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Top 23 Jupyter Notebook Mlops Projects
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amazon-sagemaker-examples
Example 📓 Jupyter notebooks that demonstrate how to build, train, and deploy machine learning models using 🧠 Amazon SageMaker.
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WorkOS
The modern identity platform for B2B SaaS. The APIs are flexible and easy-to-use, supporting authentication, user identity, and complex enterprise features like SSO and SCIM provisioning.
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evidently
Evaluate and monitor ML models from validation to production. Join our Discord: https://discord.com/invite/xZjKRaNp8b
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whylogs
An open-source data logging library for machine learning models and data pipelines. 📚 Provides visibility into data quality & model performance over time. 🛡️ Supports privacy-preserving data collection, ensuring safety & robustness. 📈
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InfluxDB
Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.
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hands-on-llms
🦖 𝗟𝗲𝗮𝗿𝗻 about 𝗟𝗟𝗠𝘀, 𝗟𝗟𝗠𝗢𝗽𝘀, and 𝘃𝗲𝗰𝘁𝗼𝗿 𝗗𝗕𝘀 for free by designing, training, and deploying a real-time financial advisor LLM system ~ 𝘴𝘰𝘶𝘳𝘤𝘦 𝘤𝘰𝘥𝘦 + 𝘷𝘪𝘥𝘦𝘰 & 𝘳𝘦𝘢𝘥𝘪𝘯𝘨 𝘮𝘢𝘵𝘦𝘳𝘪𝘢𝘭𝘴
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vertex-ai-samples
Sample code and notebooks for Vertex AI, the end-to-end machine learning platform on Google Cloud
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hamilton
Hamilton helps data scientists and engineers define testable, modular, self-documenting dataflows, that encode lineage and metadata. Runs and scales everywhere python does.
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serverless-ml-course
Serverless Machine Learning Course for building AI-enabled Prediction Services from models and features
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fsdl-text-recognizer-2022-labs
Complete deep learning project developed in Full Stack Deep Learning, 2022 edition. Generated automatically from https://github.com/full-stack-deep-learning/fsdl-text-recognizer-2022
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mlops-python-package
Kickstart your MLOps initiative with a flexible, robust, and productive Python package.
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mlops-with-vertex-ai
An end-to-end example of MLOps on Google Cloud using TensorFlow, TFX, and Vertex AI
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MLSys-NYU-2022
Slides, scripts and materials for the Machine Learning in Finance Course at NYU Tandon, 2022
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examples
📝 Examples of how to use Neptune for different use cases and with various MLOps tools (by neptune-ai)
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SaaSHub
SaaSHub - Software Alternatives and Reviews. SaaSHub helps you find the best software and product alternatives
Project mention: [D] How do you keep up to date on Machine Learning? | /r/learnmachinelearning | 2023-08-13Made With ML
I need to use AWS Sagemaker (required, can't use easier services) and my adviser gave me this document to start with: https://github.com/aws/amazon-sagemaker-examples/blob/main/introduction_to_amazon_algorithms/jumpstart-foundation-models/question_answering_retrieval_augmented_generation/question_answering_langchain_jumpstart.ipynb
There is MLOps Zoomcamp course (which shows end-to-end MLOps process with open-source MLOps tools) https://github.com/DataTalksClub/mlops-zoomcamp.
Project mention: [P] Free open-source ML observability course: starts October 16 🚀 | /r/MachineLearning | 2023-10-15Hi everyone, I’m one of the creators of Evidently, an open-source (Apache 2.0) tool for production ML monitoring. We’ve just launched a free open course on ML observability that I wanted to share with the community.
Project mention: Ask HN: Daily practices for building AI/ML skills? | news.ycombinator.com | 2023-12-14coming from a similar context, i believe going top down might be the way to go.
up to your motivation, doing basic level courses first (as shared by others) and then tackling your own application of the concepts might be the way to go.
i also observe the need for strong IT skills for implementing end-to-end ml systems. so, you can play to your strenghts and also consider working on MLOps. (online self-paced course - https://github.com/GokuMohandas/mlops-course)
i went back to school to get structured learning. whether you find it directly useful or not, i found it more effective than just motivating myself to self-learn dry theory. down the line, if you want to go all-in, this might be a good option for you too.
