This post was originally published on my website adamnovotny.com Summary Keras LSTM can be a powerful tool for forecasting. Below is a simple template notebook showing how to setup a data science forecasting experiment. Dataset A synthetic dataset was generated using a scikit-learn regression generator make_friedman1. The dataset is nonlinear, with…

In this article I will leverage the global temperate dataset I discussed previously to make a temperature forecast using Facebook Prophet for the next 50 years. Note: the temperature dataset serves ONLY as a vehicle to learn how to do forecasting using Prophet. …

This article is an extension of my previous article describing a similar deployment process using native AWS Lambda tools. However, Amazon since started supporting container images and updated it’s pricing policy to 1ms granularity. Both are major developments improving tooling and making small deployments cost effective. My previous article focused…

This article is a follow up to my previous tutorial on how to setup Google Colab and auto-sklean. Here, I will go into more detail that shows auto-sklearn performance on an artificially created dataset. The full notebook gist can be found here. First, I generated a regression dataset using scikit…

Visit my personal website adamnovotny.com to view the most recently updated version of ML and data resources I’ve found helpful. AdaBoost: Fits a sequence of weak learners on repeatedly modified data. The modifications are based on errors made by previous learners. Analysis of variance ANOVA Anomaly detection: future examples may…

Auto ML is fast becoming a popular solution to build minimal viable models for new projects. A popular library for Python is Auto-sklearn that leverages the most popular Python ML library scikit-learn. Auto-sklearn runs a smart search over scikit-learn models and parameters to find the best performing ensemble of models. …

Google released a white paper describing how the company intends to generate all of its electricity needs from renewable energy sources by 2030. Previously, Google committed to reducing emissions by buying offsets or generating renewable energy off-cycle. …

While AWS Lambda functions are typically used to build API endpoints, at their core Lambda functions can return almost anything. This includes returning html markup with dynamic content. I will not go into details describing how to deploy AWS Lambda functions. Please see the official documentation. …

In general, a machine learning pipeline should have the following characteristics: To ensure data consistency, the pipeline should include every step (such as feature engineering) required to train and score training and testing datasets, and score real time requests. …