AI surveillance means utilizing artificial intelligence in video monitoring systems for security purposes. It means employing deep learning algorithms to process live and recorded videos continuously so as to derive useful insights without necessarily requiring a human analysis.
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AI can recognize occurrences, conducts, things, configurations, deviations, visages, number plates, and audios, among other things. As a result of this real-time alerting capability, footage can be searched instantly, crowd analytics facilitated, and even automatic reporting made, among other smart enhancements. In fact, these AI surveillance platforms convert video data into ‘knowledge’ to enhance physical security.
Highlights:
Data Analytics
Gain advanced operational intelligence through data and automation. Transform into an agile organization built around data to confidently tackle more complex use cases and rapidly drive value.
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Data alone is not knowledge. Our Analytics & AI advisors can make sense of the volumes of information your company stores. Drawing on decades of collective data science experience and leveraging state-of-the-art tools, technologies and techniques, we can identify patterns and trends hidden in plain sight.
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AI/ML and MLOps
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Machine Learning Ops (MLOps) is a set of practices and tools that aim to operationalize and streamline the deployment, monitoring, and maintenance of machine learning models in production environments. Here are some benefits of organizations using MLOps to help their data science teams navigate this emerging landscape:
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Enhanced Collaboration: MLOps facilitates collaboration between data science and operations teams, allowing for seamless integration of machine learning models into production systems.
Improved Model Deployment: MLOps provides standardized and automated processes for deploying machine learning models, reducing the time and effort required to move models from development to production.
Scalability and Efficiency: By implementing MLOps practices, organizations can scale their machine learning initiatives more efficiently, enabling them to handle larger volumes of data and deploy more complex models.
Model Monitoring and Management: MLOps enables proactive monitoring of machine learning models in production, allowing teams to detect and address performance issues and drift in model accuracy over time.
Governance and Compliance: MLOps supports the implementation of governance and compliance standards for machine learning models, ensuring that models meet regulatory requirements and internal policies.
Faster Iteration Cycles: With streamlined processes for model deployment and monitoring, data science teams can iterate on their models more quickly, leading to faster innovation and improved model performance.
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Get the meaningful analysis and insights needed to make better decisions regarding when and where to allocate resources and focus efforts.

Data Assessment
We help organizations assess their current data infrastructure, including data sources, data quality, and existing data management processes.

Technology Implementation
We advise organizations on the implementation of data management tools, data analytics platforms, and data visualization software that align with the organization's strategy.

Strategy Development
We work with organizations to develop a comprehensive data strategy tailored to the organization's goals and challenges.

Training and Support
We offer training programs and ongoing support to help organizations build internal capabilities in data analysis, interpretation, and decision-making.

Data Strategy and Governance
We assist organizations in ensuring compliance with data protection regulations and best practices for data privacy and security.

Compliance and Privacy
Weassist organizations in ensuring compliance with data protection regulations and best practices for data privacy and security.