Security and scalability in an enterprise grade service with experiment and model sharing.
Storage intelligence is at your fingertips with our proprietary snapshot technologies.
Multicloud/multiregion Kubernetes compute managed by Concurrent for MLflow 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'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.
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 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.
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
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.
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
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
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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.
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InfinStor works with some of the brightest minds in the world of artificial intelligence and machine learning.
Create a bucket for storing MLflow artifacts and provide InfinStor service permission to access it.
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
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.
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
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This is a step-by-step guide to deploying an MLflow model in Sagemaker.
By Jagane Sundar
