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In today’s enterprise, the role of the data scientist can seem deceptively simple: generate insights from data and deliver them to decision makers. This process can look like a one-way trip — models are delivered, the business takes action, and the data scientists are left wondering if and how their models have driven impact. Moreover, the sober …

Read more “How Palantir Foundry’s Ontology Deploys Data Science to the Front Line”

Today we announce the general availability of Renate, an open-source Python library for automatic model retraining. The library provides continual learning algorithms able to incrementally train a neural network as more data becomes available. By open-sourcing Renate, we would like to create a venue where practitioners working on real-world machine learning systems and researchers interested …

Read more “Automatically retrain neural networks with Renate”

Deploying high-quality, trained machine learning (ML) models to perform either batch or real-time inference is a critical piece of bringing value to customers. However, the ML experimentation process can be tedious—there are a lot of approaches requiring a significant amount of time to implement. That’s why pre-trained ML models like the ones provided in the PyTorch …

Read more “Create Amazon SageMaker models using the PyTorch Model Zoo”

To produce any sufficiently accurate machine learning model, the process requires tuning parameters and hyperparameters. Your model’s parameters are variables that your chosen machine learning technique uses to adjust to your data, like weights in neural networks to minimize loss. Hyperparameters are variables that control the training process itself. For example, in a multilayer perceptron, …

Read more “Perform hyperparameter tuning using R and caret on Vertex AI”