vertex-ai-samples VS Micronaut

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).

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.

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)

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.

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.

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.

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.

Micronaut has a share of the space too.

https://micronaut.io/

However, you’re right that Spring Boot has the lions share of the Java ecosystem.

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/

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.

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.

https://micronaut.io/ https://quarkus.io/

when it comes to full stack frameworks, Micronaut(https://micronaut.io/) is actually good and pleasant to work with.

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).

🥇 Spring Boot 🥈 Quarkus 🥉 Micronaut 🏅 Ktor 🏅 http4k

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.