What is an AI Engineer?
Organizations are discovering the profound impact that artificial intelligence (AI) and machine learning have on their business. Business analytics professionals are evolving their skills to become citizen data scientists, joining forces with traditional data scientists to build machine learning models that provide insight and recommendations about future decisions — decisions that were unfathomable just a few years ago. In order to truly become an AI-driven enterprise, an organization must embed AI into its applications so that everyone in the organization has access to insight and is empowered to make better, faster decisions.
AI-driven organizations are creating the role of AI engineer and staffing it with people who can perform a hybrid of data engineering, data science, and software development tasks. Unlike data engineers, AI engineers don’t write code to build scalable data pipelines and often don’t compete in Kaggle competitions. Instead, AI engineers extract data efficiently from a variety of sources, build and test their own machine learning models, and deploy those models using either embedded code or API calls to create AI-infused applications.
Why are AI Engineers Important?
AI engineers tackle the unique design challenges that result from combining the logic found in traditional applications with the learned logic from machine learning models.
These considerations include:
- Working with a variety of different infrastructure types, including chips (GPUs, FPGAs, etc.), on-premises systems, and the cloud.
- Understanding how the process of machine learning (feature engineering, model building, and model validation, to name a few) adapts to support continuous development pipelines.
- Deciding when a model is ready for deployment and monitoring its accuracy over time to see when it needs to be retrained or replaced.
An organization’s top software engineers are best positioned to evolve into AI engineers because they are most likely to have a full-stack application development background and experience with embedding machine learning algorithms. Computer science majors fresh from college will also fill some demand for AI engineers with their combination of programming experience, strong math and statistics fundamentals, and data science skills honed by choosing machine learning as their preferred elective.
DataRobot + AI Engineers
Software developers and computer science majors can accelerate their transformation into AI engineers by using DataRobot’s automated machine learning capabilities. DataRobot replicates best practices of the world’s top data scientists for data preparation and preprocessing, feature engineering, and model training and validation. Unlike a traditional data scientist, DataRobot applies dozens of different machine learning algorithms in minutes via its model blueprint functionality and automatically ranks the most appropriate algorithms (or ensembles of algorithms) based on the training data and the target variable.
Since every model DataRobot builds is production-ready, AI engineers can quickly add machine learning capabilities to existing systems like ERPs, CRMs, RDBMSs, and more. They can use DataRobot’s RESTful API and just a few lines of code to support real-time predictions or batch deployments. AI engineers can also download their models in native Python or Java code to insert directly into their applications.