A curated list of resources to deep dive into the intersection of applied machine learning and threat detection.
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Updated
Sep 23, 2020
A curated list of resources to deep dive into the intersection of applied machine learning and threat detection.
Cloud Solutions for machine learning and operation.
Machine Learning Engineering for Production (MLOps) Coursera Specialization
Kueski Challenge - Vacante de Machine Learning Engineer
The Golang library for Modzy Machine Learning Operations (MLOps) Platform
The official Java library for the Modzy Machine Learning Operations (MLOps) Platform
A framework for conducting MLOps.
A curated list of awesome open source tools and commercial products that will help you train, deploy, monitor, version, scale, and secure your production machine learning on kubernetes 🚀
The project comprises a real-time tweets data pipeline, a sentimental analysis of the tweets module, and a Slack bot to post the tweets' sentiments. The project uses SentimentIntensityAnalyzer from the VaderSentiment library. The analyzer gives positive, negative, and compound scores for small texts (such as tweets in this case). The real-time d…
The official JavaScript SDK for the Modzy Machine Learning Operations (MLOps) Platform.
A modern, enterprise-ready business intelligence web application
In this tutorial we'll bring the TensorFlow 2 Quickstart to Valohai, taking advantage of Valohai versioned experiments, data inputs, outputs and exporting metadata to easily track & compare your models.
This project contains the production-ready Machine Learning solution for detecting and classifying Covid-19, Viral disease, and No disease in posteroanterior and anteroposterior views of chest x-ray
🌀 #12. "Machine Learning Operations (MLOps) - Airline Passenger Satisfaction Prediction"
Carefully curated list of awesome data science resources.
Python library for Modzy Machine Learning Operations (MLOps) Platform
Explore a modular, end-to-end solution for heart disease prediction in this repository. From problem definition to model evaluation, dive into detailed exploratory data analysis. Experience seamless integration with MLOps tools like DVC, MLflow, and Docker for enhanced workflow and reproducibility.
Free Open-source ML observability course for data scientists and ML engineers. Learn how to monitor and debug your ML models in production.
MLOps for online machine learning using Docker and Python
Sample notebooks that use the Openlayer Python API
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