AI and generative Al can lead to major enterprise advancements and productivity gains. By offering new capabilities, they open up opportunities for enhancing customer engagement, content creation, virtual experts, process automation and optimization, and more.

A sub-discipline of artificial intelligence (AI), machine learning (ML) focuses on the development of algorithms to build systems capable of learning from, and making decisions based on, data. In iOS development, ML allows us to create applications that can identify patterns and make predictions, adapting a user’s experience by learning from their behaviour.

As demand for machine learning (ML) grows, rigorous testing and quality assurance are crucial. ML models need quality training data and robust algorithms. Without thorough testing, inaccurate outcomes can occur, especially in sectors like healthcare, finance, and transportation. A 2023 ScienceDirect report found data leakage in 294 academic publications across 17 disciplines, highlighting the need to address this issue in ML-based science.

RAG and Fine-Tuning are two prominent LLM customization approaches. While RAG involves providing external and dynamic resources to trained models, fine-tuning involves further training on specialized datasets, altering the model. Each approach can be used for different use cases. In this blog post, we explain each approach, compare the two and recommend when to use them and which pitfalls to avoid.

As can be inferred from their name, foundation models are the foundation upon which developers build AI applications for tasks like language translation, text summarization, sentiment analysis and more. Models such as OpenAI's GPT, Google's Gemini, Meta’s Llama and Anthropic’s Claude, are pre-trained on vast amounts of text data and have the capability to understand and generate human-like language.

Written by Noam Wasersprung, Head of Product at ClearML Last month we released the Resource Allocation & Policy Management Center to help teams visualize their compute infrastructure and understand which users have access to what resources. This new feature makes it easy for administrators to visualize their resource policies for enabling workload prioritization across available resources.