Chief Scientific Officer, Machine Medicine Technologies
Data Scientist, Credit Suisse
Senior Data Platform Engineer, GoodRx
NLP Engineer, Speechkit
In this project we will see the end-to-end machine learning development process to design, build and manage reproducible, testable, and evolvable machine learning models by using AWS
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Business Overview
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. Amazon Web Services(AWS) is used as a cloud provider. We would advise you to have a basic understanding of NLP Project on LDA Topic Modelling Python using RACE Dataset 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 Amazon Web Services(AWS)
Tech Stack
➔ Language: Python
➔ Libraries: Flask, gunicorn, scipy, nltk, tqdm, numpy, joblib, pandas, scikit_learn, boto3
➔ Services: Flask, Docker, AWS, Gunicorn
Prerequisites
It is advisable to have a basic knowledge of the following services to get an understanding of the project.
Flask
AWS s3
Aws ECR
AWS ECS
AWS EC2 Load balancer
AWS Code commit
AWS Code Build
AWS Code Deploy
AWS Code Pipeline
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