Simplifying complexity

Data engine

Connect to 25+ data connectors including pi historian. Utilize query builder and data merger modules, 60+ data engineering methods and algorithms for tabular, vision, text, time series, audio, video and more.

Vision engine

Solve advanced and complex computer vision use cases. From annotation to using transfer learning and more.

Digital signal processing engine (DSP)

Identify, analyze, align patterns in signals. De-noise and extract valuable information from signals. From sound waves to brain waves DSP engine will help you distinguish between signal patterns (coming soon).

Auto ml engine

Autopilot your machine learning models with automated machine learning workflow. From data preprocessing, feature engineering, model selection, hyperparameter optimization, model interpretation to prediction analysis, mlOS auto ml has your back.

ml engine

Build, evaluate, compare, tune, train new and retrain existing classification, regression, deep learning and clustering models on any data.

Model governance engine

Build solutions your customers will love and trust. Discover a workflow for reviewing production solutions. Break the BlackBox open by managing models and making solutions transparent, explainable, developer independent and more.

Deployment engine

Deploy models to production as micro-services in minutes. Integrate models into legacy or new solutions and make decisions in real-time.

MLOps

mlOS MLOps pipeline organizes and ensures consistency of data. From data exploration to model deployment, mlOS MLOps pipelines aid in updating models as data changes and model degrades. Continuous integration, continuous deployment and retraining workflows make it easy to operationalize end-to-end machine learning solutions.

Data engine

Connect to 25+ data connectors including pi historian. Utilize query builder and data merger modules, 60+ data engineering methods and algorithms for tabular, vision, text, time series, audio, video and more.

Vision engine

Solve advanced and complex computer vision use cases. From annotation to using transfer learning and more.

Digital signal processing engine (DSP)

Identify, analyze, align patterns in signals. De-noise and extract valuable information from signals. From sound waves to brain waves DSP engine will help you distinguish between signal patterns (coming soon).

Auto ml engine

Autopilot your machine learning models with automated machine learning workflow. From data preprocessing, feature engineering, model selection, hyperparameter optimization, model interpretation to prediction analysis, mlOS auto ml has your back.

ml engine

Build, evaluate, compare, tune, train new and retrain existing classification, regression, deep learning and clustering models on any data.

Model governance engine

Build solutions your customers will love and trust. Discover a workflow for reviewing production solutions. Break the BlackBox open by managing models and making solutions transparent, explainable, developer independent and more.

Deployment engine

Deploy models to production as micro-services in minutes. Integrate models into legacy or new solutions and make decisions in real-time.

Model monitoring engine

Deploy challenger models and detect, retrain, and replacement decaying models. Monitor model performance, and swap old models with new ones when needed.

Algorithm manager

Add secret or proprietary recipes in mlOS by adding custom data wrangling, feature extraction or preprocessing algorithms. Add custom or different flavours of general-purpose or deep learning algorithms for supervised and unsupervised learning.

Job scheduler

Write and schedule scripts to refresh data, pull and push data into the databases, perform real-time or interval-based predictions, edge communication and more.

Variable store

Light weight data lake for tracking and tracing data lineage. Version control for data – real-time data ingestion for Internet of Things (IoT) and other applications.

Custom app deployment

Deploy custom models as micro-services. Import existing models created outside of mlOS and deploy as micro-services. Serve analytics through API’s and more.

mlOS data lab

write, run, execute and update python code in a notebook without any required setup. Rich interactive coding experience that allows you to add new cells and use any of the functionalities that python offers including interactive data visualization. Import existing notebooks or store, share and export code in GitHub or GitLab. View and execute mlOS data lab notebooks in Jupyter notebook, Jupyter Lab and other compatible frameworks.

Project manager

Manage all projects including the health of mlOS instances. share and collaborate on projects.