How data scientists minimize the “pilot-to-production gap”

When a technology, system or solution is to be successfully transferred from the test or pilot phase to the production environment, there are often difficulties with scaling and performance or even data quality, availability and maintenance.

The development of a state-of-the-art AI model that a data scientist has developed locally on his environment is often not compatible with the operationalization platform and therefore cannot be used directly on the production system. In many cases, successfully developed models are therefore never used in practice. In IT and industry, this gap between pilot project and production (also known as the “pilot-to-production gap” or “pilot-to-deployment gap”) means not only inefficiency and dissatisfaction on the part of data science teams, but also a huge loss of time and effort. The latter in turn result in financial losses for companies.

BYOM as an approach is based on the idea that data scientists can use their familiar tools and languages in their chosen development environment and operationalization environment and thus offers a significant extension of their possibilities in model development and deployment. This allows data scientists to develop and train models in the language of their choice (such as Python, R, SAS and others). This code, including the analytical model, is converted into so-called exchange formats (PMML, ONNX) using a BYOM tool. These formats can then be put into production on large volumes of data. Included in the exchange format is not only the analytical model, but also all data preparation steps. In this way, users save themselves lengthy revisions and can use the models immediately.

For example, a Teradata customer from the entertainment sector was able to create new models with the help of partner tools and import them into its own system using the BYOM function. The optimizations achieved in this way enabled the company to increase the purchase rate by 30 percent and generated profits of 60 million dollars. How is that possible?

BYOM enables adaptability and flexibility

A major advantage of analytics platforms with BYOM capabilities is the flexibility that BYOM offers users and data scientists. The latter can thus use the tools and languages they are used to in any environment. The use of an analytics platform with BYOM capabilities therefore enables companies to fully utilize the skills of their data scientists.

Thanks to BYOM’s adaptability, data scientists in companies can train and harmonize their own models. In addition to the architecture and hyperparameters, all aspects of a model can be optimized and tailored to the individual requirements of companies. This allows more agility and greater control over the training process and model architecture, which in turn means that new approaches can be iterated, implemented and tested more quickly. BYOM therefore enables data scientists to develop models that are precisely tailored to the company’s specific requirements and data. This reduces the risk that ready-made models may not reflect all relevant aspects of the business, which could lead to ineffective or inadequate solutions.

A positive side effect is that BYOM can attract sought-after experts such as data scientists as a “selling point” in the job description or retain them in a company, as they can work with exactly the tools and languages they are used to and know best, thus avoiding the additional time-consuming process of learning other programming languages. In view of the global shortage of IT specialists, BYOM is an investment that pays off in any case.

BYOM offers innovation potential

Another advantage of BYOM lies in its innovation potential: new approaches can be tested independently of external providers without long development or implementation cycles. Thanks to BYOM, existing expertise within the company can be better utilized internally, as the method not only helps with AI, but with all types of analytics: from simple statistical methods to advanced analytics and AI. For example, AI models developed in-house and individually trained for a company can be easily operationalized and continuously developed with the latest findings, data and technologies as well as feedback from the field. This creates customized AI solutions that are differentiated and therefore better tailored to the specific requirements of users and companies. This agility and capacity for innovation allows us to experiment with new ideas, respond with customized solutions and adapt more quickly to constantly changing market requirements. In this way, BYOM can help to promote innovation and create significant competitive advantages.

BYOM reduces pilot-to-production gap

When data scientists develop models, the aim is not only to find an interesting solution, but also to ensure that this solution is actually integrated into business operations and used to create real added value. Another key advantage of analytics platforms with BYOM capabilities for companies is therefore the minimization of risk in the creation and operationalization of models. Since BYOM allows data scientists to work in their familiar environment even after deployment, they retain full control over the entire development process of their models, including data processing, training and validation. This in turn allows errors to be rectified at an early stage and the quality and accuracy of the models to be optimized. In the long term, this can lead to savings in development costs. Thanks to BYOM, data scientists can continuously monitor and adapt their AI models and ensure that they generate added value in the long term. This includes, for example, monitoring model performance in production, receiving feedback from end users or updating models based on new data.

One of BYOM’s main objectives is to reduce the gap between the development of models in the pilot phase and their successful deployment in the production environment. By developing and integrating their own models, data scientists can ensure that the models are transferred smoothly into operations and thus create real added value for the company. Such customized models also have a higher probability of being successful in the production environment – thus realizing the value potential of analytics and AI.

Conclusion

BYOM allows data scientists to develop models in their familiar environment and in familiar languages, enabling companies to fully utilize the skills of the data scientist team. This leads to increased efficiency and productivity and at the same time to an increase in employee satisfaction, as the results of the data scientists are actually used and the pilot-to-production gap is closed.

(pd/Teradata)