InfinStor transforms the way data scientists use the world's leading open source machine learning platform.
Security and scalability in an enterprise grade service with experiment and model sharing.
Storage intelligence is at your fingertips with our proprietary snapshot technologies.
Harness multicloud and multiregion compute with the power of the InfinStor parallel processing engine.
MLOps is DevOps for machine learning: a critical backend component of any data science offering.
Data Versioning, Compute, etc. provide best of breed tools
Manage the staging and deployment of model versions
Keep model fresh and protect against data drift
Deployment options include Amazon Sagemaker
InfinStor empowers users by providing innovative multiregion and multicloud compute capabilities.
Deep Learning is an iterative process. Experiment tracking applies structure and helps data scientists and the enterprise. Analogous to a lab notebook.
Versioning, tracking, and staging of models is necessary. Archiving, rollbacks, and other forms of model management are not solved by Git.
The ability to go back and look at the data via training or inference is essential for scientific correctness, and fulfilling regulatory requirements.
Deep Learning is an iterative process - experiment tracking applies structure and helps Data Scientists and the Enterprise. Analogous to the ‘Lab Notebook’
Versioning, tracking, staging of models is necessary. Archive, rollback, etc. Not solved by git
Ability to go back and look at the data – training or inference, is essential for scientific correctness, and may be a regulatory requirement
Unstructured data is data that is not structured according to a particular data model or schema. The vast majority of data in this world is unstructured data.
Some examples of unstructured data are images, videos, emails, websites, 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.
A breakthrough solution from InfinStor can help unlock the potential of AI. InfinStor helps users manage unstructured data and parallelize the compute of unstructured data.
With InfinStor, users can enjoy 100 to 1000 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.
InfinStor enhances open source MLflow with enterprise features and builds MLOps capabilities on top.
Fully managed service for data scientists
Authentication, authorization, and audit
Both on-wire and at-rest encryption
Flexibility with data center failure
Resilience to region failure
JupyterLab and Sagemaker Studio
Storage intelligence is at your fingertips with our proprietary snapshot technologies, InfinSnap and InfinSlice.
Harness multicloud and multiregion compute with the power of the InfinStor Compute Engine.
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."
"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."
"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."
"You need a platform such as MLflow, supported by companies like InfinStor, to help streamline the workflows and manage your experiments, models, and production."
MLflow Kernel takes tracking to the next level, integrating with JupyterLab and Sagemaker.
by Adhitya Vadivel
This is the first article in a two part series: LogBERT explainer (this article).
By Syed Abdul Khader
This is a step-by-step guide to deploying an MLflow model in Sagemaker.
By Jagane Sundar
Create a bucket for storing MLflow artifacts and provide InfinStor service permission to access it.
Start using our free service by creating a bucket for MLflow artifacts in your AWS account and permitting our service to access the bucket.
MLfLow Kernel connects to an MLflow service and records all the data science activities in the notebook as MLflow artifacts.