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Embedded AI Integration with MATLAB and Simulink

Pickl AI

This involves: Data Preparation : Collect and preprocess data to ensure it is suitable for training your model. This step ensures that the AI component is correctly linked within the overall system architecture. Model Selection : Choose appropriate algorithms (e.g.,

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A Guide to LLMOps: Large Language Model Operations

Heartbeat

Deployment : The adapted LLM is integrated into this stage's planned application or system architecture. This includes establishing the appropriate infrastructure, creating communication APIs or interfaces, and assuring compatibility with current systems.

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Accelerate machine learning time to value with Amazon SageMaker JumpStart and PwC’s MLOps accelerator

AWS Machine Learning Blog

Such a pipeline encompasses the stages involved in building, testing, tuning, and deploying ML models, including but not limited to data preparation, feature engineering, model training, evaluation, deployment, and monitoring.