Machine Learning

GlobeOSS Advanced Digital Analytics services enable our customers to derive value from massive digital data into a world of insights and intelligence. GlobeOSS provides An enterprise-grade service for the end-to-end machine learning lifecycle.

Rapidly build and train models

Use the studio development experience to access integrated tools and best-in-class support for open-source frameworks and libraries.

Operationalize at scale

Deploy models and manage and govern them efficiently with MLOps.

Deliver quality solutions

Understand and protect data and models, built for fairness, and improve model quality.

Cloud, Hybrid or On-Prem

Run machine learning workloads anywhere with built-in governance, security, and compliance.

Data Preparation

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Data Labeling

Create, manage, and monitor labeling projects, and automate iterative tasks with machine learning–assisted labeling.

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Data preparation & Data Sets

Perform interactive data preparation with PySpark. Access data and create and share datasets.

 

Model Building & Training

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Notebooks

We use collaborative Jupyter notebooks with interactive computing

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Open-source libraries and frameworks

Get built-in support for Scikit-learn, PyTorch, TensorFlow, Keras, Ray RLLib, and more.

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Automated ML

Automatically train and tune accurate models

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Experimentation

We run experiments and create and share custom dashboards.

Validation & Deployment

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Pipelines and CI/CD

Automate machine learning workflows.

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Hybrid and multicloud

Train and deploy models on-premises and across multicloud.

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Model Registry & Audit Trail

Use the central registry to store and track data, models, and metadata. Automatically capture lineage and governance data with audit trail.

 
 

Management & Monitoring

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Monitor & Analytize

Track, log, and analyze data, models, and resources. Debug models and optimize model accuracy.

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Policies & Securities

Leverage pre-built and custom policies for compliance management. Continuous monitoring with Elastic Security

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Data drift

Detect drift and maintain model accuracy.

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Audit

Trace ML artifacts for compliance.