Picsellia

What Picsellia does

Picsellia is an end-to-end MLOps platform designed for computer vision, covering all six stages of the CV lifecycle: data management, annotation, training, deployment, and production monitoring. It centralizes visual data, offers AI-assisted labeling, tracks experiments for reproducibility, and provides real-time monitoring for deployed models. The platform is built for ML engineers, offering a full Python SDK, enterprise-grade security (ISO 27001:2022 certified), and flexible deployment options including cloud, on-premise, or hybrid. It aims to replace fragmented toolchains with a single, integrated solution for teams needing to ship vision AI for real, particularly in industrial and regulated sectors.

Products

• Data EngineModule for data management, versioning, and preparation, including unlimited data storage, dataset versioning, smart data curation, visual similarity search, CLIP embeddings, and multi-format support.
• VisionAI FactoryModule for model training, experimentation, and deployment, featuring experiment tracking, model registry, pre-built pipelines, custom training pipelines, one-click GPU allocation, and model deployment.
• Reliability EngineModule for model monitoring, performance tracking, and drift detection, offering real-time monitoring, anomaly detection, drift detection, feedback loops, continuous training, and shadow deployments.
• Labeler SeatsProvides access for annotation workforce with unlimited seats available.
• Annotation ServicesProfessional labeling services with trained annotators for projects, ensuring quality for all task types.
• DatalakeCentralizes visual data, connects cloud storage, ingests images and videos, and allows exploration with visual similarity search and smart tagging. Manages datasets and assets.
• Labeling ToolAI-assisted labeling tool that cuts annotation time, ensures quality control, and supports annotation campaigns.
• AI LaboratoryManages experiment tracking, model versioning, and ensures reproducibility of results with automated pipelines.
• Model DeploymentEnables deployment of models with confidence, offering real-time monitoring to catch drift.
• Model MonitoringTracks prediction confidence in real-time, detects drift, provides performance dashboards, and flags anomalies.
• Automated PipelinesSets up pipelines for scheduled or trigger-based retraining, and enables zero-downtime deployment of updated models.

Key capabilities

• End-to-end platform for computer vision MLOps
• Data management and versioning
• AI-assisted labeling and annotation campaigns
• Experiment tracking and reproducibility
• Model training and deployment
• Real-time production monitoring and drift detection
• Full Python SDK and API access
• Enterprise-grade security (ISO 27001:2022 certified)
• Flexible deployment: SaaS, on-premise, hybrid, air-gapped
• Role-Based Access Control (RBAC)
• 99.9% Uptime SLA
• Scalability from proof-of-concept to millions of predictions
• Integration with existing ML frameworks (PyTorch, TensorFlow, Keras, Ultralytics, Hugging Face)
• Integration with cloud providers (Amazon S3, Google Cloud, Azure, NVIDIA Jetson, SageMaker)
• Integration with data & MLOps ecosystem (Snowflake, Databricks, MLflow, Weights & Biases, Jupyter)
• Usage-based pricing with volume discounts
• Human-in-the-Loop workflows for expert validation
• Active Learning for smart sampling and labeling efficiency
• Automated retraining and redeployment
• Multi-reviewer workflows for annotation quality
• Golden test datasets for model evaluation
• Per-defect metrics for granular performance tracking
• Asynchronous monitoring for non-blocking production insights
• Edge inference capability
• Geo-referenced data management for agriculture (GPS metadata, multi-spectral layers, orthomosaic support)
• Model lifecycle management

Use cases

• Automated Defect Detection in manufacturing
• Visual inspection and quality control in manufacturing
• Surface Defect Detection
• Assembly Verification
• Traceability in manufacturing
• Packaging Inspection
• Safety Compliance monitoring in manufacturing
• Industrial inspection automation
• Waste sorting machine development
• Construction and waste monitoring
• Precision agriculture (drone imagery processing, crop analysis)
• Infrastructure inspection and monitoring in energy & utilities
• Predictive Maintenance in energy
• Security & Surveillance in energy
• Safety Protocols in energy
• Automated Waste Sorting
• Contamination Detection in waste management
• Fill Level Monitoring in waste management
• Material Composition analysis in waste management
• Reducing model development time
• Improving model accuracy and reliability
• Managing massive data volumes (e.g., aerial imagery)
• Ensuring experiment reproducibility
• Streamlining annotation campaigns
• Continuous learning for models in dynamic environments

AI approach

Picsellia provides an end-to-end MLOps platform specifically for computer vision. It enables users to manage visual data, annotate, train, deploy, and monitor computer vision models. The platform supports various stages of the CV lifecycle, including data management, annotation, experiment tracking, model deployment, and production monitoring. It emphasizes reproducibility, data lineage, and continuous learning for models, particularly in dynamic environments like waste management. The platform integrates with popular ML frameworks and cloud infrastructure.

Tech named: PyTorch, TensorFlow, Keras, Ultralytics, Hugging Face, CLIP embeddings, YOLOv8, Airflow, MLflow

Industries served

• Manufacturing
• Energy
• Aerospace
• Defense
• Agriculture
• Waste Management
• Construction
• Utilities

What it says sets it apart

• End-to-end platform covering all 6 stages of the CV lifecycle, replacing fragmented tools
• Built by ML engineers who experienced the pain of fragmented workflows
• Engineer-first approach with full Python SDK, reproducible experiments, and complete data lineage
• Enterprise-grade security (ISO 27001:2022 certified) and compliance (EU AI Act–ready)
• Flexible deployment options: on-premise, air-gapped, EU & US data residency
• Focus on production-ready AI infrastructure for industrial and regulated teams
• Transparency: full visibility into ML pipeline, reproducible experiments, complete data lineage, data ownership
• Simplicity: powerful features without complexity, reducing cognitive load
• Continuous learning loop for models, adapting to changing data streams (e.g., waste management)
• Specialized features for specific industries like geo-referenced data for agriculture and human-level precision for manufacturing inspection