VevestaX VS vertex-ai-samples

Compare VevestaX vs vertex-ai-samples and see what are their differences.

Our great sponsors
  • InfluxDB - Power Real-Time Data Analytics at Scale
  • WorkOS - The modern identity platform for B2B SaaS
  • SaaSHub - Software Alternatives and Reviews
VevestaX vertex-ai-samples
10 24
27 1,342
- 7.5%
0.0 9.8
over 1 year ago 4 days ago
Jupyter Notebook Jupyter Notebook
Apache License 2.0 Apache License 2.0
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.

VevestaX

Posts with mentions or reviews of VevestaX. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2022-05-12.

vertex-ai-samples

Posts with mentions or reviews of vertex-ai-samples. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2024-02-18.

What are some alternatives?

When comparing VevestaX and vertex-ai-samples you can also consider the following projects:

bodywork-pipeline-with-aporia-monitoring - Integrating Aporia ML model monitoring into a Bodywork serving pipeline.

awesome-mlops - A curated list of references for MLOps

MLOps - End to End toy example of MLOps

mlops-with-vertex-ai - An end-to-end example of MLOps on Google Cloud using TensorFlow, TFX, and Vertex AI

Made-With-ML - Learn how to design, develop, deploy and iterate on production-grade ML applications.

MLflow - Open source platform for the machine learning lifecycle

mlflow-deployments - Source code for the post Effortless deployments with MLFlow, showcasing how logging models using MLFLow can provide you want to easily deploy them in production later.

jina - ☁️ Build multimodal AI applications with cloud-native stack

mlflow-easyauth - Deploy MLflow with HTTP basic authentication using Docker

LibreChat - Enhanced ChatGPT Clone: Features OpenAI, Assistants API, Azure, Groq, GPT-4 Vision, Mistral, Bing, Anthropic, OpenRouter, Vertex AI, Gemini, AI model switching, message search, langchain, DALL-E-3, ChatGPT Plugins, OpenAI Functions, Secure Multi-User System, Presets, completely open-source for self-hosting. More features in development

OAD - Collection of tools and scripts useful to automate microscopy workflows in ZEN Blue using Python and Open Application Development tools and AI tools.

rasa - 💬 Open source machine learning framework to automate text- and voice-based conversations: NLU, dialogue management, connect to Slack, Facebook, and more - Create chatbots and voice assistants