#Python

Abstract

Solar irradiance forecasting is critical for grid stability in photovoltaic (PV) power plants. This article replicates and extends the methodology of Lara-Benítez et al. (2023) "Short-term solar irradiance forecasting in streaming with deep learning" replacing the original offline simulation with a fully operational streaming pipeline built on InterSystems IRIS. We leverage IRIS Interoperability Productions as the streaming backbone, Embedded Python to run MLP, LSTM, and CNN deep learning models, and IntegratedML as an AutoML baseline.

This article introduces SHAP explainability methods as an approach to understand the reasons behind predictions in machine learning black-box models. It also includes a simple Jupyter notebook that you can use and modify to gain hands-on experience with these concepts:

https://www.kaggle.com/code/jorgeivnjh/explainability-in-ml-models

https://github.com/JorgeIvanJH/Explainability-in-ML-models

We will leverage these concepts for a future implementation in our Continuous Training Pipeline: https://community.intersystems.com/post/complementing-iris-mlflow-continuous-training-ct-pipeline

Today I have published a new Open Exchange package for generation of Synthetic Data directly into IRIS.

 It can be a frustrating process to find decent datasets when you are looking to make a demo app. Maybe the dataset doesn't matter that much, but you still want it to appear somewhat genuine and with several linked tables that are usable directly within IRIS with the neat implicit joins with ->. Maybe you just want linked tables that are easily installable with IPM to benchmark queries, this dataset generation would be perfect.

What is a Microservice?

A microservice is an architectural style that structures an application as a collection of small, autonomous services. Each component is developed around a specific business capability, can be deployed independently, and is typically managed by a miniature, specialized, self-governing team. (Source: https://microservices.io/)

Since I started using Claude Code, my motivation to create things has skyrocketed.

Previously, even if I wanted to build something, actually doing the coding felt like a hassle, so unless there was a very strong need, I rarely went as far as programming. But now, if I just jot down the specifications, Claude Code handles the rest automatically, resulting in a dramatic improvement in productivity.

I come from a generation native to ObjectScript, so I used to feel some hesitation when it came to switching to Python.