Category: docker
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Building a Video Game Recommender System with FastAPI, PostgreSQL, and Render: Part 2
Building a Video Game Recommender System with FastAPI, PostgreSQL, and Render: Part 2 Deploying a FastAPI + PostgreSQL recommender system as a web application on Render The post Building a Video Game Recommender System with FastAPI, PostgreSQL, and Render: Part 2 appeared first on Towards Data Science. Lucas See Go to original source
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How to Import Pre-Annotated Data into Label Studio and Run the Full Stack with Docker
How to Import Pre-Annotated Data into Label Studio and Run the Full Stack with Docker From VOC to JSON: Importing pre-annotations made simple The post How to Import Pre-Annotated Data into Label Studio and Run the Full Stack with Docker appeared first on Towards Data Science. Yagmur Gulec Go to original source
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A Data Scientist’s Guide to Docker Containers
A Data Scientist’s Guide to Docker Containers For a ML model to be useful it needs to run somewhere. This somewhere is most likely not your local machine. A not-so-good model that runs in a production environment is better than a perfect model that never leaves your local machine. However, the production machine is usually…
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Why Data Scientists Should Care about Containers — and Stand Out with This Knowledge
Why Data Scientists Should Care about Containers — and Stand Out with This Knowledge “I train models, analyze data and create dashboards — why should I care about Containers?” Many people who are new to the world of data science ask themselves this question. But imagine you have trained a model that runs perfectly on…
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Learnings from a Machine Learning Engineer — Part 5: The Training
Learnings from a Machine Learning Engineer — Part 5: The Training In this fifth part of my series, I will outline the steps for creating a Docker container for training your image classification model, evaluating performance, and preparing for deployment. AI/ML engineers would prefer to focus on model training and data engineering, but the reality…
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Journey to Full-Stack Data Scientist: Model Deployment
Journey to Full-Stack Data Scientist: Model Deployment An introduction to productionizing machine learning models using APIs and Docker. Growing Responsibilities of Data Scientists The title of data scientist is ever-changing and often vague. It usually involves one who is fluent in mathematics, programming, and machine learning. They spend time cleaning data, building models, fine-tuning, and conducting…
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The Fallacy of Complacent Distroless Containers
The Fallacy of Complacent Distroless Containers Making containers smaller is the most popular practice when reducing your attack surface. But how real is this sense of security? Continue reading on Towards Data Science » Cristovao Cordeiro Go to original source