machine-learning in Python
Scikit-learn
BigML
Google Cloud TPU
Amazon Forecast
python-recsys
AWS Personalize
Qubole
neptune.ai
Algorithmia
Comet.ml
Weights & Biases
Managed MLflow
Spell
MCenter
5Analytics
Track and version your notebooks Log all your notebooks directly from Jupyter or Jupyter Lab. All you need is to install a Jupyter extension.
Manage your experimentation process Neptune tracks your work with virtually no interference to the way you like to do it. Decide what is relevant to your project and start tracking: - Metrics - Hyperparameters - Data versions - Model files - Images - Source code
Integrate with your workflow easily Neptune is a lightweight extension to your current workflow. Works with all common technologies in data science domain and integrates with other tools. It will take you 5 minutes to get started.
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Only negative is I didn't see it integrated with Azure, does with Google, AWS and one more. Looks real nice, and pretty powerful and plenty useful features for a data science group
Based on our record, neptune.ai should be more popular than machine-learning in Python. It has been mentiond 23 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.
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
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
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
Have you seen this? https://machinelearningmastery.com/machine-learning-in-python-step-by-step/. Source: over 2 years ago
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
Experiment tracking tools like MLflow, Weights and Biases, and Neptune.ai provide a pipeline that automatically tracks meta-data and artifacts generated from each experiment you run. Although they have varying features and functionalities, experiment tracking tools provide a systematic structure that handles the iterative model development approach. - Source: dev.to / 14 days ago
Neptune.ai - Log, store, display, organize, compare, and query all your MLOps metadata. Free for individuals: 1 member, 100 GB of metadata storage, 200h of monitoring/month. - Source: dev.to / 4 months ago
Hi I am Jakub. I run marketing at a dev tool startup https://neptune.ai/ and I share learnings on dev tool marketing on my blog https://www.developermarkepear.com/. Whenever I'd start a new marketing project I found myself going over a list of 20+ companies I knew could have done something well to “copy-paste” their approach as a baseline (think Tailscale, DigitalOCean, Vercel, Algolia, CircleCi, Supabase,... - Source: Hacker News / 9 months ago
There are a lot of tools out there for experiment tracking (eg neptune.ai), but I'm really not sure whether that sort of thing is over the top for what I need to do. Source: 10 months ago
Welcome to another episode of The Developer-led Podcast, where we dive into the strategies modern companies use to build and grow their developer tools. In this exciting episode, we're joined by Jakub Czakon, the CMO at Neptune.ai, a startup that assists developers in efficiently managing their machine-learning model data. Jakub is renowned not only for his role at Neptune.ai but also for his developer marketing... - Source: dev.to / 10 months ago
Scikit-learn - scikit-learn (formerly scikits.learn) is an open source machine learning library for the Python programming language.
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.
BigML - BigML's goal is to create a machine learning service extremely easy to use and seamless to integrate.
Comet.ml - Comet lets you track code, experiments, and results on ML projects. It’s fast, simple, and free for open source projects.
Google Cloud TPU - Custom-built for machine learning workloads, Cloud TPUs accelerate training and inference at scale.
Weights & Biases - Developer tools for deep learning research