Name: Picsellia
Author: Picsellia

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Picsellia is an MLOps platform specifically designed for Computer Vision (CV) projects. It caters to companies of all sizes, from startups to large enterprises. Data scientists, machine learning engineers, computer vision engineers, AI researchers, and software developers from various industries such as manufacturing, energy, retail, security, smart city, farming, and sports analytics rely on Picsellia to develop, deploy, and maintain CV models at scale.

## Key Features

**Data Management**: Picsellia offers a centralized platform for efficient storage and management of CV data, including images, annotations, and metadata. The platform allows easy tracking and management of different dataset versions, ensuring reproducibility and traceability. Picsellia provides intuitive data visualization tools for in-depth analysis and insights into CV datasets. It also supports data annotation workflows for tasks like bounding boxes, polygons, keypoints, and other annotations.

**Experiment Tracking**: Picsellia facilitates the organization and tracking of CV experiments, including model training, hyperparameter tuning, and evaluation metrics. The platform enables easy comparison of different models and experiments, aiding in the identification of effective approaches. During model training and evaluation, Picsellia automatically tracks and logs various metrics, providing valuable insights into model performance. Collaboration features enable seamless sharing of experiments, insights, and annotations among team members.

**Model Deployment**: Picsellia supports model versioning, simplifying the management and deployment of CV models. Trained models can be deployed as APIs, allowing seamless integration with other applications and systems. The platform ensures scalability of model deployments to handle high request volumes, ensuring optimal performance under heavy loads. Picsellia also provides monitoring capabilities to track the performance and behavior of deployed models, enabling prompt issue detection and resolution.

**Model Monitoring**: With Picsellia, real-time monitoring of deployed CV models is possible, enabling performance tracking and anomaly detection. The platform helps identify concept drift and data drift in CV models, allowing timely corrective actions to maintain model accuracy over time. Outlier detection features assist in handling unusual or unexpected inputs that may impact model performance. Users can set up alerts and notifications to receive timely updates on model performance and critical issues.

**Automated Pipelines**: Picsellia allows the creation of automated pipelines for streamlined CV workflows, covering data preparation, model training, and deployment. The platform enables the orchestration of complex CV workflows, including data preprocessing, feature extraction, model training, and evaluation. Customization options are available to tailor pipelines to specific requirements, incorporating preferred tools, libraries, and frameworks. Automation of repetitive tasks ensures time and effort savings, while maintaining consistency and reproducibility.

Watsonx.ai is part of the IBM watsonx platform that brings together new generative AI capabilities, powered by foundation models, and traditional machine learning into a studio spanning the AI lifecycle. Watsonx.ai can be used to train, validate, tune, and deploy generative AI, foundation models, and machine learning capabilities, and build AI applications with less time and data.

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Machine learning tools are algorithmic applications of artificial intelligence that give systems the ability to learn and improve without ample human input; similar concepts are data mining and predictive modeling. They allow software to become more accurate in predicting outcomes without being explicitly programmed. The idea is that a model or algorithm is used to get data from the world, and that data is fed back into the model so that it improves over time. It’s called machine learning because the model “learns” as it is fed more and more data.

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