Artificial Intelligence & Machine Learning

MLOps, or Machine Learning Operations, revolutionises the deployment and maintenance of ML models by merging DevOps principles with the machine learning lifecycle. It encompasses training, testing, deploying, and monitoring, aiming to automate these processes for improved reliability and efficiency.

Supercomputers are extremely fast, powerful computers that can solve complex problems that regular computers cannot handle. They are used in various fields, such as science, engineering, and business, for research, design, and data analysis.

AI prompt engineering combines art and science, guiding language models to produce the best outcomes. It's akin to choosing words carefully to instruct a brilliant yet literal-minded friend. Prompt engineers apply their AI understanding and creativity to craft queries for generating text, translating languages, and more, ensuring informative and inventive responses.

Generative AI, a branch of AI focusing on content creation, uses neural networks to generate new output from prompts. It learns from vast data to produce text, images, and more, mimicking human creativity. Businesses use it for marketing, customer support, and analytics, enhancing productivity and decision-making.

Unsupervised learning algorithms discover hidden patterns in unlabeled data. Without predefined outputs, they identify inherent structures, groupings, or relationships. Key types include clustering, dimensionality reduction, and anomaly detection, used for customer segmentation, data simplification, and fraud detection

Retrieval-augmented generation (RAG) enhances large language models (LLMs) by combining their generative abilities with external knowledge sources, leading to more accurate, up-to-date, and contextually relevant responses.

Supervised learning is a machine learning type where algorithms learn from labeled data, much like a student learns with a teacher. The goal is to predict outputs for new, unseen inputs by identifying patterns in the provided examples.

Reinforcement Learning from Human Feedback (RLHF) refines AI models, particularly LLMs, using human preferences. It trains a separate reward model on human feedback and then uses this model within a reinforcement learning algorithm to guide the central AI towards more helpful and aligned behaviour.

Reinforcement learning (RL) is a machine learning technique where agents learn to make decisions by interacting with an environment, receiving rewards for positive actions. This trial-and-error approach allows RL agents to optimize their behaviour over time, leading to impressive achievements in robotics, game playing, healthcare, and finance.

Quantum machine learning is a fusion of machine learning and quantum computing, which uses quantum computers to analyse complex data and solve problems at unprecedented speeds, opening up new perspectives in fields such as the pharmaceutical industry, finance, and technology.

Quantum computing has the potential to change the game while revolutionising the computing landscape. While quantum computing can quickly solve complex problems that conventional computing cannot effectively handle, quantum computing is far from meeting expectations and many challenges remain.

Predictive analytics leverages historical data, statistical algorithms, and machine learning to forecast future events, trends, or behaviours. It shifts focus from past occurrences to likely future outcomes, answering "What's next?". This empowers organisations to make proactive, data-driven decisions.

This article provides a comprehensive overview of overfitting in machine learning, explaining what it is, why it happens, its consequences, how to detect it, and strategies to prevent it.

Machine learning is a sub-field of artificial intelligence (AI). It focuses on the development of algorithms and statistical models that allow a computer system to perform tasks without specific programming for them. In other words, instead of coding each instruction, we allow the machine to learn from information and improve its performance over time.

This guide delves into the different types of machine learning algorithms: supervised, unsupervised, and reinforcement learning, each suited for distinct tasks. It also explores use cases like customer churn prediction, sales forecasting, image recognition, recommendation systems, and fraud detection, highlighting relevant algorithms.

A Large Language Model (LLM) is an advanced artificial intelligence system, specialised in natural language processing, capable of understanding, generating and interacting with human text by learning through vast textual data sets.

Deep learning is a machine learning method through which the machine is able to learn and improve on its own. This technique is based on artificial neurones, image recognition technologies and language recognition.

Computer vision is a field of AI that enables machines to interpret visual information like humans. It has diverse applications, from medical imaging and self-driving cars to manufacturing and augmented reality. Learning resources like OpenCV, TensorFlow, and online courses can help you get started.

Cognitive search uses AI to understand user intent, going beyond keyword matching. Natural language processing, machine learning, and knowledge graphs enable insightful results. It enhances relevance, accuracy, and user experience across healthcare, finance, law, and more, transforming data into actionable insights.

Natural Language Processing (NLP) is a specialised field of artificial intelligence that enables machines to understand, interpret, and generate human language. Using computational and linguistic techniques, NLP facilitates applications like virtual assistants, text analytics, and machine translation, transforming vast amounts of unstructured data into meaningful insights.

Automated Machine Learning (AutoML) simplifies and accelerates building machine learning models by automating tasks like data preprocessing, feature engineering, algorithm selection, and hyperparameter optimization. This makes AI more accessible, efficient, and potentially improves model performance with minimal human intervention.

Artificial intelligence (AI) is becoming increasingly linked to the digital technologies we use every day, and has consistently improved their effectiveness. AI assimilates a lot of information with the data it receives. It then analyses this data to deliver the best possible action.

Artificial General Intelligence (AGI) aims to create machines with human-like cognitive abilities. AGI research explores machine learning, natural language processing, and ethical implications. While AGI remains elusive, advancements bring us closer to a future where machines possess human-level intelligence.

AI inference uses trained models to make predictions on new data. It involves inputting data, processing it based on learned patterns, and generating an output such as a classification or prediction. This process is used in various applications, such as image recognition and fraud detection.

AI agents are revolutionizing technology by enabling autonomous actions and goal-oriented behavior. They are used in various applications, from chatbots to self-driving cars. OVHcloud offers solutions to support AI development and deployment.

Artificial intelligence (AI) infrastructure is the technological foundation for organizations to deploy, manage and optimize AI solutions. This on a large scale. Whether it’s massive data, complex models, or the need for high computing power via GPUs, every AI project requires a robust, suitable infrastructure.

Machine learning, a subset of artificial intelligence, has an enormous impact on our everyday lives and tasks, and its role is increasing as the enterprise network becomes more sophisticated. Machine learning is defined as the process of a computer solving problems using algorithms.