mlOS

mlOS, developed by Braintoy AI, is a low-code/no-code applied AI platform that aims to simplify the deployment and management of machine learning models. According to the vendor, mlOS provides a unified pipeline for building, deploying, monitoring, and scaling production models. This platform is designed to cater to organizations of all sizes, from small startups to large enterprises. It is used by professionals in various industries, including financial services, energy, insurance, manufacturing, and retail.

Key Features

Unified pipeline for building, deploying, monitoring, and scaling production models: According to the vendor, mlOS offers a unified pipeline that covers the entire lifecycle of machine learning models. This streamlined pipeline aims to ensure consistency and simplify the management process.

Low-code/no-code platform: The vendor states that mlOS is designed to be accessible to both technical and non-technical users. It offers a low-code/no-code interface that allows users to build and customize models without extensive coding knowledge or expertise.

Data engine: According to the vendor, mlOS provides connectivity to over 25 data connectors and offers 60+ data engineering methods and algorithms for various data types, such as tabular, vision, text, time series, audio, and video. This data engine aims to empower users to work with diverse data sources and apply advanced data engineering techniques.

Vision engine: The vendor claims that mlOS includes a vision engine that can help solve advanced computer vision use cases. It offers features like annotation and transfer learning, enabling tasks such as image recognition and object detection.

Digital signal processing engine (DSP): According to the vendor, mlOS includes a digital signal processing engine that can identify, analyze, and align patterns in signals, including sound waves and potentially brain waves. The DSP engine aims to extract valuable information from signals and can be used in various applications.

Auto ml engine: The vendor states that mlOS includes an auto ml engine that automates the machine learning workflow. This engine handles data preprocessing, feature engineering, model selection, hyperparameter optimization, model interpretation, and prediction analysis, with the goal of making the process more efficient.

ml engine: According to the vendor, mlOS's ml engine provides a wide range of algorithms and techniques for building, evaluating, comparing, tuning, training, and retraining classification, regression, deep learning, and clustering models. This engine aims to empower users to work with any type of data and create powerful models.

Model governance engine: The vendor claims that mlOS includes a model governance engine that ensures transparency and explainability in models. This engine helps manage models, making them transparent, explainable, and independent of specific developers.

Deployment engine: According to the vendor, mlOS includes a deployment engine that allows users to deploy models to production as micro-services in minutes. This engine enables integration with legacy or new solutions, enabling real-time decision-making.

Model monitoring engine: The vendor states that mlOS includes a model monitoring engine that continuously monitors model performance and detects decaying models. This engine facilitates the deployment of challenger models and ensures timely replacement when necessary.