The Internet of Things (IoT) is rapidly changing various industries by improving processes and products. With the growth of IoT devices and data transmissions, enterprises are facing challenges in managing, monitoring, and securing devices. Machine learning (ML) can help generate intelligence by working with large datasets from IoT devices. ML can create accurate models that analyze and interpret the data generated by IoT devices, identify and secure devices, detect abnormal behavior, and prevent threats. ML can also authenticate devices and improve user experiences. Other IoT applications benefiting from ML include predictive maintenance, smart homes, supply chain, and energy optimization. Building ML features on IoT edge devices is possible with TensorFlow Lite.
Building Repeatable and Reliable ML Pipelines with a CI/CD System
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What Is MLOps?
MLOps are the capabilities, culture, and practices (similar to DevOps) where Machine Learning development and operations teams work together across its lifecycle
ETL Pipelines and Data Strategy Overview
Data is a primary component in innovation and the transformation of today’s enterprises. But developing an appropriate data strategy is not an easy task, as modernizing and optimizing data architectures requires highly skilled teams.
Building Machine Learning Models Overview
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Build a Real-time ETL Pipeline for an IoT System
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