DeepSparkChaker's repositories
GiveMeCredit_Top5_Solution_Kaggle
Banks play a crucial role in market economies. They decide who can get finance and on what terms and can make or break investment decisions. For markets and society to function, individuals and companies need access to credit. Credit scoring algorithms, which make a guess at the probability of default, are the method banks use to determine whether or not a loan should be granted. This competition requires participants to improve on the state of the art in credit scoring, by predicting the probability that somebody will experience financial distress in the next two years. The goal of this competition is to build a model that borrowers can use to help make the best financial decisions.
FraudDetection_Fastapi
this api will detect fraud
Fastapi_NLP_Docker
Deploy sentiment analyis with Fastapi
kaggle-solutions
🏅 Collection of Kaggle Solutions and Ideas 🏅
machine-learning-imbalanced-data
Code repository for the online course Machine Learning with Imbalanced Data
anonymization-api
How to build and deploy an anonymization API with FastAPI
awesome-mlops
A curated list of references for MLOps
booking_extra_baggage
At eDreams ODIGEO we are always looking for ways to improve customer satisfaction. With this objective in mind, we would like to predict whether a new customer
deploy-keras-model-in-production
Using Flask to deploy saved keras model in production. Flask based REST API to expose the prediction endpoints.
deploying-machine-learning-models
Example Repo for the Udemy Course "Deployment of Machine Learning Models"
fastapi-model-deployment
An example for deploying Tensorflow 2 models with Docker and Fast API
fastapi-nlp
To showcase the features of building REST API's by FastAPI for Machine learning and Deep learning models
MLOps
Learn how to responsible deliver value using MLOps.
ner_spacy_app
Flask app of named entity recognition with spacy
Spam_Detection_Fastapi
This api will let us detect spam messages
Spam_Detection_Fastapi_Streamlit_Docker_Compose
This repo contains code for a small webapp to predict if we have a spam messages . The application consists of a frontend and a backend part and is used to demonstrate how to serve ML models as microservices through an API. The frontend is built with Streamlit and used to acquire messages.
TextClassification-Keras
Text classification models implemented in Keras, including: FastText, TextCNN, TextRNN, TextBiRNN, TextAttBiRNN, HAN, RCNN, RCNNVariant, etc.