Discover the challenges and learnings from attempting to implement a data versioning pipeline using AWS Lambda and DVC. This blog shares a real-world experience of navigating the pitfalls of combining these technologies for a cloud-based solution.
In this blog post, I explore the features and use cases of AWS SageMaker in the field of Machine Learning. Specifically, I focus on its capabilities that enable early deployment of ML models, significantly reducing the time to production. Discover how SageMaker simplifies the process of building, training, and deploying models, and learn about its standout features, such as infrastructure/resource management, experiment management, the SageMaker Python SDK, and more. Find out about the cost and learning curve associated with SageMaker, and gain insights into its competition with Google Cloud Datalab and Microsoft Azure Machine Learning Studio