Snorkel AI

MAY 26, 2023

It won’t be a long demo, it’ll be a very quick demo of what you can do and how you can operationalize stuff in Snowflake. And so data scientists might be leveraging one compute service and might be leveraging an extracted CSV for their experimentation. The demo is actually very simple.

SQL 52

SQL ML ML Python 52

The MLOps Blog

JUNE 27, 2023

It provides tools and components to facilitate end-to-end ML workflows, including data preprocessing, training, serving, and monitoring. Kubeflow integrates with popular ML frameworks, supports versioning and collaboration, and simplifies the deployment and management of ML pipelines on Kubernetes clusters.

Machine Learning 134

Machine Learning Machine Learning ML ML 134

The MLOps Blog

MARCH 21, 2023

An ML platform standardizes the technology stack for your data team around best practices to reduce incidental complexities with machine learning and better enable teams across projects and workflows. We ask this during product demos, user and support calls, and on our MLOps LIVE podcast. Data engineers are mostly in charge of it.

Machine Learning 69

Machine Learning Machine Learning Data Scientist ML 69

The MLOps Blog

JUNE 5, 2023

A lot of them are demos at that point, they’re still not products. You have your: feature store model registry data from a data lake The data is then moved across this workflow, modeled and then deployed, Now there’s a good link between your development environments and the production environment where it’s monitoring.

ML 52

ML ML Machine Learning Machine Learning 52

ODSC - Open Data Science

MAY 24, 2023

Building a Business with a Real-Time Analytics Stack, Streaming ML Without a Data Lake, and Google’s PaLM 2 Building a Pizza Delivery Service with a Real-Time Analytics Stack The best businesses react quickly and with informed decisions. Here’s a use case of how you can use a real-time analytics stack to build a pizza delivery service.

Data Lakes 52

Data Lakes ML ML Analytics 52

The MLOps Blog

MAY 9, 2023

The pipelines are interoperable to build a working system: Data (input) pipeline (data acquisition and feature management steps) This pipeline transports raw data from one location to another. Model/training pipeline This pipeline trains one or more models on the training data with preset hyperparameters. Kale v0.7.0.

ML 98

ML ML Machine Learning Machine Learning 98

Flipboard

MAY 15, 2025

These organizations have a huge demand for lakehouse solutions that combine the best of data warehouses and data lakes to simplify data management with easy access to all data from their preferred engines. For Project name , enter demo. For Lakehouse catalog name , enter rms-catalog-demo.

AWS 117

AWS SQL Data Lakes Data Warehouse 117