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Machine Learning Model Deployment

Track :

Artificial Intelligence

Lessons no : 23

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What will you learn in this course?
  • Develop scalable machine learning deployment pipelines using cloud platforms and containerization tools
  • Implement model versioning and continuous integration/continuous deployment (CI/CD) for reliable ML workflows
  • Optimize machine learning models for real-time inference and low-latency applications
  • Apply best practices for model monitoring, logging, and performance evaluation in production environments
  • Utilize popular deployment frameworks like TensorFlow Serving, TorchServe, and ONNX Runtime effectively
  • Design and deploy machine learning models across diverse cloud services such as AWS, Azure, and Google Cloud
  • Troubleshoot common deployment issues, including model drift, latency, and resource constraints
  • Automate deployment processes using scripting, orchestration tools, and automation pipelines
  • Ensure compliance with data privacy, security standards, and ethical considerations in ML deployment
  • Integrate machine learning models seamlessly into existing software systems and APIs

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Lessons | 23


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2025-11-20

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I loved it
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very interesting lessons
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