in the last post, we talked about how to deploy a Machine learning trained model on Kubernates. Here is another way of deploying ML models: AWS lambda + API gateway Basically, your model (mlpreg.pkl) will be stored in S3, your lambda function will download the model use it to make predictions, another call will allow […]

With the rise of Machine Learning and models, the need for automating and streamlining model deployment become a necessity. Pushed mostly by the fact that ML models as a new way of programming, are no longer an experimental concept but rather a day to day artifacts that can also follow a release and versioning process. […]