The AI Engine

built with Enterprise MLflow

Get Started

Full Machine Learning Lifecycle Solution

MLOps is DevOps for machine learning: a critical backend component of any data science offering.

Train Model

Data Versioning, Compute, etc. provide best of breed tools

Manage Model

Manage the staging and deployment of model versions

Retrain Model

Keep model fresh and protect against data drift

Deploy Model

Deployment options include Amazon Sagemaker

01

Unlock the power of artificial intelligence

InfinStor's one-of-a-kind breakthrough solution can help unleash the potential of artificial intelligence and machine learning. InfinStor helps users manage unstructured data and parallelize the compute of unstructured data.

With InfinStor, users can enjoy thousand times faster iterations. With lightning fast inference for live disease diagnostics and patient care, InfinStor is the industry's leading AI platform for unstructured data.

Experiment Tracking

Deep Learning is an iterative process. Experiment tracking applies structure and helps data scientists and the enterprise. Analogous to a lab notebook.

Useful for data scientists and data engineers
Set up to automatically log all runs
Track scheduled or automated runs

Model Management

Versioning, tracking, and staging of models is necessary. Archiving, rollbacks, and other forms of model management are not solved by Git.

Manage all the versions of a model
Transition models between stages
Staging, production, and archived states

Data Versioning

The ability to go back and look at the data via training or inference is essential for scientific correctness, and fulfilling regulatory requirements.

Automatically snapshots S3 data at run start
Provides consistent view of data for training
Snapshot view persists for future reference

Experiment Tracking

Deep Learning is an iterative process - experiment tracking applies structure and helps Data Scientists and the Enterprise. Analogous to the ‘Lab Notebook’

Useful for both Data Scientists and Data Engineers
Data Scientists can setup to automatically log all runs
Data Engineers can track scheduled/automated runs

Model Management

Versioning, tracking, staging of models is necessary. Archive, rollback, etc. Not solved by git

Manage model versions
Assign ‘Staging’, ‘Production’ and ‘Archived’ states
Transition Models between stages

Data Versioning

Ability to go back and look at the data – training or inference, is essential for scientific correctness, and may be a regulatory requirement

Automatically ‘snapshots’ S3 data at the start of run
Provides consistent view of data for training
Snapshot view persisted forever for future reference or regulatory reasons

02

Redefine unstructured data management

Unstructured data is data that is not structured or organized according to a particular data model or schema. The vast majority of data in this world is unstructured data. Some examples of unstructured data include images, videos, emails, websites, social media posts, and hand written documents.

There are a few current challenges with AI such as high AI development cost and time consuming AI development. Some examples of AI use cases include drug discovery and disease diagnostics. Most AI platforms are focused on structured data. InfinStor provides innovative tools for managing unstructured data.

Experiment Tracking

Deep Learning is an iterative process - experiment tracking applies structure and helps Data Scientists and the Enterprise. Analogous to the ‘Lab Notebook’

Useful for both Data Scientists and Data Engineers
Data Scientists can setup to automatically log all runs
Data Engineers can track scheduled/automated runs

Model Management

Versioning, tracking, staging of models is necessary. Archive, rollback, etc. Not solved by git

Manage model versions
Assign ‘Staging’, ‘Production’ and ‘Archived’ states
Transition Models between stages

Data Versioning

Ability to go back and look at the data – training or inference, is essential for scientific correctness, and may be a regulatory requirement

Automatically ‘snapshots’ S3 data at the start of run
Provides consistent view of data for training
Snapshot view persisted forever for future reference or regulatory reasons

03

Experience Supercharged MLflow

InfinStor transforms the way data scientists use the world's leading open source machine learning platform. InfinStor enhances open source MLflow with enterprise features and builds MLOps capabilities on top. And InfinStor empowers users by providing innovative multiregion and multicloud compute capabilities.

Cloud-Scale

Fully managed service for data scientists

Security

Authentication, authorization, and audit

Encryption

Both on-wire and at-rest encryption

High Availability

Flexibility with data center failure

Disaster Recovery

Resilience to region failure

Integrations

JupyterLab and Sagemaker Studio

Experiment Tracking

Deep Learning is an iterative process - experiment tracking applies structure and helps Data Scientists and the Enterprise. Analogous to the ‘Lab Notebook’

Useful for both Data Scientists and Data Engineers
Data Scientists can setup to automatically log all runs
Data Engineers can track scheduled/automated runs

Model Management

Versioning, tracking, staging of models is necessary. Archive, rollback, etc. Not solved by git

Manage model versions
Assign ‘Staging’, ‘Production’ and ‘Archived’ states
Transition Models between stages

Data Versioning

Ability to go back and look at the data – training or inference, is essential for scientific correctness, and may be a regulatory requirement

Automatically ‘snapshots’ S3 data at the start of run
Provides consistent view of data for training
Snapshot view persisted forever for future reference or regulatory reasons

Innovative Storage Solutions

Storage intelligence is at your fingertips with our proprietary snapshot technologies, InfinSnap and InfinSlice.

Automatic data provenance
Automatic data versioning
Time based data selection

Multicloud Compute Infrastructure

Harness multicloud and multiregion compute with the power of the InfinStor Compute Engine.

Auto scaling virtual machines
Full directed acyclic graph processor
MLflow recorder for JupyterLab

Hear what industry leaders say about InfinStor

InfinStor works with some of the brightest minds in the world of artificial intelligence and machine learning.

"There are quite a few commercial products that host MLflow but integrate it with access permission models ... such as InfinStor, [which] supports the MLflow API."

Matei Zaharia
Matei Zaharia
Co-founder of Databricks and creator of MLflow

"It was a pleasure working with the InfinStor team to tightly integrate their MLflow product into our corporate-wide MLOps platform. The quality of their product, their technical capability and their responsiveness to our requests were outstanding."

InfinStor Customer
Fortune 500 Pharma Customer
Senior PM at Leading Biopharma

"Love Jagane Sundar's company, InfinStor. If you want managed MLflow, he's the person to talk to. Easily the best customer service I've had in a long time."

David Knickerbocker
David Knickerbocker
Co-founder and CTO of VAST-OSINT

"You need a platform such as MLflow, supported by companies like InfinStor, to help streamline the workflows and manage your experiments, models, and production."

Kevin Petrie
Kevin Petrie
VP of Research of Eckerson Group

Get started with InfinStor for free

Create a bucket for storing MLflow artifacts and provide InfinStor service permission to access it.

Experiment Tracking

Deep Learning is an iterative process - experiment tracking applies structure and helps Data Scientists and the Enterprise. Analogous to the ‘Lab Notebook’

Useful for both Data Scientists and Data Engineers
Data Scientists can setup to automatically log all runs
Data Engineers can track scheduled/automated runs

Model Management

Versioning, tracking, staging of models is necessary. Archive, rollback, etc. Not solved by git

Manage model versions
Assign ‘Staging’, ‘Production’ and ‘Archived’ states
Transition Models between stages

Data Versioning

Ability to go back and look at the data – training or inference, is essential for scientific correctness, and may be a regulatory requirement

Automatically ‘snapshots’ S3 data at the start of run
Provides consistent view of data for training
Snapshot view persisted forever for future reference or regulatory reasons