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What is Predibase?

Declarative machine learning systems present an exceptional blend of adaptability and user-friendliness, enabling swift deployment of innovative models. Users focus on articulating the “what,” leaving the system to figure out the “how” independently. While intelligent defaults provide a solid starting point, users retain the liberty to make extensive parameter adjustments, and even delve into coding when necessary. Our team leads the charge in creating declarative machine learning systems across the sector, as demonstrated by Ludwig at Uber and Overton at Apple. A variety of prebuilt data connectors are available, ensuring smooth integration with your databases, data warehouses, lakehouses, and object storage solutions. This strategy empowers you to train sophisticated deep learning models without the burden of managing the underlying infrastructure. Automated Machine Learning strikes an optimal balance between flexibility and control, all while adhering to a declarative framework. By embracing this declarative approach, you can train and deploy models at your desired pace, significantly boosting productivity and fostering innovation within your projects. The intuitive nature of these systems also promotes experimentation, simplifying the process of refining models to better align with your unique requirements, which ultimately leads to more tailored and effective solutions.

What is HPE Ezmeral ML OPS?

HPE Ezmeral ML Ops presents a comprehensive set of integrated tools aimed at simplifying machine learning workflows throughout each phase of the ML lifecycle, from initial experimentation to full-scale production, thus promoting swift and flexible operations similar to those seen in DevOps practices. Users can easily create environments tailored to their preferred data science tools, which enables exploration of various enterprise data sources while concurrently experimenting with multiple machine learning and deep learning frameworks to determine the optimal model for their unique business needs. The platform offers self-service, on-demand environments specifically designed for both development and production activities, ensuring flexibility and efficiency. Furthermore, it incorporates high-performance training environments that distinctly separate compute resources from storage, allowing secure access to shared enterprise data, whether located on-premises or in the cloud. In addition, HPE Ezmeral ML Ops facilitates source control through seamless integration with widely used tools like GitHub, which simplifies version management. Users can maintain multiple model versions, each accompanied by metadata, within a model registry, thereby streamlining the organization and retrieval of machine learning assets. This holistic strategy not only improves workflow management but also fosters enhanced collaboration among teams, ultimately driving innovation and efficiency. As a result, organizations can respond more dynamically to shifting market demands and technological advancements.

Media

Media

Integrations Supported

Amazon Redshift
Apache Hive
LiteLLM
MySQL
Opik
PostgreSQL
Python
Snowflake

Integrations Supported

HPE Ezmeral

API Availability

Has API

API Availability

Pricing Information

Pay-as-you-go pricing
Free Trial Offered?

Pricing Information

Pricing not provided

Supported Platforms

SaaS

Supported Platforms

SaaS
On-Prem

Customer Service / Support

Web-Based Support

Customer Service / Support

Standard Support
Web-Based Support

Training Options

Documentation Hub
Online Training

Training Options

Documentation Hub

Company Facts

Organization Name

Predibase

Company Location

United States

Company Website

predibase.com

Company Facts

Organization Name

Hewlett Packard Enterprise

Date Founded

2015

Company Location

United States

Company Website

www.hpe.com/us/en/solutions/ezmeral-machine-learning-operations.html

Categories and Features

AI Development

Not specified

AI Fine-Tuning

Not specified

AI Infrastructure

Not specified

Machine Learning

Not specified

ML Model Deployment

Not specified

Categories and Features

Machine Learning

Not specified

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