How to practically train and deploy simple AI model in Google Cloud AI Platform via the GUI interface of GCP console in a step-by-step tutorial?
Google Cloud AI Platform offers a comprehensive environment to build, train, and deploy machine learning models at scale, utilizing the robust infrastructure of Google Cloud. Utilizing the GUI of the Google Cloud Console, users can orchestrate workflows for model development without needing to interact directly with command-line tools. The step-by-step tutorial below demonstrates how to
What are the main requirements and the simplest methods for creating a natural language processing model? How can one create such a model using available tools?
Creating a natural language model involves a multi-step process that combines linguistic theory, computational methods, data engineering, and machine learning best practices. The requirements, methodologies, and tools available today provide a flexible environment for experimentation and deployment, especially on platforms like Google Cloud. The following explanation addresses the main requirements, the simplest methods for natural
How to apply the 7 steps of ML in an example context?
Applying the seven steps of machine learning provides a structured approach to developing machine learning models, ensuring a systematic process that can be followed from problem definition to deployment. This framework is beneficial for both beginners and experienced practitioners, as it helps in organizing the workflow and ensuring that no critical step is overlooked. Here,
How to use Fashion-MNIST dataset in Google Cloud Machine Learning / AI Platform?
Fashion-MNIST is a dataset of Zalando's article images, consisting of a training set of 60,000 examples and a test set of 10,000 examples. Each example is a 28×28 grayscale image, associated with a label from 10 classes. The dataset serves as a direct drop-in replacement for the original MNIST dataset for benchmarking machine learning algorithms,
What are some more detailed phases of machine learning?
The phases of machine learning represent a structured approach to developing, deploying, and maintaining machine learning models. These phases ensure that the machine learning process is systematic, reproducible, and scalable. The following sections provide a comprehensive overview of each phase, detailing the key activities and considerations involved. 1. Problem Definition and Data Collection Problem Definition
In TensorFlow 2.0 and later, sessions are no longer used directly. Is there any reason to use them?
In TensorFlow 2.0 and later versions, the concept of sessions, which was a fundamental element in earlier versions of TensorFlow, has been deprecated. Sessions were used in TensorFlow 1.x to execute graphs or parts of graphs, allowing control over when and where the computation happens. However, with the introduction of TensorFlow 2.0, eager execution became
- Published in Artificial Intelligence, EITC/AI/DLTF Deep Learning with TensorFlow, TensorFlow, TensorFlow basics
Is TensorFlow lite for Android used for inference only or can it be used also for training?
TensorFlow Lite for Android is a lightweight version of TensorFlow specifically designed for mobile and embedded devices. It is primarily used for running pre-trained machine learning models on mobile devices to perform inference tasks efficiently. TensorFlow Lite is optimized for mobile platforms and aims to provide low latency and a small binary size to enable
- Published in Artificial Intelligence, EITC/AI/TFF TensorFlow Fundamentals, Programming TensorFlow, TensorFlow Lite for Android
How can one start making AI models in Google Cloud for serverless predictions at scale?
To embark on the journey of creating artificial intelligence (AI) models using Google Cloud Machine Learning for serverless predictions at scale, one must follow a structured approach that encompasses several key steps. These steps involve understanding the basics of machine learning, familiarizing oneself with Google Cloud's AI services, setting up a development environment, preparing and
How does one implement an AI model that does machine learning?
To implement an AI model that performs machine learning tasks, one must understand the fundamental concepts and processes involved in the machine learning. Machine learning (ML) is a subset of artificial intelligence (AI) that enables systems to learn and improve from experience without being explicitly programmed. Google Cloud Machine Learning provides a platform and tools
- Published in Artificial Intelligence, EITC/AI/GCML Google Cloud Machine Learning, Introduction, What is machine learning
Machine learning algorithms can learn to predict or classify new, unseen data. What does the design of predictive models of unlabeled data involve?
The design of predictive models for unlabeled data in machine learning involves several key steps and considerations. Unlabeled data refers to data that does not have predefined target labels or categories. The goal is to develop models that can accurately predict or classify new, unseen data based on patterns and relationships learned from the available