. All of these goals are accessible using data science, a science that can be made all the more effective by having a dedicated tool and platform such as Verta.   

Some applications of data science in Business

In the following, we will describe some important areas of the application of data science to business:

Decision making :

Decision making is certainly an anchor point in any self-respecting business; for anyone with a business it is important to make the right decisions at the right time. This is quite accessible provided you have the right data and the right model; something which is all the more achievable by having a powerful tool to manage this data, design and concept the right models, test them and validate them, which means having a powerful ModelOps tool like Verta.

Sales optimization :

It is clear that the goal of any salesperson is to optimize their income; this optimization can be done in a scientific way.

Having the adequate tools to manage the sales data and to create and concept a dedicated model for sales optimization, to test it and validate it, is one of the main conditions to achieve your purpose. For that reason, it’s highly recommended to use a platform like Verta.  

Stock market forecast

Trading and therefore stock market forecasts is an area of ​​application of data science par excellence, and for good reason: a good trader will use all his technical faculties to study the stock market and establish a model as close as possible to reality to be able to make reliable forecasts and therefore make choices in terms of buying and selling shares.

In this type of business, it becomes imperative to have a data management platform to create and store models and test them in collaboration with a set of specialists who will undoubtedly work to extract the best possible model. It is in this view of the fact that having a ModelOps as well as a technical collaboration tool, particularly via the cloud, is of capital importance.

Optimization of website traffic

If you have a website that is not of high visibility on the internet, it is worth investigating the reasons why this visibility is low, and again: involved in data science, it studies and analyzes a set of data characterizing the most visited sites on the net and extracts the most useful information for direct application to your website, a technical practice which can be carried out fully by exploiting the possibilities offered by a ModelOps. Thus, managing data and extracting relevant information will be all the more efficient and profitable for your business.

Conclusion 

Data science and business form an inseparable couple. It is not enough to have money to establish a business, so it is therefore inevitable that one must have the knowledge that goes with it, to have the data science that goes with it.

And once you have this data science knowledge, you will have to think about using it in the best possible way by exploiting all the performance and technical options offered by a model and data management platform, especially the ModelOps option.

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