Google Cloud Machine Learning Reviews and details

Social recommendations and mentions

• Gemini 1.5 outshines GPT-4-Turbo-128K on long code prompts, HVM author

  2. Google Cloud Vertex AI: https://cloud.google.com/vertex-ai. Policy: https://cloud.google.com/vertex-ai/docs/generative-ai/data-governance#foundation_model_training. - Source: Hacker News / 2 months ago

• Let's build your first ML app in Google Cloud Run

  Google Cloud Platform (GCP) provides a very befitting Machine Learning solution called Vertex Ai that handles Google Cloud's unified platform for building, deploying, and managing machine learning (ML) models. Our goal is to build a simple Machine Learning application that optimizes all that GCP provides plus an implementation of continuous integration and continuous development (CI/CD). - Source: dev.to / 4 months ago

• Google Gemini Pro API Available Through AI Studio

  Cross posting some links from another post that HNers found helpful - https://cloud.google.com/vertex-ai (marketing page) - https://cloud.google.com/vertex-ai/docs (docs entry point) - https://console.cloud.google.com/vertex-ai (cloud console) - https://console.cloud.google.com/vertex-ai/model-garden (all the models) - https://console.cloud.google.com/vertex-ai/generative (studio / playground) VertexAI is the... - Source: Hacker News / 5 months ago

• Google Imagen 2

  For the peer comments - https://cloud.google.com/vertex-ai (main page) - https://cloud.google.com/vertex-ai/docs/start/introduction-unified-platform (docs entry point) - https://console.cloud.google.com/vertex-ai (cloud console). - Source: Hacker News / 5 months ago

• Introducing Gemini: our largest and most capable AI model

  Starting on December 13, developers and enterprise customers can access Gemini Pro via the Gemini API in Google AI Studio or Google Cloud Vertex AI. Source: 5 months ago

• How to Use AI/ML Models for Your Projects

  Google Cloud Platform (https://cloud.google.com/vertex-ai): Conversely, Google Cloud Platform (GCP) provides a comprehensive suite of AI and machine learning services, including APIs for vision, language, conversation, and structured data analysis. Whether you're analyzing images, interpreting human speech, or diving deep into data patterns, GCP has something for you. - Source: dev.to / 5 months ago

• Create a ChatBot with VertexAI and LibreChat

  VertexAI is a machine learning platform available on Google Cloud. It offers a variety of services to train and deploy AI models, including those for Generative AI. - Source: dev.to / 6 months ago

• Any better alternatives to fine-tuning GPT-3 yet to create a custom chatbot persona based on provided knowledge for others to use?

  Depending on how much work you want to put into it, you can get started at HuggingFace with their models and datasets, but you'd need compute power, multiple MLOps, etc. I was introduced to the concept in this video, since Google has their Vertex AI tools on Google Cloud, and there's always LangChain but I'm not sure about anything recent. Source: 10 months ago

• Can we use Users session token (google) to access Bard inside the Flutter application?

  Bard like functionality is available through https://cloud.google.com/vertex-ai. Source: 11 months ago

• Google I/O 2023: All topic-specific highlights (ADHD react streamer edition)

  As a bonus, Google's AI Cloud training service, Vertex, becomes more layman-friendly. Make your own AI with $300 free credit when you sign up. Source: 12 months ago

• Starting with image recognition, what do you suggest?

  I assume I would need to use something like Google's VertexAI to create datasets & train the Machine Learning algorithm that pictures of X mean X. Then need VisionAI on top so the app can access the APIs. Does this sound right? Source: 12 months ago

• Starting with image recognition, what do you suggest?

  I assume I would need to use something like Google's VertexAI with VisionAI on top. Are these the best options? Source: 12 months ago

• Building CI/CD for Vertex AI pipelines: The production

  Vertex AI is used to build training pipelines. - Source: dev.to / over 1 year ago

• Any MLOps platform you use?

  Vertex AI - An ML platform by Google Cloud. They have AI-powered tools to ingest, analyze, and store video data. Good for image classification, NLP, recommendation systems etc. Source: about 1 year ago

• JBCNConf 2022: A great farewell

  She made mentions to ML-Ops and MLFlow including Vertex AI the GCP implementation. I will post the video as soon as it is available. In the meantime, you can enjoy any other talk from Nerea Luis. - Source: dev.to / almost 2 years ago

• Building CI/CD for Vertex AI pipelines: The first solution

  We are already building training pipelines with Vertex AI. Example of Vertex AI pipeline from the GC blog article. - Source: dev.to / almost 2 years ago

• Voice assistant that can be taught how to swear (Part 2)

  In the future, you can replace Trie with a text classifier in Vertex AI;. - Source: dev.to / about 2 years ago

• Simplest way to run large batch jobs in the cloud?

  Consider using Vertex AI on GCP. Great end-to-end suite of tools for feature engineering, training, hyperparameter tuning, model tuning + explanation, MLOps. You can schedule your pipeline with Cloud Scheduler here. Source: about 2 years ago

• Data Science toolset summary from 2021

  Google Cloud Vertex AI - Vertex AI brings together the Google Cloud services for building ML under one, unified UI and API. In Vertex AI, you can now easily train and compare models using AutoML or custom code training and all your models are stored in one central model repository. These models can now be deployed to the same endpoints on Vertex AI. Link - https://cloud.google.com/vertex-ai. - Source: dev.to / over 2 years ago

• Big Data Pipeline on AWS, Microsoft Azure, and Google Cloud

  AI platform: contains AutoML, but you can also deploy models directly - https://cloud.google.com/ai-platform. Source: about 3 years ago

• Machine Learning for Beginners

  There are various tools that developers and data scientists use to implement Machine Learning algorithms. Often, engineers will use python with libraries such as scikit-learn, TensorFlow, and Keras to build their models, and rely on pandas and numpy to work with their data. There are also a suite of automated Machine Learning tools which further abstract away the nitty gritty. The most popular of these are Google... - Source: dev.to / about 3 years ago