Head of Data science, OutFund
Data Science, Yelp
Dev Advocate, Pinecone and Freelance ML
University of Economics and Technology, Instructor
MLOps on GCP - Solved end-to-end MLOps Project to deploy a Mask RCNN Model for Image Segmentation as a Web Application using uWSGI Flask, Docker, and TensorFlow.
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Business Overview
87% of Data Science Projects never make it to production - VentureBeat
Machine learning operations are widely known as MLOps include various technologies, processes, and practices that automate deployment, monitoring, and management of machine learning models in production. Many organizations are turning towards machine learning and Artificial intelligence. MLOps advocates automation and monitoring at all steps of the ML system. In this project, we aim to provide hands-on experience in MLOps by using cloud computing. Google cloud platform is used as a cloud provider. We would advise you to have a basic understanding of Image Segmentation using Mask R-CNN with Tensorflow before jumping into this project.
Aim
To provide an end-to-end machine learning development process to design, build and manage reproducible, testable, and evolvable machine learning models by using Google Cloud Platform(GCP)
Tech Stack
➔ Language: Python
➔ Services: GCP, uWSGI, Flask, Kubernetes, Docker
➔ Libraries: TensorFlow, mrcnn, matplotlib, os, flask
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