This will be our third and final installment covering machine learning in AWS! Part I Part II
In today’s post we are going to pick up where we left off exploring Sagemaker’s machine learning libraries!
AWS Sagemaker is the heart of machine learning in AWS and is intended to handle the entire machine learning workflow from building, training, and deploying to production. Features ranges from offering Sagemaker notebooks for data preparation and feature engineering to spinning up EC2 instances to train models on large datasets.
Machine learning in the cloud is rapidly becoming more popular for data scientists and corporations. By utilizing a Platform-as-a-Service (PaaS), such as Amazon Sagemaker, approach to machine learning there is no need to worry about the data storage and networking reguirements necessary to execute complex machine learning jobs. A PaaS approach to machine learning provides pre-configured environments on which a data scientist may train and host predictive models. For large scale jobs, PaaS ML eases the headache of running distributed machine learning jobs.
“Big data is like teenage sex: everyone talks about it, nobody really knows how to do it, everyone thinks everyone else is doing it, so everyone claims they are doing it…” - Dan Ariely, Duke Professor of Psychology and Behavioral Economics, Duke University