Articles tagged with "sagemaker"

An unsung hero of Amazon SageMaker: Local Mode

Amazon SageMaker offers a highly customizable platform for machine learning at scale. Job execution within Amazon SageMaker can take some time to set up, which can be inconvenient or even time consuming during development and debugging phases. Running training and processing jobs locally can greatly increase the speed of development and debugging before running them at scale on AWS.

Understanding Iterations in Ray RLlib

Recently I’ve been engaged in my first reinforcement learning project using Ray’s RLlib and Sagemaker. I had dabbled in machine learning before, but one of the nice things about this project is that it allows me to dive deep into something unfamiliar. Naturally, that results in some mistakes being made. Today I want to share a bit about my experience in trying to improve the iteration time for the IMPALA algorithm in Ray’s RLlib.

Having fun @work: AWS GameDay

Joining an AWS Training allows you to learn new things for your daily work. Attending a training commonly happens in groups of up to 13 people and has more of a frontal teaching character. An alternative event are workshops are more practical and done in a small group. And now, a third solution brings teams and people together and plays a competitive game: AWS GameDays.

Amazon SageMaker ist mehr als Machine Learning in python - er kann auch Teaching in go

Haben Sie nicht auch schon mal beim Durchlesen von Code Anleitungen gedacht, wie schön dass wäre, wenn die Anleitung und der Code zusammen ausführbar wären? Nun, genau das kann Amazon SageMaker! Amazon SageMaker unterstützt nicht nur bei der Erstellung von Code und Modellen für Machine Learning. Das “literal Programming”, also dokumentenzentrierte Programmierung kann auch mit anderen Sprachen, z.B. go/golang verwendet werden, um Code und Dokumentation als Paket zu verwenden. Hier ein Beispiel, wie man die jupyter Notebooks mit go verwendet: