ml-deployment

There are 0 repository under ml-deployment topic.

cceyda / torchserve-dashboard
Management Dashboard for Torchserve
ml-deployment pytorch streamlit torchserve
Language:Python 120
manhph2211 / ml-deployment
Pushing Text To Speech models into production using torchserve, kubernetes and react web app :smile:
text-to-speech kubernetes react tts-api voice-synthesis tts-demo torchserve tts-service mlops mlopsvn mern-project mern-stack-app expressjs mongodb text-to-speech-app text-to-speech-service vietnamese-text-to-speech ml-deployment
Language:Python 21
gasparian / ml-serving-template
Serving large ml models independently and asynchronously via message queue and kv-storage for communication with other services [EXPERIMENT]
fasttext semantic-clustering clustering falcon bjoern rabbitmq redis pubsub machine-learning ml-ops ml-serving ml-production ml-deployment ml-web-app redis-client rabbitmq-client rabbitmq-python pika rpc
Language:Python 15
charumakhijani / spark-ml-deployment
spark ml-deployment batch-prediction ml-production machine-learning-pipelines
Language:Jupyter Notebook 11
HemanthJoseph / ML-Projects-with-Deployment
An end-to-end Machine Learning project from writing a Jupyter notebook to check the viability of the solution, to breaking down the same into modular code, creating a Flask web app integrated with a HTML template to make a website interface, and deploying on AWS and Azure.
aws-ec2 aws-elastic-beanstalk azure machine-learning ml-deployment
Language:Python 11
anomaly-detection-platform
koushikvikram / anomaly-detection-platform
🔥🔥🔥🔥🧊🔥🔥 A Data Platform for Monitoring and Detecting Anomalies in Real-Time.
docker grafana prometheus jupyter-notebook python anomaly-detection machine-learning mlops fastapi ml-platform ml-engineering caddy ml-deployment koushik-vikram koushikvikram dashboard real-time-machine-learning real-time-monitoring ml-monitoring
Language:Jupyter Notebook 10
jesussantana / DeepLearning.AI-Introduction-to-Machine-Learning-in-Production
In the first course of Machine Learning Engineering for Production Specialization, you will identify the various components and design an ML production system end-to-end: project scoping, data needs, modeling strategies, and deployment constraints and requirements; and learn how to establish a model baseline, address concept drift, and prototype the process for developing, deploying, and continuously improving a productionized ML application.
ml-lifecycle ml-deployment machine-learning ml-engineering mlops
Language:Jupyter Notebook 8
dao-duc-tung / eks-ml-deploy-system
A EKS-based ML deployment solution
eks kubernetes ml-deployment ml-system mlops
Language:Python 6
dao-duc-tung / ecs-ml-deploy-system
An ECS-based ML deployment solution
mlops ecs ml-deployment ml-system
Language:Python 5
ksm26 / ROS-based-3D-detection-Tracking
Deployment of 3D-Detection and Tracking pipeline in simulation based on rosbags and real-time.
3d-detection deployment detection-model lidar lidar-point-cloud ml-deployment mmdeploy onnx openpcdet pointpillars regnet ros tensorrt tracking tracking-algorithm tracking-by-detection nuscenes
Language:Python 5
manhph2211 / ml-mornitoring
Simply Automate Monitoring Infrastructure with Terraform, Ansible, AWS EC2, Nginx, Prometheus, Grafana and Github Actions :smile:
ansible-playbook grafana infrastructure-monitoring ml-deployment mlops mlopsvn nginx-proxy prometheus terraform-ansible-integration terraform-aws terraform-github-actions
Language:HCL 5
manhph2211 / ml-project-template
This repo shows how to implement a simple image generation app that uses Jax-Implementation of a conditional VAE, Jax, fastapi, docker, streamlit, heroku, ec2, and cloudflare :smiley:
fastapi heroku image-generation jax streamlit vae play-with-jax generative-models jax-implementation mlops-project mlopsvn ml-deployment
Language:Jupyter Notebook 4
CleverInsight / predicteasy
Powerful AutoML toolkit
automl auto-feature-engineering auto-feature-selection auto-feature-extraction automl-pipeline ml-deployment
Language:Jupyter Notebook 3
Fatiima-Ezzahra / Operationalizing-Machine-Learning-in-Azure
This project is part of the Udacity Azure ML Nanodegree. In this project, we use Azure to configure a cloud-based machine learning production model, deploy it, and consume it. We also create, publish, and consume a pipeline.
automl azure azure-ml azure-ml-pipeline ml-pipeline ml-deployment swagger
Language:Jupyter Notebook 3
Rahulgarg95 / fitbit-calorie
A regression model to predict calories burnt using values from multiple sensors.
azure calories-burnt clustering data-preprocessing flask-restful machine-learning ml-deployment mongodb object-oriented-programming pandas randomforest-regressor sklearn xgboost-regressor
Language:Jupyter Notebook 3
partheev / campus-placement-analysis
Identifying Patterns and Trends in Campus Placement Data using Machine Learning
campus-placements flask machine-learning ml-deployment react redhat-openshift-on-ibm-cloud
Language:Jupyter Notebook 2
schmidtbri / ml-base
Base classes and utilities that are useful for deploying ML models.
mlops ml-deployment
Language:Python 2
alpemek / ML-Deployment
A basic example of deploying machine learning applications
docker docker-compose ml-deployment object-detection
Language:Python 1
chawla201 / Airplane-Accident-Severity-Prediction
Ensemble Learning | Flask
ensemble-learning bagging boosting machine-learning classification-algorithims decison-trees xgboost adaboost gradient-boosting python seaborn exploratory-data-analysis hackerearth flask-application ml-deployment
Language:Jupyter Notebook 1
COOLMudi / Corporate-Bankruptcy-Technocolab
This is Mudit Vyas worked as ML Developer Intern , Team Leader in Technocolabs Software.This is Internship Project For Technocolabs Software.
bankruptcy-dataset bankruptcy-prediction classification-algorithm data-analytics data-science eda machine-learning machine-learning-models ml-deployment numpy pandas python seaborn streamlit
Language:Jupyter Notebook 1
harisonmg / sentiment-analysis
Demonstration of building a machine learning model and deploying it on a web app.
nlp ml ml-deployment
Language:Jupyter Notebook 1
varvarvarvar / language_identification
:globe_with_meridians: Language identification for Scandinavian languages
prometheus grafana pytorch mlflow docker s3 flask language-identification mlflow-docker mlflow-ui lstm pytorch-lstm language-detection mlflow-pytorch ml-monitoring ml-pipeline ml-serving ml-deployment cicd wili-2018-dataset
Language:Python 1
ArkadiuszSlowik / Differentiate-customer-groups
The goal of this project is to build a data driven model that finds the customer groups that lead to good ROIs (Return on Investment).
api flask hyperparameter-tuning machine-learning machine-learning-algorithms ml-deployment sklearn
Language:Jupyter Notebook 0
IdrisseAA / MNIST-Audio-Digit-Classifier
We will apply deep learning techniques for the classification of the free-spoken-digit-dataset, akin to an audio version of MNIST.
audio-classification deep-learning mnist-classification spectrogram speech-classification-python flask ml-deployment
Language:Python 0
markbotros1 / portfolio-app
A web app to showcase some of my favorite projects
aws-lambda ml-deployment portfolio reactjs rest-api
Language:TypeScript 0
Rahulgarg95 / SensorFaultDetection
A classification model built to determine the issues in system given data from multiple sensors.
aws-s3 classification-model clustering data-preprocessing flask machine-learning ml-deployment mongodb object-oriented-programming pandas sklearn
Language:Python 0
RicardoRibeiroRodrigues / AWS-IaC-mlops-pipeline
Terraform code, aws scripts and pipeline templates for the AWS-IaC-mlops-pipeline.
mlops aws iac python sagemaker terraform ml-deployment codepipeline
Language:Python 0
ShahDishank / placement_predictor
It is a website that utilize machine learning model to predict the probability of getting placed and salary.
machine-learning ml-deployment pickle-file python
Language:Jupyter Notebook 0
Shreyjain203 / Machine-Learning-Model-to-Production-Website-using-GKE
An end-to-end ML model deployment pipeline on GCP: train in Cloud Shell, containerize with Docker, push to Artifact Registry, deploy on GKE, and build a basic frontend to interact through exposed endpoints. This showcases the benefits of containerized deployments, centralized image management, and automated orchestration using GCP tools.
artifact-registry docker-container docker-image google-cloud-platform google-kubernetes-engine kubernetes-deployment machine-learning-deployment ml-deployment
Language:Python 0
vinit714 / AI-Powered-Text-Generation-and-Sentiment-Analysis-Dashboard
This Flask web application performs text sentiment analysis and text generation based on user input. Users can input text, and the application will analyze its sentiment using NLTK's Vader sentiment analysis tool and generate additional text using the GPT-2 model.
artificial-intelligence css flask flask-web-framework front-end-development gpt-2 html huggingface-transformers ml-deployment natural-language-processing nlp nltk python sentiment-analysis text-generation web-interface webdevelopment
Language:Python 0
murilo-cunha / mlops-and-deployment
Slides for ML deployment and MLOps
lecture-slides ml ml-deployment mlops slides slidev
Language:CSS
Priyansi / ml-deployment-snippets
Code Snippets for an Image Classification model deployed using FastAPI and Streamlit.
ml-deployment image-classification fastapi streamlit
Language:Python
richardvlas / Operationalizing-Machine-Learning
In this project we use Microsoft Azure Cloud Computing Services to configure a cloud-based machine learning production model, deploy it, and consume it. We will also create, publish, and consume a pipeline.
azure swagger ml-pipeline ml-deployment azure-ml azure-mlstudio azure-deployment
Language:Jupyter Notebook
shrutibalan4591 / Store-Sale-Prediction
An end-to-end ML project, which aims at developing a regression model for the problem of predicting the sales of a given product, based on its properties like item category, weight, visibility, MRP, type of outlet the product is sold, size of the outlet etc.
data data-analysis data-mining flask html-css lasso-regression linear-regression machine-learning ml-deployment ml-model-development python random-forest-regression regression retail sales xgboost-regression
Language:Python

