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How Vidmob is using generative AI to transform its creative data landscape

This post was co-written with Mickey Alon from Vidmob. Generative artificial intelligence (AI) can be vital for marketing because it enables the creation of personalized content and optimizes ad targeting with predictive analytics. Specifically, such data analysis can result in predicting trends and public sentiment while also personalizing customer journeys, ultimately leading to more effective …

Tips for Effective Feature Selection in Machine Learning

When training a machine learning model, you may sometimes work with datasets with a large number of features. However, only a small subset of these features will actually be important for the model to make predictions. Which is why you need feature selection to identify these helpful features. This article covers useful tips for feature …

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Build powerful RAG pipelines with LlamaIndex and Amazon Bedrock

This post was co-written with Jerry Liu from LlamaIndex. Retrieval Augmented Generation (RAG) has emerged as a powerful technique for enhancing the capabilities of large language models (LLMs). By combining the vast knowledge stored in external data sources with the generative power of LLMs, RAG enables you to tackle complex tasks that require both knowledge …

Language-like communication improves learning in artificial networks, finds study

Across all species, critical skills are passed on from parents to offspring through communication. Researchers at the University Hospital Bonn (UKB) and the Researchers at the University of Bonn showed that effective communication relies on how both the sender and receiver represent information. Their study reveals how this process underlies training efficacy and task performance. …

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Deploy Amazon SageMaker pipelines using AWS Controllers for Kubernetes

Kubernetes is a popular orchestration platform for managing containers. Its scalability and load-balancing capabilities make it ideal for handling the variable workloads typical of machine learning (ML) applications. DevOps engineers often use Kubernetes to manage and scale ML applications, but before an ML model is available, it must be trained and evaluated and, if the …