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Managed MLflow VS machine-learning in Python

Compare Managed MLflow VS machine-learning in Python and see what are their differences

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Managed MLflow logo Managed MLflow

Managed MLflow is built on top of MLflow, an open source platform developed by Databricks to help manage the complete Machine Learning lifecycle with enterprise reliability, security, and scale.

machine-learning in Python logo machine-learning in Python

Do you want to do machine learning using Python, but you’re having trouble getting started? In this post, you will complete your first machine learning project using Python.
  • Managed MLflow Landing page
    Landing page //
    2023-05-15
  • machine-learning in Python Landing page
    Landing page //
    2020-01-13

Category Popularity

0-100% (relative to Managed MLflow and machine-learning in Python)

Managed MLflow

machine-learning in Python

Data Science And Machine Learning
67 67%
Data Science And Machine Learning
33% 33
Data Science Notebooks
100 100%
Data Science Notebooks
0% 0
Data Dashboard
0 0%
Data Dashboard
100% 100
Machine Learning
100 100%
Machine Learning
0% 0

User comments

Share your experience with using Managed MLflow and machine-learning in Python. For example, how are they different and which one is better?
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Social recommendations and mentions

Based on our record, machine-learning in Python seems to be more popular. It has been mentiond 7 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.

Managed MLflow mentions (0)

We have not tracked any mentions of Managed MLflow yet. Tracking of Managed MLflow recommendations started around Mar 2021.

machine-learning in Python mentions (7)

  • Data science and cybersecurity with python project
    After that you should probably look at some very basic ML tutorials. I just googled it, I have no idea if this is good https://machinelearningmastery.com/machine-learning-in-python-step-by-step/. Source: over 1 year ago
  • Ask HN: How can I learn ML in 6 months as a teenager?
    Few different approaches based on search engine 'ml with python': Work though use cases / examples : https://www.databricks.com/resources/ebook/big-book-of-machine-learning-use-cases On-line class(es) / step by step projects: * https://bootcamp-sl.discover.online.purdue.edu/ai-machine-learning-certification-course * https://www.w3schools.com/python/python_ml_getting_started.asp *... - Source: Hacker News / over 1 year ago
  • Are these CS courses enough CS knowledge for ML engineer?
    MLE: ALL OF THE ABOVE (this is important - pure machine learning skills generally won’t make you hireable unless you’re doing a PhD and/or are a genius) Plus: 1. https://machinelearningmastery.com/machine-learning-in-python-step-by-step/ 2. https://www.coursera.org/learn/machine-learning 3. https://www.3blue1brown.com/topics/neural-networks. Source: about 2 years ago
  • how to do i train an AI
    Have you seen this? https://machinelearningmastery.com/machine-learning-in-python-step-by-step/. Source: over 2 years ago
  • Python Data Science Project Ideas (+References)
    Machine learning models Fine-tune existing machine learning models for improved accuracy, or create your own custom models. - Source: dev.to / over 2 years ago
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What are some alternatives?

When comparing Managed MLflow and machine-learning in Python, you can also consider the following products

Algorithmia - Algorithmia makes applications smarter, by building a community around algorithm development, where state of the art algorithms are always live and accessible to anyone.

Scikit-learn - scikit-learn (formerly scikits.learn) is an open source machine learning library for the Python programming language.

Weights & Biases - Developer tools for deep learning research

BigML - BigML's goal is to create a machine learning service extremely easy to use and seamless to integrate.

neptune.ai - Neptune brings organization and collaboration to data science projects. All the experiement-related objects are backed-up and organized ready to be analyzed and shared with others. Works with all common technologies and integrates with other tools.

Google Cloud TPU - Custom-built for machine learning workloads, Cloud TPUs accelerate training and inference at scale.

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