Through the years, I actually have been involved in implementing many “smart software” projects that demonstrated high advantages to major organizations. At the center of those different software projects were algorithms based on Mathematical Programming, Simulation, and Heuristics, in addition to AI models based on ML and generative AI. Most of those projects led to substantial ROI for these organizations; some have even shaped their company’s future.

Despite all of the hype around AI and Data, many organizations (outside of the software industry)  struggle to implement a successful AI strategy. Most CIOs/CDOs involved have mostly produced “standard” data projects (data lakes/warehouses/data management/Dashboarding), some implemented several AI pilots, and only a few have generated deployed projects showing substantial ROI for his or her company. 

One could consider the distribution of corporations when it comes to AI penetration as a highly left-skewed fat-tail distribution.

The aim of this text shouldn’t be to list all of the obstacles stopping the broader penetration of AI projects inside corporations. For this purpose, I’d recommend these two enlightening articles:

Why businesses fail at Machine Learning 

How AI can assist leaders make higher decisions under pressure 

As a substitute, we give attention to two gaping holes in the present software implementation approach.

Gaping hole 1: A really siloed Environment

Visualizing the varied groups involved in a typical AI project is interesting.

After all, there are valid reasons for having these different roles, let alone the necessity for specialization. Nevertheless, it’s price noting that:

• On an actual project, the gap between the information scientists and end-users is substantial.
• Each silo uses different technology stacks. It shouldn’t be unusual for data scientists to develop mainly in Python, while IT developers use JavaScript, Java, Scala, etc.
• There has never been a greater variety of programming skills between and inside each siloes.

Gaping hole 2: Getting acceptance from the end-users / business-users

As highlighted in a previous article, end-users appear to have disappeared from the AI landscape. It’s all about data, technologies, algorithms, testing, deployment, etc. As if all AI projects will necessarily replace completely human experts. I’m convinced that the longer term of AI within the industry lies within the hybrid collaboration between business users and AI software. 

Nevertheless, end-users are an integral a part of AI software development. Not getting them fully involved in the course of the development process puts you prone to not having your software used when the system goes live. 

Our strategy is to be sure that these two steps get implemented:

1. A smooth end-user Interaction with the algorithm(s)
2. And a straightforward tracking of business-user satisfaction

How you can fill Gap 1? 

Some obvious directions are:

1. To standardize as much as possible on a single programming language.
2. Provide an easy-to-learn/use programming experience to cater to all programming levels. 

Python is the best candidate for this. It’s at the center of the AI stack and ideal for integrating with other environments.

Many Python libraries can be found and supply a simple learning curve (including low code); unfortunately, they often suffer from performance issues and lack of customization.

Let’s consider, as an example, the event of graphical Interfaces: One has the selection of using full-code libraries like Plotly Dash (and even development in Java Script) or easy-to-develop libraries like Streamlit or Gradio. Nevertheless, these libraries don’t scale performance-wise and can set you right into a strict framework forbidding most customization. 

A Python developer shouldn’t should arbitrage a lot between programming productivity and performance/customization.

We spent lots of time on the design/implementation of our product, Taipy, to go one step further by guaranteeing ease of development while providing an enormous leap in performance and customization. Listed here are two examples of performance issues (amongst many others) solved with Taipy:

How you can fill Gap 2?

 Addressing the 2 salient points mentioned above is crucial:

1. A smooth end-user Interaction with the back-end algorithm(s)
2. And a straightforward tracking of the business-user satisfaction

Addressing Point 1: the end-user must interact with the algorithm/back-end. 

For this purpose, it is important to:

• Provide variables/parameters that the end-user can control through the GUI.
• Allow the end-user to execute backend algorithms using these different parameter values, resulting in different results.
• Provide the chance to check these different runs and track KPI performance over time.

In Taipy, now we have introduced the ‘scenario’ concept that addresses the entire above requirements.

A scenario consists of the execution of the algorithm/pipeline where Taipy stores all the information elements (data sources, data outputs)

Taipy’s scenario registry enables the end-user to:

• keep track of all of its runs, 
• revisit a past scenario, understand its results, scan its input data, etc.

Addressing Point 2: easy tracking of the business-user satisfaction

One other great good thing about Taipy’s Scenario function is that it reduces the gap between the end-user and the information scientists. The Taipy scenario registry is a gold mine for data scientists since they’ll access all end-user’s runs. As well as, the end-user can tag any of those scenarios and share them with the information scientists for examination.

This scenario feature can dramatically increase the software’s acceptance by the end-user. Unfortunately, in practice, testing AI algorithms is mostly limited to a couple of test cases and the usage of drift detection. More is required to ensure a high acceptance of the software. And Taipy’s scenarios will help lots here.

Listed here are some examples of Taipy AI applications enabling the business user to explore previously generated scenarios.

Conclusion

To conclude with, Taipy has proven instrumental within the success of AI projects for leading corporations, offering an efficient and user-friendly Python framework. With the launch of Taipy Designer, we proceed to democratize AI development, specializing in accessibility for Data Analysts and ensuring the seamless integration of AI into business processes.

This text was originally published on Taipy.

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