.png){kind=logo}
- Insights
- Cryptocurrencies
- Stocks
- White Papers
- Industry
- Geography
- Insights
- Cryptocurrencies
- Stocks
- White Papers
- Industry
- Geography
Data is becoming an integral part of business systems in the modern world primarily because of the increasing dependence on technology. This has resulted in the formation of a new position in a typical data science team-artificial intelligence (AI) software engineer. A potential justification of this move is the increasing adoption of AI and robotics technology in the modern business landscape. Having such a position brings on-board much more focus on current technology allowing the team to focus on much more relevant data. For instance, the engineer can provide useful facts and information on the AI software development process providing new perspectives that can lead to improvements in the technology.
Roles of an AI software Engineer in a Data Science Team:
The data science team role in the business landscape is changing over the years because of technology. For instance, the amount of data companies output in a single business day is increasing as an outcome of adopting Big Data. This necessitates the employment of data analysis software and technology to supplement the data science team's infrastructure. As such, the engineer can facilitate the software development process to ensure that the resulting software suits the needs of the team.
The quality of the work of the data science team, just like any other profession, is only as good as the tools that they use. Without an AI software engineer, the need to keep the software and tools up to date is difficult. Incorporating the software development process as well as TFS or GIT into the daily operations of a data science project can guarantee positive outcomes for the team. When the model for the project is created, numerous iterations, as well as different updates that are very difficult to keep track, comes into play. Therefore, an IT software engineer can be instrumental in implementing integration and versioning control processes for the data science team's tools.
Before the work of the data science team can be published or introduced to the general population, be it a model or a fully functional integrated application, sufficient testing is necessary. This role is an integral part of the software development process in which an AI software engineer is well vast. That makes the position holder the best person to test out the project outputs of the data science team.
The purpose of APIs is to aid in the integration of data sources and products into applications. This makes it a vital aspect of the work that the data science teams produce. It is the role of the engineer to ensure that a platform that can render the conversion of models into APIs is built and maintained. In this way, standardization of the API customization process can be developed leading to high-quality output.
The AI software engineer is a vital element of a data science team because of the conditions of the business landscape today. While these teams have managed to overcome challenges over the years without this role, changing times require adaptation to ensure that they can meet the growing demand for their services as well as the increasing complexity of the scope of their work. Perhaps, as technology grows in complexity, the purpose of this new role will become much more apparent.
