Discussing the deployment process of Machine Learning models. Deployment is an important part of any Machine Learning cycle, the main application of Deployment is letting the end-users use the ML models and in order to collect real-time data, that is prone to noise.
Also discussing various methods by which a DL/ML model can be deployed in production. I will also be discussing related data pipelines, specifically for Image and numerical data, that are important for IoT applications like remote sensing and drones. These methods will be including
Azure VM service
Azure App/Web Service
Heroku
Azure Functions - Serverless
API Generation
Docker - Containerization
Edge Devices - NVIDIA JETSON NANO, Raspberry PI, etc
Ankur is currently working as a Backend Engineer (AI) at Wipro Technologies. He also likes to work in tech fields like Python, Machine Learning, Data Analytics, IoT, Robotics, Cloud Computing, DevOps, and Databases. He recently won Smart India Hackathon (SIH'20). He has 2+ experience of in Communities, Tech Events, and webinars, where he has closely worked with TFUG Mysore, Google CrowdSource …
Ankur is currently working as a Backend Engineer (AI) at Wipro Technologies. He also likes to work in tech fields like Python, Machine Learning, Data Analytics, IoT, Robotics, Cloud Computing, DevOps, and Databases. He recently won Smart India Hackathon (SIH'20). He has 2+ experience of in Communities, Tech Events, and webinars, where he has closely worked with TFUG Mysore, Google CrowdSource Pune, IOSD UIET KUK, MSP UIET KUK, etc. In his free time, he loves to cook, read about ancient architecture and discuss the cosmos
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