What are possible application of ML in the field of electric power systems?
Machine learning (ML) has emerged as a transformative technology in the field of electric power systems, providing advanced analytical and predictive capabilities that augment traditional engineering approaches. The application of ML in this domain leverages large volumes of data generated by modern power grids, sensors, and customer consumption patterns, enabling utilities and grid operators to
What is the difference between algorithm and model?
In the context of artificial intelligence and machine learning, particularly as addressed within Google Cloud's machine learning frameworks, the terms "algorithm" and "model" have specific, differentiated meanings and roles. Understanding this distinction is fundamental for grasping how machine learning systems are built, trained, and deployed in real-world applications. Algorithm: The Recipe for Learning An algorithm
What is an optimisation algorithm?
An optimisation algorithm, within the context of machine learning, refers to a systematic mathematical process or procedure used for adjusting the internal parameters of a machine learning model to improve its performance on a specific task. The primary goal of an optimisation algorithm is to find the optimal values of these parameters—commonly known as weights
What is artificial intelligence and what is it currently used for in everyday life?
Artificial intelligence (AI) refers to the field of computer science devoted to the creation of systems capable of performing tasks that typically require human intelligence. These tasks include reasoning, learning, problem-solving, perception, language understanding, and decision-making. AI encompasses a broad spectrum of subfields, including machine learning, natural language processing, computer vision, robotics, and expert systems.
What basic differences exist between supervised and unsupervised learning in machine learning and how is each one identified?
Supervised and unsupervised learning constitute two fundamental approaches in machine learning, each characterized by the nature of the data they operate on and the objectives they pursue. An accurate understanding of their basic differences is vital when embarking on any study or practical implementation of machine learning systems, particularly within educational courses that introduce foundational
In order to train algorithms, what is the most important: data quality or data quantity?
The question of whether data quality or data quantity holds greater importance in training algorithms is central to the practice of machine learning. Both factors significantly influence model performance, but their relative importance varies depending on the context, the type of algorithm, and the application domain. To provide a comprehensive and factual perspective, it is
- Published in Artificial Intelligence, EITC/AI/GCML Google Cloud Machine Learning, First steps in Machine Learning, The 7 steps of machine learning
Is machine learning, as often described as a black box, especially for competition issues, genuinely compatible with transparency requirements?
The compatibility of machine learning (ML) with transparency requirements—especially in the context of competition law and regulatory oversight—presents a complex interplay of technical, ethical, and legal considerations. The frequent reference to ML systems as “black boxes” reflects the difficulty stakeholders often face in understanding, interpreting, and governing the decisions made by these systems. To address
Is preparing an algorithm for ML difficult?
The process of preparing an algorithm for machine learning (ML) is a multifaceted endeavor that encompasses several distinct stages, each presenting its own set of challenges. The complexity of this task varies depending on factors such as the nature of the problem, the quality and quantity of available data, the required level of accuracy, and
What is agentic AI with its applications, how it differs from generative AI and analytical AI and can it be implemented in Google Cloud?
Agentic AI: Definition, Applications, Comparisons, and Implementation on Google Cloud Agentic AI refers to artificial intelligence systems endowed with the capacity to initiate, plan, and execute actions autonomously in pursuit of goals, often by interacting with an environment or other software systems through multi-step decision making. The term “agentic” is derived from “agency”, signifying the
- Published in Artificial Intelligence, EITC/AI/GCML Google Cloud Machine Learning, Introduction, What is machine learning
Can the Pipelines Dashboard be installed on your own machine?
The Pipelines Dashboard, often associated with Google Cloud AI Platform Pipelines (now Vertex AI Pipelines), is a web-based user interface designed for visualizing, managing, and monitoring machine learning (ML) workflows executed as pipelines. The dashboard allows users to view pipeline runs, inspect component outputs, monitor execution status, and interact with artifacts generated throughout the ML
- Published in Artificial Intelligence, EITC/AI/GCML Google Cloud Machine Learning, Google Cloud AI Platform, Setting up AI Platform Pipelines