When deciding between using a platform and deploying your own machine learning algorithm on AWS, the choice depends on several factors including your team's expertise, the complexity of the project, and your long-term goals.
A platform, such as AWS SageMaker, offers a managed environment where you can build, train, and deploy machine learning models without worrying about the underlying infrastructure. This is beneficial if your team lacks deep expertise in managing servers and infrastructure. SageMaker simplifies the process by providing pre-built algorithms, frameworks, and tools that can accelerate the development cycle. For example, if you're developing a model to predict customer churn, SageMaker's built-in algorithms can be quickly utilized to start the project.
On the other hand, deploying your own machine learning algorithm on AWS EC2 instances gives you full control over the environment and customization options. This approach is suitable if your project requires specific configurations or optimizations that aren't available in pre-built platforms. It also allows for more flexibility in integrating with other services or custom data processing pipelines. For instance, if you're working on a highly specialized image recognition model that needs custom hardware acceleration, deploying on EC2 instances with GPU support would be ideal.
In summary, if you prioritize speed and ease of use without needing extensive customization, a platform like AWS SageMaker is preferable. If you require maximum control and customization, deploying your own algorithm on AWS EC2 might be the better choice.