What are some common AI/ML algorithms to be used on the processed data?
In the context of Artificial Intelligence (AI) and Google Cloud Machine Learning, the processed data—meaning data that has undergone cleaning, normalization, feature extraction, and transformation—is ready for machine learning algorithms to learn patterns, make predictions, or classify information. The selection of a suitable algorithm is driven by the underlying problem, the structure and type of
How Keras models replace TensorFlow estimators?
The transition from TensorFlow Estimators to Keras models represents a significant evolution in the workflow and paradigm of machine learning model creation, training, and deployment, particularly within the TensorFlow and Google Cloud ecosystems. This change is not merely a shift in API preference but reflects broader trends in accessibility, flexibility, and the integration of modern
How to configure specific Python environment with Jupyter notebook?
Configuring a specific Python environment for use with Jupyter Notebook is a fundamental practice in data science, machine learning, and artificial intelligence workflows, particularly when leveraging Google Cloud Machine Learning (AI Platform) resources. This process ensures reproducibility, dependency management, and isolation of project environments. The following comprehensive guide addresses the configuration steps, rationale, and best
- Published in Artificial Intelligence, EITC/AI/GCML Google Cloud Machine Learning, Further steps in Machine Learning, Working with Jupyter
How to use TensorFlow Serving?
TensorFlow Serving is an open-source system developed by Google for serving machine learning models, particularly those built using TensorFlow, in production environments. Its primary purpose is to provide a flexible, high-performance serving system for deploying new algorithms and experiments while maintaining the same server architecture and APIs. This framework is widely adopted for model deployment
- Published in Artificial Intelligence, EITC/AI/GCML Google Cloud Machine Learning, First steps in Machine Learning, Plain and simple estimators
What is Classifier.export_saved_model and how to use it?
The function `Classifier.export_saved_model` is a method commonly found in TensorFlow-based machine learning workflows, particularly associated with the process of deploying machine learning models to production environments, such as Google Cloud’s serverless platforms (for instance, AI Platform Prediction). Understanding this method requires familiarity with the TensorFlow framework, the SavedModel format, and the best practices for exporting
- Published in Artificial Intelligence, EITC/AI/GCML Google Cloud Machine Learning, First steps in Machine Learning, Serverless predictions at scale
Why is regression frequently used as a predictor?
Regression is commonly employed as a predictor within machine learning due to its foundational capacity to model and forecast continuous outcomes based on input features. This predictive capability is rooted in the mathematical and statistical formulation of regression analysis, which estimates the relationships among variables. In the context of machine learning, and particularly in Google
- Published in Artificial Intelligence, EITC/AI/GCML Google Cloud Machine Learning, First steps in Machine Learning, The 7 steps of machine learning
Are Lagrange multipliers and quadratic programming techniques relevant for machine learning?
The question of whether one needs to learn Lagrange multipliers and quadratic programming techniques to be successful in machine learning depends on the depth, focus, and nature of the machine learning tasks one intends to pursue. The seven-step process of machine learning, as outlined in many introductory courses, includes defining the problem, collecting data, preparing
Can more than one model be applied during the machine learning process?
The question of whether more than one model can be applied during the machine learning process is highly pertinent, especially within the practical context of real-world data analysis and predictive modeling. The application of multiple models is not only feasible but is also a widely endorsed practice in both research and industry. This approach arises
Can Machine Learning adapt which algorithm to use depending on a scenario?
Machine learning (ML) is a discipline within artificial intelligence that focuses on building systems capable of learning from data and improving their performance over time without being explicitly programmed for each task. A central aspect of machine learning is algorithm selection: choosing which learning algorithm to use for a particular problem or scenario. This selection
- Published in Artificial Intelligence, EITC/AI/GCML Google Cloud Machine Learning, Introduction, What is machine learning
What is the simplest route to most basic didactic AI model training and deployment on Google AI Platform using a free tier/trial using a GUI console in a step-by-step manner for an absolute begginer with no programming background?
To begin training and deploying a basic AI model using the Google AI Platform via the web-based GUI, especially as an absolute beginner with no programming background, it is advisable to use Google Cloud’s Vertex AI Workbench and AutoML (now part of Vertex AI) features. These tools are specifically designed for users without coding experience