Articles tagged with "sagemaker"

Retrieval-Augmented Generation (RAG) solutions are an impressive way to talk to one’s data. One of the challenges of RAG solutions is the associated cost, often driven by the vector database. In a previous blog article I presented how to tackle this issue by using Athena with Locality Sensitive Hashing (LSH) as a knowledge database. One the of the main limitations with Athena is the latency and the low number of concurrent queries. In this new blog article, I present a new low-cost serverless solution that makes use of an embedded vector database, SQLite, to achieve a low cost while maintaining high concurrency.

Large language models (LLMs) can generate complex text and solve numerous tasks such as question-answering, information extraction, and text summarization. However, they may suffer from issues such as information gaps or hallucinations. In this blog article, we will explore how to mitigate these issues using Retrieval Augmented Generation (RAG) and build a low-cost solution in the process.

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.

Finding the right set of hyperparameters when training machine learning models is a resource-consuming and costly task. In this post, we try to simplify this by exploring hyperparameter tuning for reinforcement learning models with Ray 2.x on AWS Sagemaker.

A few years ago Amazon SageMaker introduced direct support for reinforcement learning (RL) through integration of RL-frameworks, including Ray. However, support has not been kept up to date and the supported versions are no longer what you might call current.

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.

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.

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: