Cloud-native services for machine learning provide a flexible and scalable environment for developing, training, and deploying machine learning models. These services are designed to leverage the cloud’s capabilities, offering features such as managed infrastructure, automated scaling, and integrated tools for data management and experimentation.

Popular cloud-native services include Amazon SageMaker, Google Vertex AI, and Microsoft Azure ML. These platforms allow users to specify instance types, select machine learning frameworks, and provide training scripts, which the service then uses to automatically provision resources, execute training, and manage the lifecycle of the model. This significantly reduces the time and cost associated with setting up and maintaining a dedicated training environment ¹².

However, while these services offer convenience, they may also impose limitations on customization compared to on-premises solutions. Users may encounter challenges related to specific package dependencies or the need for a consistent development environment across different platforms ¹.

In summary, cloud-native services streamline the machine learning process, making it accessible and efficient, but they may require careful consideration regarding flexibility and customization.