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Machine Learning

Swift Machine Learning: Using Apple Core ML

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.

Ensuring Accuracy and Reliability with ML Model Validation

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 vs Fine-Tuning: Navigating the Path to Enhanced LLMs

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.

Commercial vs. Self-Hosted LLMs: A Cost Analysis & How to Choose the Right Ones for You

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.

Manage Resource Utilization and Allocation with ClearML

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.

S1.E8: AI & Machine Learning in Testing | QA Therapy Podcast

Feeling like your team is pinning all their hopes on AI and ML to solve every challenge? In this episode of QA Therapy, we're thrilled to have Tariq King, QA Therapist, join us to explore how AI and ML will shape the future of testing. Tariq, currently serving as the Vice President of Product-Service Systems at EPAM, with over 40 research articles under his belt.

Unparalleled Productivity: The Power of Cloudera Copilot for Cloudera Machine Learning

In the fast-evolving landscape of data science and machine learning, efficiency is not just desirable—it’s essential. Imagine a world where every data practitioner, from seasoned data scientists to budding developers, has an intelligent assistant at their fingertips. This assistant doesn’t just automate mundane tasks but understands the intricacies of your workflows, anticipates your needs, and dramatically enhances your productivity at every turn.