ml-deployment

cceyda / torchserve-dashboard

manhph2211 / ml-deployment

gasparian / ml-serving-template

charumakhijani / spark-ml-deployment

HemanthJoseph / ML-Projects-with-Deployment

koushikvikram / anomaly-detection-platform

jesussantana / DeepLearning.AI-Introduction-to-Machine-Learning-in-Production

dao-duc-tung / eks-ml-deploy-system

dao-duc-tung / ecs-ml-deploy-system

ksm26 / ROS-based-3D-detection-Tracking

manhph2211 / ml-mornitoring

manhph2211 / ml-project-template

CleverInsight / predicteasy

Fatiima-Ezzahra / Operationalizing-Machine-Learning-in-Azure

Rahulgarg95 / fitbit-calorie

partheev / campus-placement-analysis

schmidtbri / ml-base

alpemek / ML-Deployment

chawla201 / Airplane-Accident-Severity-Prediction

COOLMudi / Corporate-Bankruptcy-Technocolab

harisonmg / sentiment-analysis

varvarvarvar / language_identification

ArkadiuszSlowik / Differentiate-customer-groups

IdrisseAA / MNIST-Audio-Digit-Classifier

markbotros1 / portfolio-app

Rahulgarg95 / SensorFaultDetection

RicardoRibeiroRodrigues / AWS-IaC-mlops-pipeline

ShahDishank / placement_predictor

Shreyjain203 / Machine-Learning-Model-to-Production-Website-using-GKE

vinit714 / AI-Powered-Text-Generation-and-Sentiment-Analysis-Dashboard

murilo-cunha / mlops-and-deployment

Priyansi / ml-deployment-snippets

richardvlas / Operationalizing-Machine-Learning

shrutibalan4591 / Store-Sale-Prediction