Optimizing ML Operations on Edge Real-Time Deep Learning Solutions on Edge Devices
Author: Abhishek Chopde, Senior Data Scientist, AI@Scale, Machine Vision, Conversational AI
Far-reaching applications of Machine Vision In 2023, MLVS is poised to impact fields such as sensor technology, 3D imaging, event-based vision, and model optimization, with applications ranging from absence/presence detection, barcoding, and surveillance to crowd control, defect detection, and logistics automation. Part of the cause of the rapid development of MLVS is the increasing amount of image and video data available. With the abundance of data, there is a greater need for faster and more efficient processing times. You need heavy computational resources to train an intelligent MLVS model that automatically and correctly detects people or classifies objects. However, only some organizations will have the required infrastructure for this computational need. To solve this problem, many organizations rely mainly on cloud computing to remotely process their data with the help of many third-party data centers. While this solves the computing problem, the weaknesses of cloud computing come to the fore with specific applications. There are some privacy concerns with transferring data to servers you have no control over, and any application that requires real-time processing at scale will run into latency problems. The solution to this problem is edge machine learning (edge ML). Deploying edge ML for real-time MLVS Edge ML can process data locally at the point of collection. It addresses security issues by storing sensitive user data in the cloud. It also makes real-time data processing possible, essential for technologies like autonomous vehicles, shipment sorting facilities, and critical patient monitoring systems.
Deep learning is a technology that provides a machine with the ability to process visual images for the tasks that it is performing. For many years, it has aided in raising product quality, accelerating production, and optimizing manufacturing and logistics. This tried-and-true technology is now combining with artificial intelligence to drive the shift to Industry 4.0.
MLVS (Machine Learning Vision Solution) becomes more challenging when the collected data needs to be processed in real-time and operated in specialized environments. Are global institutions and enterprises equipped to implement MLVS effectively in real time?
Machine Learning operations
The success of any AI operation is the effective deployment and tracking of models in production through machine learning operations or MLOps. It is an operational development framework for ML applications that involve the digital architecture of the entire life cycle of developing, testing, optimizing, deploying, and monitoring ML models. MLOps for Machine Vision This is similar to how the human brain’s occipital lobe provides a control center for visual functions. The hippocampus provides one for learning, memory, spatial recognition, and navigation functions. MLOps for machine vision guide global businesses in training their operational machines for visual data recognition, data processing, and storage quickly and efficiently.
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