Tag: Machine Learning
Preparing Datasets for Fine-Tuning ML Models: A Comprehensive Guide
Fine-tuning machine learning models starts with having well-prepared datasets. This guide will walk you through how to create these datasets, from gathering data to making instruction files. By the end, you’ll be equipped with practical knowledge and tools to prepare...
SWARM Engineering Helps Business Users Optimize Supply Chains with Next-Gen Technology
The overwhelming narrative heading into the new year has been everything surrounding artificial intelligence (AI) and machine learning (ML). What do these technologies mean for society as a whole, and how will they be incorporated into daily life? A key...
Cloud to Edge AI with a Mobile Database Platform
Edge computing is a technical architecture that extends data processing from the cloud to the edge, moving it closer to the point of interaction, including onto mobile devices. From the database perspective, a typical architecture includes a database in the cloud,...
Leveraging your ML models with Couchbase Analytics User Defined Functions (UDF)
With Couchbase 7.0 you’re now capable of allowing integration of Python UDFs with Couchbase Analytics. In Part 1 of this blog series, we covered the essentials for getting Couchbase and the Analytics for Machine Learning (ML) set up. ML has...
How Couchbase Simplifies Data Science (Part 1)
This post was co-authored by Karen Yuan, a High School Intern. Data science extracts knowledge from data and applies that knowledge to solve problems. In the next two posts, we will learn how the Couchbase Data Platform can meet various...
ML Meets NoSQL: Integrating Python User-Defined Functions with SQL++ for Analytics
After conversations with our customers about the delay in obtaining analytical insights from their Python-based machine learning models, we set out to create a seamless pipeline from Python to Couchbase Analytics that returns faster insights in real time. Introduction With...
Training a Linear Regression Model with Couchbase & a Jupyter Notebook: Step-by-Step Tutorial
Data scientists love Jupyter Notebooks – and it makes a natural pairing with the Couchbase document database. Why? The Jupyter Notebook web application lets you create and share documents that contain narrative text, equations and the like for use cases...
Couchbase Analytics Now Supports Azure Blob Storage Using External Analytics Collections
You can now analyze even more of your enterprise data using Couchbase Analytics – without having to move or migrate a byte. Starting today, I’m excited to announce the forthcoming support for Microsoft Azure Blob Storage for external Analytics Collections...
Announcing Couchbase Server 7.0
Couchbase Server 7.0 solves the requirements of modern application development. In doing so, it settles the debate over using relational or NoSQL databases. There’s no more need to compromise. (Try it out for yourself here or skip straight to the...
![How to Use Couchbase as a Machine Learning Model Store [Part 2 of 2]](https://www.couchbase.com/blog/wp-content/uploads/sites/1/2021/07/machine-learning-model-store-couchbase-data-platform-800x280.png)
How to Use Couchbase as a Machine Learning Model Store [Part 2 of 2]
When it comes to machine learning, a lot is said and written about training your ML models. But of equal importance is where you store those models once you’re ready to serve up real-time predictions. Last week, we looked at...
![5 Use Cases for Real-Time Prediction Serving Systems with Couchbase [Part 1 of 2]](https://www.couchbase.com/blog/wp-content/uploads/sites/1/2021/07/machine-learning-real-time-prediction-serving-system-with-couchbase-800x280.jpg)
5 Use Cases for Real-Time Prediction Serving Systems with Couchbase [Part 1 of 2]
Every machine learning use case has different performance needs, and when it comes to ML-powered enterprise prediction serving systems, it’s no different. A machine learning (ML) model takes an input (e.g., an image) and makes a prediction about it (e.g.,...
How to Harness Real-Time Data Analytics Using Operational Data
Today’s organizations generate data at massive scales and volumes. Applications running on servers in the cloud, data centers and edge devices all produce more data, from more data sources, than ever before. With this growth, enterprises constantly grapple with efficiently...