Conversation with Dirk-Jan Kubeflow (vs cloud native solutions like SageMaker)
Dirk-Jan Verdoorn - Data Scientist at Dept Agency
Kubeflow. (From the website:) The Machine Learning Toolkit for Kubernetes. The Kubeflow project is dedicated to making deployments of machine learning (ML) workflows on Kubernetes simple, portable and scalable. Our goal is not to recreate other services, but to provide a straightforward way to deploy best-of-breed open-source systems for ML to diverse infrastructures. Anywhere you are running Kubernetes, you should be able to run Kubeflow.
TensorFlow Extended (TFX). If using TensorFlow with Kubeflow, combine with TFX for maximum power. (From the website:) TensorFlow Extended (TFX) is an end-to-end platform for deploying production ML pipelines. When you're ready to move your models from research to production, use TFX to create and manage a production pipeline.
Alternatives:
MLA 021 Databricks
MLA 019 DevOps
MLA 018 Descript
MLA 017 AWS Local Development
MLA 016 SageMaker 2
MLA 015 SageMaker 1
MLA 014 Machine Learning Server
MLA 013 Customer Facing Tech Stack
MLA 012 Docker
MLG 032 Cartesian Similarity Metrics
MLA 011 Practical Clustering
MLA 010 NLP packages: transformers, spaCy, Gensim, NLTK
MLA 009 Charting tools
MLA 008 Exploratory Data Analysis
MLA 007 Jupyter Notebooks
MLA 006 Salary
MLA 005 Shapes & Sizes
MLA 003 Storage: HDF, Pickle, Postgres
MLA 002 Numpy & Pandas
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