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What is Valohai?

While models may come and go, the infrastructure of pipelines endures over time. Engaging in a consistent cycle of training, evaluating, deploying, and refining is crucial for success. Valohai distinguishes itself as the only MLOps platform that provides complete automation throughout the entire workflow, starting from data extraction all the way to model deployment. It optimizes every facet of this process, guaranteeing that all models, experiments, and artifacts are automatically documented. Users can easily deploy and manage models within a controlled Kubernetes environment. Simply point Valohai to your data and code, and kick off the procedure with a single click. The platform takes charge by automatically launching workers, running your experiments, and then shutting down the resources afterward, sparing you from these repetitive duties. You can effortlessly navigate through notebooks, scripts, or collaborative git repositories using any programming language or framework of your choice. With our open API, the horizons for growth are boundless. Each experiment is meticulously tracked, making it straightforward to trace back from inference to the original training data, which guarantees full transparency and ease of sharing your work. This approach fosters an environment conducive to collaboration and innovation like never before. Additionally, Valohai's seamless integration capabilities further enhance the efficiency of your machine learning workflows.

What is Feast?

Facilitate real-time predictions by utilizing your offline data without the hassle of custom pipelines, ensuring that data consistency is preserved between offline training and online inference to prevent any discrepancies in outcomes. By adopting a cohesive framework, you can enhance the efficiency of data engineering processes. Teams have the option to use Feast as a fundamental component of their internal machine learning infrastructure, which allows them to bypass the need for specialized infrastructure management by leveraging existing resources and acquiring new ones as needed. Should you choose to forego a managed solution, you have the capability to oversee your own Feast implementation and maintenance, with your engineering team fully equipped to support both its deployment and ongoing management. In addition, your goal is to develop pipelines that transform raw data into features within a separate system and to integrate seamlessly with that system. With particular objectives in mind, you are looking to enhance functionalities rooted in an open-source framework, which not only improves your data processing abilities but also provides increased flexibility and customization to align with your specific business needs. This strategy fosters an environment where innovation and adaptability can thrive, ensuring that your machine learning initiatives remain robust and responsive to evolving demands.

Media

Media

Integrations Supported

Microsoft Azure
WEKA

Integrations Supported

Amazon DynamoDB
Amazon EMR
Amazon ElastiCache
Amazon Redshift
Amazon S3
Apache Kafka
DataHub
Databricks
Delta Lake
Flyte
PySpark
Python
Ray
Redis
Seldon
Snowflake
TensorFlow

API Availability

API Availability

Pricing Information

$560 per month
Free Version
Free Trial Offered?

Pricing Information

Pricing not provided

Supported Platforms

SaaS

Supported Platforms

Windows
Mac
Linux

Customer Service / Support

24 Hour Support
Web-Based Support

Customer Service / Support

Web-Based Support

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Training Options

Documentation Hub

Company Facts

Organization Name

Valohai

Date Founded

2016

Company Location

Finland

Company Website

valohai.com

Company Facts

Organization Name

Tecton

Date Founded

2019

Company Location

United States

Company Website

feast.dev/

Categories and Features

AI Inference

Not specified

Artificial Intelligence

Predictive Analytics
Process/Workflow Automation

Deep Learning

Convolutional Neural Networks
Document Classification
Image Segmentation
ML Algorithm Library
Model Training
Neural Network Modeling
Self-Learning
Visualization

Machine Learning

Deep Learning
ML Algorithm Library
Model Training
Predictive Modeling
Statistical / Mathematical Tools
Templates
Visualization

ML Model Management

Not specified

Predictive Analytics

AI / Machine Learning
Benchmarking
Demand Forecasting
Modeling & Simulation

Categories and Features

AI Inference

Not specified

Data Engineering

Not specified

Machine Learning

Not specified

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