AI projects: Why should you have several tools when one is enough?

AI projects are complex and involve many steps: from data collection to predictive analysis. Several tools seem to be necessary to exploit the potential of data: an ETL, a visualisation tool, AI and machine learning algorithms, an interpretation or BI solution, etc. Yet solutions exist to simplify and desecrate AI processes. papAI, Datategy’s AI platform, is one of them. Let’s take a closer look at its benefits.

AI projects

Covering the whole cycle of an AI project

A company’s data assets are often scattered among different information systems, business lines, subsidiaries, etc. The formats, languages, sources, etc. are heterogeneous, as are the tools to be used. These are all obstacles to the success of an AI project using this data. 

With the papAI platform, you have a unique tool that allows you to collect (from several databases such as PostgreSQL, mySQL, Oracle, MicrosoftSQL, from CSV files, or from APIs), sort, make intelligible, use and analyse all data. Thus, you do not need to multiply the processing, the data can be exploited in its original format and coupled with other raw information without qualification work. You don’t need to interconnect many tools either: you speed up your AI processes to get the results as easily and quickly as possible. For the restitution, there is no need to transfer the results to a Business Intelligence tool, the results can be visualised, understood and interpreted: an immediate lever for decision making. 

Many algorithms available to data scientists

Unlike specialised and verticalised AI tools, the papAI platform has a wide variety of pre-packaged algorithms that can be used for any type of project, regardless of the business or sector. Among them, we find time series. The platform integrates classical time series models like ARIMA, which are widely used in finance and economics. But papAI also offers more advanced time series such as neural networks (LSTM type). 

About fifteen distinct families that evolve with trends and needs are thus available within the platform. This is the advantage of a flexible solution, which enriches and brings the algorithms to life.  

All these algorithms can be used to cover numerous business use cases. They provide your data scientists with models that can be easily deployed and adjusted to your company. This saves time and performance. 

papAI, the MLOps platform

Another important advantage of papAI is that the platform is MLOps – a major trend in the software development sector. MLOps is based on the enrichment of machine learning technologies and allows for the rapid release of algorithms.

This concept works on the same model as DevOps: it is based on agile methods to facilitate and automate the testing of algorithms before they are put into rapid production. Some business processes are constantly evolving or being enriched with new data. AI models must therefore be able to adapt. This is where MLOps comes into its own. 

papAI is fully in line with this approach. The platform offers data scientists a solution for developing algorithms quickly. An ideal approach for employees who excel in statistics, but do not want to waste time developing from scratch. With this low-code approach, data scientists have all the models prepared and can develop them according to their needs. A real plus for increasingly agile projects. 

The advantages of papAI are numerous (we invite you to read the article on this subject). But its main asset is the centralisation of data-driven predictive analysis projects in a single, accessible, ergonomic and, above all, high-performance tool. 

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AI projects: why have several tools when one is enough?
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