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Below, I explain a sample app with a FastAPI endpoint. RabbitMQ is used to deliver and return messages between the API endpoint and the backend. The backend code could run on a different microservice, and multiple backends can be started for scalability.
In this video, I continue to talk about microservices for ML. I explain how to implement a direct call from the training service to fetch data and run training with model evaluation.
Dynamic form is a common requirement when building more advanced UIs. With Oracle JET you have all the tools available to build dynamic form. One of the examples of dynamic form requirements — report parameter capture screens.
Building fixed forms to capture parameters for each report would be an overkill. A smarter approach is to build one dynamic form, which would handle a set of different UI components and render based on metadata received from the service.
TensorFlow 2.0 comes with Keras packaged inside, so there is no need to import Keras as a separate module (although you can do this if you need). The TensorFlow 2.0 API is simplified and improved. This is good news for us machine learning developers.
This is how you import Keras from TensorFlow:
In ADF Faces, you can leverage the full power of JavaScript. I will explain how you could assign a value from ADF Faces component to the plain HTML div.
The sample app is available on GitHub repo. It doesn't require DB connection, you can run it straight away in Oracle JDeveloper.
There is always a bit of luck involved when selecting parameters for Machine Learning model training. Lately, I have worked with gradient boosted trees and XGBoost in particular. We are using XGBoost in the enterprise to automate repetitive human tasks. While training ML models with XGBoost, I created a pattern to choose parameters, which helps me to build new models quicker. I will share it in this post, and hopefully you will find it useful too.
I'm using the Pima Indians Diabetes Database for the training. CSV data can be downloaded from here.
The process to prepare data for machine learning model training looks somewhat similar to the process of preparing food ingredients to cook dinner. You know that in both cases it takes time, but then you are rewarded with a tasty dinner, or in this case, a great ML model.
I will not be diving into data science or discussing how to structure and transform data. It all depends on the use case, and there are so many ways to reformat data to get the most out of it. I would rather focus on a simple but practical example — how to split data into training and test datasets with Python.
We are using the Nginx web server for the Oracle ADF WorkBetter demo hosted on the DigitalOcean cloud server. Nginx helps to serve web application content fast and offer improved performance. One of the important tuning options — content compression; Nginx does this job well and is simple to set up.
Content compression doesn't provide direct runtime performance; a browser would run the same code, doesn't matter it was compressed or not. But it brings improved perceived performance (which is very important), and network time is way faster because of reduced content size. Oracle ADF is a server-side framework, each request would bring content from the server — the faster this content comes, it means better application performance.
JET keeps evolving and in the latest version, its toolkit provides improved support for form cross-field validation. It is much easier to implement validation than it was before. I will show it in this example.
Example of the data entry form. Validation logic: