vertex-ai-samples
Micronaut
vertex-ai-samples | Micronaut | |
---|---|---|
24 | 50 | |
1,358 | 5,951 | |
4.0% | 0.4% | |
9.8 | 9.9 | |
about 22 hours ago | 4 days ago | |
Jupyter Notebook | Java | |
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
-
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).
-
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.
-
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)
-
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.
-
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.
-
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
-
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
Micronaut
-
Javalin β a simple web framework for Java and Kotlin
Micronaut has a share of the space too.
https://micronaut.io/
However, youβre right that Spring Boot has the lions share of the Java ecosystem.
-
Spark β A web micro framework for Java and Kotlin
I've used vert.x in a big project once. I don't ever want to do that again. Performance is pretty good, but the developer experience is beyond clunky.
My current favourite Java server framework is Micronaut.
Great performance and easy to develop for!
https://micronaut.io/
- Java 21 Released
-
Java consumes 38x less energy than Python
I wonder how much you'd save with Micronaut: https://micronaut.io/
> Micronaut is a software framework for the Java virtual machine platform. It is designed to avoid reflection, thus reducing memory consumption and improving start times. Features which would typically be implemented at run-time are instead pre-computed at compile time.
https://en.wikipedia.org/wiki/Micronaut_(framework)
I don't think you'd go down to 9, but something like 20-30 could be doable.
-
mlfx FXML compiler
I'd like to introduce my project. It is called mlfx. It can compile FXML ahead of time. It is basically an annotation processor, which internally uses Micronaut framework's AST abstraction and compiles fxml files directly to JVM bytecode. This decreases UI load time and also helps with native-image reflection configs. It also has some compliance tests that load compiled code and check resulting object graph against one loaded by javafx-xml. It also has some drawbacks now, but, please, read README. Now I'm successfully using it in two production projects.
-
What other programming languages/frameworks do you enjoy besides c#/dotnet?
https://micronaut.io/ https://quarkus.io/
-
Virtual Threads Arrive in JDK 21, Ushering a New Era of Concurrency
when it comes to full stack frameworks, Micronaut(https://micronaut.io/) is actually good and pleasant to work with.
-
Tech-stack for web application using Kotlin?
For the server Quarkus and Micronaut might be interesting besides Spring Boot. Quarkus is more popular and backed by RedHat (so probably here to stay).
-
Top 5 Server-Side Frameworks for Kotlin in 2022: Micronaut
π₯ Spring Boot π₯ Quarkus π₯ Micronaut π Ktor π http4k
-
Would love some guidance in how to get started with building web projects with Java.
Spring boot is still The King. Although I've not done more than hello world with Micronaut, it might have easier learning curve than Spring (and concepts are similar to Spring so you can carry over later to learn Spring). It could also be a useful skill in world of microservices these days.
What are some alternatives?
mlops-with-vertex-ai - An end-to-end example of MLOps on Google Cloud using TensorFlow, TFX, and Vertex AI
Quarkus - Quarkus: Supersonic Subatomic Java.
awesome-mlops - A curated list of references for MLOps
spring-native - Spring Native is now superseded by Spring Boot 3 official native support
MLflow - Open source platform for the machine learning lifecycle
Vert.x - Vert.x is a tool-kit for building reactive applications on the JVM
VevestaX - 2 Lines of code to track ML experiments + EDA + check into Github
Flowable (V6) - A compact and highly efficient workflow and Business Process Management (BPM) platform for developers, system admins and business users.
jina - βοΈ Build multimodal AI applications with cloud-native stack
Nacos - an easy-to-use dynamic service discovery, configuration and service management platform for building cloud native applications.
rasa - π¬ 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
JaCoCo - :microscope: Java Code Coverage Library