How many machine learning tools should we know?
The question of how many machine learning tools one should know, particularly in the context of Google Cloud Machine Learning and specifically with Kubeflow for machine learning on Kubernetes, is nuanced and depends heavily on the intended use cases, the complexity of workflows, the team’s expertise, and the evolving landscape of machine learning (ML) productionization.
- Published in Artificial Intelligence, EITC/AI/GCML Google Cloud Machine Learning, Advancing in Machine Learning, Kubeflow - machine learning on Kubernetes
Finance or, better, trading (stocks, crypto, ETFs,…) requires a lot of data to be analyzed. How can I create a ML model to take into consideration all those factors—financial and non-financial, like human psychology, political events, weather?
Analyzing and predicting movements in financial markets, such as stocks, cryptocurrencies, ETFs, and similar assets, is a complex task that necessitates consideration of a wide range of variables. These variables extend far beyond traditional financial metrics, encompassing non-financial factors including human sentiment, political events, and even weather conditions. Developing a machine learning (ML) model that
- Published in Artificial Intelligence, EITC/AI/GCML Google Cloud Machine Learning, Introduction, What is machine learning
What is data ingestion?
Data ingestion refers to the process of collecting and importing data from various sources into a centralized location, typically for the purpose of storage, processing, and analysis. Within the context of machine learning on Google Cloud and other cloud-based environments, data ingestion forms the foundational step that precedes all subsequent processes, such as data preparation,
- Published in Artificial Intelligence, EITC/AI/GCML Google Cloud Machine Learning, Further steps in Machine Learning, Big data for training models in the cloud
Can we use streaming data to train and use a model continuously and improve it at the same time?
The ability to use streaming data for both continuous model training and real-time inference is a significant topic in machine learning, particularly within modern data-driven applications. The traditional approach to building machine learning models typically involves collecting a batch of data, cleaning and preparing it, training a model, evaluating it, deploying it, and then periodically
What is the difference between machine learning and data science?
The distinction between "machine learning" and "data science" is foundational yet frequently misunderstood in the fields related to artificial intelligence and analytics, especially when considering applications within platforms such as Google Cloud Machine Learning. Understanding the boundaries and intersections between these two concepts is important for professionals and students seeking clarity on their respective roles,
What are the first steps to prepare for using Google Cloud ML tools to detect content changes on websites?
To effectively use Google Cloud Machine Learning (GCP ML) tools for detecting content changes on websites, one must undertake a series of well-defined preparatory steps. This process integrates principles of machine learning, web data collection, cloud-based architecture, and data engineering. Each step is foundational to ensure that the subsequent application of machine learning models yields