News

Graphcore And Spell Collaborate For End-To-End ML Operations

Companies also announce free IPU Test Drive with Graphcore AI hardware and Spell software.

Spell, Graphcore partner to build next-gen AI infrastructure

Two 5-year-old startups – one each from the UK and the USA – today announced a  partnership to design and build what they describe as “the next generation of AI infrastructure.”

Free Trial Now Available.

Spell recognized as one of the coolest Gartner Cool Vendors® in new report.

2021 Cool Vendors Tackle Complexity With Connected, Customized and Complementary Solutions: A Gartner Trend Insight Report


Published 4 January 2022 - ID G00761055


By Carlie Idoine, Janelle Hill, Adrian Lee, Adrian Leow, Daryl Plummer, Rita Sallam

Spell has been named a Contender in the The Forrester Wave™: AI Infrastructure, Q42021, The 13 Providers That Matter Most And How They Stack Up. 

We're in good company with other featured vendors including NVIDIA, AWS, Google Cloud Platform, Microsoft, HPE, IBM, and Dell. 

Spell is on fire!

Named one of the hottest companies in AI.

Spell Thought Leadership: Avoiding AI Technical Debt

It's all about Infrastructure.

Intellyx Brain Candy Brief

Spell provides an MLOps platform for developing, training, and optimizing models for deep learning use cases.

Spell Recognized as a 2021 Gartner® Cool Vendor™

Gartner® names Spell as a Cool Vendor™ for Enterprise AI Operationalization and Engineering in their October 2021 report.

Spell Recognized in New Gartner® Market Guide

Gartner® names Spell as a Representative Vendor for ModelOps in the September 2021 Market Guide for AI Trust, Risk and Security Management report.

Spell introduces MLOps for deep learning

Deep learning model development and management drives special requirements, especially around the provisioning of hardware. Spell introduced a machine learning operations (MLOps) platform last week that caters to these very needs.

Spell Bringing MLOps to Deep Learning to Ease the Deep Learning Path for Enterprises

New York-based startup, Spell, has launched what it calls a cloud-agnostic MLOps platform that is targeted to serve the more complex and unique needs of deep learning using the principles of MLOps used for machine learning.

Spell unveils deep learning operations platform to cut AI training costs

Spell today unveiled an operations platform that provides the tooling needed to train AI models based on deep learning algorithms.

Spell Operationalizes Advanced AI With the First Comprehensive MLOps Platform for Deep Learning

Unique “Dlops” Software Manages and Automates the Full AI Life Cycle for Enhanced Governance, Time-to-Value, and ROI

SD Times news digest: JetBrains Compose Multiplatform Alpha, Spell launches MLOps solution for deep learning, Blueprint launches new process discovery solution

Spell’s new cloud-agnostic, end-to-end platform for deep learning tracks, manages, and automates the entire deep learning workflow, from developing and training to deploying and optimizing models at scale. 

Spell named one of The 10 Hottest Data Science And Machine Learning Startups of 2021

Spell.ml develops a machine learning platform for deep learning operations (DLOps) that the company says goes beyond traditional machine learning with its capabilities for preparing, training, deploying and managing the full lifecycle of machine learning and deep learning models.

The 10 Coolest Machine Learning Tools Of 2021

Spell.ml develops a machine learning platform for deep learning operations (DLOps) that the company says goes beyond traditional machine learning with its capabilities for preparing, training, deploying and managing the full lifecycle of machine learning and deep learning models. Spell.ml says its cloud-agnostic platform can help reduce the costs of deep learning model development.

Spell listed in CRN's Emerging Big Data Vendors To Know In 2021

Spell.ML’s machine learning platform for deep learning operations goes beyond traditional machine learning with its capabilities for preparing, training, deploying and managing the full life cycle of machine learning and deep learning models.