vertex-ai-samples
mlops-with-vertex-ai
| vertex-ai-samples | mlops-with-vertex-ai | |
|---|---|---|
| 24 | 2 | |
| 1,358 | 327 | |
| 4.0% | 1.5% | |
| 9.8 | 0.0 | |
| about 20 hours ago | 12 months ago | |
| Jupyter Notebook | Jupyter Notebook | |
| Apache License 2.0 | Apache License 2.0 |
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.
vertex-ai-samples
- Gemini 1.5 outshines GPT-4-Turbo-128K on long code prompts, HVM author
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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).
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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 umbrella for all of the Google models available through their cloud platform.
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Google Imagen 2
For the peer comments
- https://cloud.google.com/vertex-ai (main page)
- https://cloud.google.com/vertex-ai/docs/start/introduction-u... (docs entry point)
- https://console.cloud.google.com/vertex-ai (cloud console)
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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.
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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.
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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.
- Tune PaLM 2 with your own RLHF training data
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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.
- Google Cloud Learning Machine
mlops-with-vertex-ai
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Where to start?
To take it to the next level, maybe start to dive into a truer "end-to-end mlops" example.
- GitHub - GoogleCloudPlatform/mlops-with-vertex-ai: An end-to-end example of MLOps on Google Cloud using TensorFlow, TFX, and Vertex AI
What are some alternatives?
awesome-mlops - A curated list of references for MLOps
mlops-course - Learn how to design, develop, deploy and iterate on production-grade ML applications.
MLflow - Open source platform for the machine learning lifecycle
MLOps - MLOps examples
VevestaX - 2 Lines of code to track ML experiments + EDA + check into Github
Made-With-ML - Learn how to design, develop, deploy and iterate on production-grade ML applications.
jina - ☁️ Build multimodal AI applications with cloud-native stack
nitroml - NitroML is a modular, portable, and scalable model-quality benchmarking framework for Machine Learning and Automated Machine Learning (AutoML) pipelines.
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
amazon-sagemaker-examples - Example 📓 Jupyter notebooks that demonstrate how to build, train, and deploy machine learning models using 🧠 Amazon SageMaker.
Micronaut - Micronaut Application Framework
generative-ai - Sample code and notebooks for Generative AI on Google Cloud, including Gemini