Detail: This app is a microservicce that is developed with Nameko and has these components:
Tools: Docker, Nameko, Hbase, Kafka, ELasticsearch, Logstash, Kibana, Fastapi, Treafik, Random-forest
Detail: In this project, a trained model in core of web service is used for classification the sent data:
Tools: Docker, Fastapi, Random Forest Model, Websocket, Cassandra
Detail: In this app after start the atm data will be produced into kafka, then using spark streaming the data from that topic in kafka will be parsed and cleaned and saved into potgresql and hdfs; also the spark streaming job will be run with Airflow dag and the whole app is developed and deployed with docker and docker-compose
Tools: Airflow, Docker, Spark, Kafka, PostgresSql, HDFS
Detail: In this app the price of Tesla stock is going to obtained and saved into hdfs every hour and at the end of the day a random forest model is going to be trained to predict next day prices based on history prices; the model also is going to be saved into MinIO. All these tasks is developed on Airflow and built and deployed with docker. At end there is a bokeh app the plot history of prices and uses the model in minio to get next day price in real-time!
Tools: Docker, Airflow, Spark, Minio, Bokeh, HDFS, Random-Forest
Detail: This app will collect real-time price of bitcoin in USD and save data into csv and every 12h the model for bitcoin value prediction is going to be trained; these tasks are implemented with Twisted; finaly the result is ploted with Bokeh and have api with Tornado. This app is developed and deployed with docker on Heroku.
Tools: Docker, Heroku, Tornado, Twisted, Bokeh, Random-Forest
Detail: This chat application could have as many users and rooms, that never stores data; it has been develeped with Fastapi websocket; built and deployed with docker on Heroku.
Tools: Fastapi, Docker, Websocket, Heroku
Detail: The Deepface library is deployed with Fastapi to check and compare faces; it uses mongodb as db and developed and deployed with docker on Heroku.
Tools: Docker, Tensorflow, Deepface, Fastapi, Mongodb, Heroku
Detail: Music recommender with Item based collaborative filtering based on KNN:
Tools: Docker, Fastapi, Pandas, Scikit-learn, Heroku
Detail: Book Recommender with Item based collaborative filtering based on KNN (based on kaggle dataset):
Tools: Cassandra, Flask, Restful-api, Pandas, Scikit-learn, Gunicorn, Redis, RabbitMq, Celery, Docker
Detail: Music recommender with Item based collaborative filtering based on KNN:
Tools: Flask, Pandas, Postgres, Docker, Nginx, Celery, Rabbitmq, Redis
Detail: Movie recommender with Item based collaborative filtering based on KNN (based on movielens data):
Tools: Flask, Pandas, Mongodb, Docker, Nginx, Celery, Rabbitmq, Redis
Detail: A simple microservice implemnted with Nameko, Fastapi and Flask; built with docker and deployed using Kubernetes
Tools: Nameko, RMQ. Redis, Flask, Fastapi, Docker, Kubernetes
Detail: Hands on Load Testing with Locust; This app is restful service developed with Starlette and Postgres as database. All services are built and deployed using docker.
Tools: Kafka, Faust, Locust, Starlette, Asyncpg, tortoise, redis, Docker
Detail: A simple rest service with Fastapi; dockerized and deployed with k8s
Tools: Fastapi, Kubernetes, Docker, Docker-registery
Detail: A simple web service that uses posgres is developed with Fastapi on docker and deployed with k8s
Tools: Docker, Docker-registery, Fastapi, Postgresql, Tortoise-orm, Asyncpg, Kubernetes
Detail: Just a simple microservice developed with Nameko and deployed with k8s
Tools: Docker, Nameko, Redis, RabbitMQ, Kubernetes
Detail: The data is produced into a topic in kafka, the with logstash configuration; the data will be stored in elasticsearch and using kibana a dashboard could be developed. This add is build and deployed with docker
Tools: Kafka, Logstash, Elasticsearch, Kibana, Docker-compose
Detail: A simple Faust stream processing app and how to mock the asynchronous process of Faust for unit tests; also mocking mongodb. This app is built with docker.
Tools: Faust, Mongodb, Mock, Unit tests, Kafka, Docker
Detail: A dashboard for the gold price data in mysql using grafana; also monitoring kafka and the topics data rate.
Tools: Prometheus, Grafana, Mysql, Kafka, Docker
Detail: Every Second the Bitcoin value is going to get processed and saved into postgres, if the value goes under a specific pre-defined value; it will alarm. This app is built and deployed with docker.
Tools: Faust, Kafka, Asyncpg, Tortoise, Docker, Redis
Detail: In this app:
Tools: Cassandra, Pandas, Scikit-learn, ETL, Docker
Detail: Implementation of training, pre-trained keras and torch models for categorical datasets; this app is built using docker.
Tools: Docker, Tensorflow, Torch, Keras
Detail: Streams of GPS data are produced into kafka; using spark-streaming the data will be analyzed and stored into postgres; This app is built and deployed with docker.
Tools: Docker, Kafka, Spark, Postgres
Detail: Implementation of very simple web applications and trying to do some stress tests; all apps are built and deployed with docker.
Tools: Docker, Django, Flask, Fastapi, Falcon, Rust, Go
Detail: Deployment of Jupyterlab using dockerhub and K8S.
Tools: Centos7, Kubernetes, Jupyter, Docker
Detail: Hands on Spark using scala and sbt. All process is built and deployed with docker.
Tools: Spark, Scala, Docker
Detail: Hands on Kafka, Druid, Superset; for dashboards. All services are built and deployed using docker.
Tools: Kafka, Druid, Superset, Docker
Detail: A very simple rest api app developed with rust; using docker for building and Kubernetes for deployment.
Tools: Rust, Docker, Kubernetes
Detail: Developing Java and Scala apps using docker.
Tools: Scala, Java, Docker
Detail: Hands on Mysql, Mongodb, Postgres, MSSQL using python and jupyter. All services are built and deployed using docker.
Tools: Mysql, Mongodb, Postgres, MSSQL, Docker
Detail: An store application:
Tools: Django Rest Framework, Postgre, Docker, Graphiql, Graphene
Detail: Quora Duplicate Question Classifier on XGBOOST with 81% accuracy
Tools: Xgboost, Pandas, Scikit-learn
Detail: Predicting Consumer Product based on Consumer‘s Complaints with 98% accuracy
Tools: Keras, Pandas, Tensorflow