Artificial Intelligence
Improve AI assistant response accuracy using Knowledge Bases for Amazon Bedrock and a reranking model
AI chatbots and virtual assistants have become increasingly popular in recent years thanks the breakthroughs of large language models (LLMs). Trained on a large volume of datasets, these models incorporate memory components in their architectural design, allowing them to understand and comprehend textual context. Most common use cases for chatbot assistants focus on a few […]
Fine-tune Llama 2 using QLoRA and Deploy it on Amazon SageMaker with AWS Inferentia2
In this post, we showcase fine-tuning a Llama 2 model using a Parameter-Efficient Fine-Tuning (PEFT) method and deploy the fine-tuned model on AWS Inferentia2. We use the AWS Neuron software development kit (SDK) to access the AWS Inferentia2 device and benefit from its high performance. We then use a large model inference container powered by […]
Use integrated explainability tools and improve model quality using Amazon SageMaker Autopilot
Whether you are developing a machine learning (ML) model for reducing operating cost, improving efficiency, or improving customer satisfaction, there are no perfect solutions when it comes to producing an effective model. From an ML development perspective, data scientists typically go through stages of data exploration, feature engineering, model development, and model training and tuning […]