11. Phoenix by Arize AI | Github | tutorial
There are 3 courses that I usually recommend to folks looking to get into MLE/MLOps that already have a technical background. The first is a higher-level look at the MLOps processes, common challenges and solutions, and other important project considerations. It's one of Andrew Ng's courses from Deep Learning AI but you can audit it for free if you don't need the certificate: - Machine Learning in Production For a more hands-on, in-depth tutorial, I'd recommend this course from NYU (free on GitHub), including slides, scripts, full-code homework: - Machine Learning Systems And the title basically says it all, but this is also a really good one: - Hands-on Train and Deploy ML Pau Labarta, who made that last course, actually has a series of good (free) hands-on courses on GitHub. If you're interested in getting started with LLMs (since every company in the world seems to be clamoring for them right now), this course just came out from Pau and Paul Iusztin: - Hands-on LLMs For LLMs I also like this DLAI course (that includes Prompt Engineering too): - Generative AI with LLMs It can also be helpful to start learning how to use MLOps tools and platforms. I'll suggest Comet because I work there and am most familiar with it (and also because it's a great tool). Cloud and DevOps skills are also helpful. Make sure you're comfortable with git. Make sure you're learning how to actually deploy your projects. Good luck! :)
Project mention: Gemini 1.5 outshines GPT-4-Turbo-128K on long code prompts, HVM author | news.ycombinator.com | 2024-02-18
Project mention: Using IPython Jupyter Magic commands to improve the notebook experience | dev.to | 2024-03-03In this post, we’ll show how your team can turn any utility function(s) into reusable IPython Jupyter magics for a better notebook experience. As an example, we’ll use Hamilton, my open source library, to motivate the creation of a magic that facilitates better development ergonomics for using it. You needn’t know what Hamilton is to understand this post.
Project mention: Efficient Deep Learning Systems Course (Yandex/HSE) | news.ycombinator.com | 2024-01-19
full-stack-deep-learning
I always use TDD when I work on serious AI/ML projects. Even if this practice is time-consuming in the short term, it's time efficient in the long run. I prefer to catch bugs as early as possible in my workflow. I recently worked on a MLOps Python package that provides examples to implement best practices like TDD, code coverage and more: https://github.com/fmind/mlops-python-package
There are 3 courses that I usually recommend to folks looking to get into MLE/MLOps that already have a technical background. The first is a higher-level look at the MLOps processes, common challenges and solutions, and other important project considerations. It's one of Andrew Ng's courses from Deep Learning AI but you can audit it for free if you don't need the certificate: - Machine Learning in Production For a more hands-on, in-depth tutorial, I'd recommend this course from NYU (free on GitHub), including slides, scripts, full-code homework: - Machine Learning Systems And the title basically says it all, but this is also a really good one: - Hands-on Train and Deploy ML Pau Labarta, who made that last course, actually has a series of good (free) hands-on courses on GitHub. If you're interested in getting started with LLMs (since every company in the world seems to be clamoring for them right now), this course just came out from Pau and Paul Iusztin: - Hands-on LLMs For LLMs I also like this DLAI course (that includes Prompt Engineering too): - Generative AI with LLMs It can also be helpful to start learning how to use MLOps tools and platforms. I'll suggest Comet because I work there and am most familiar with it (and also because it's a great tool). Cloud and DevOps skills are also helpful. Make sure you're comfortable with git. Make sure you're learning how to actually deploy your projects. Good luck! :)
NOTE:
The open source projects on this list are ordered by number of github stars.
The number of mentions indicates repo mentiontions in the last 12 Months or
since we started tracking (Dec 2020).
The latest post mention was on 2024-03-03.
Jupyter Notebook Mlops related posts
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- Created a plastic object detection model using AWS technologies 🌍🤖
- Created a plastic object detection model using AWS technologies 🌍🤖
- Where to start
- background in ML, how can I get into DS career as a mid 40's guy with a family?
- YouTube channel on AI, ML, NLP and Computer Vision
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A note from our sponsor - InfluxDB
www.influxdata.com | 19 Apr 2024
Index
What are some of the best open-source Mlops projects in Jupyter Notebook? This list will help you:
| Project | Stars | |
|---|---|---|
| 1 | Made-With-ML | 35,610 |
| 2 | amazon-sagemaker-examples | 9,491 |
| 3 | mlops-zoomcamp | 8,735 |
| 4 | evidently | 4,619 |
| 5 | mlops-course | 2,733 |
| 6 | phoenix | 2,541 |
| 7 | whylogs | 2,538 |
| 8 | hands-on-llms | 2,209 |
| 9 | MLOps | 1,698 |
| 10 | featureform | 1,674 |
| 11 | vertex-ai-samples | 1,332 |
| 12 | hamilton | 1,306 |
| 13 | efficient-dl-systems | 575 |
| 14 | serverless-ml-course | 476 |
| 15 | fsdl-text-recognizer-2022-labs | 418 |
| 16 | mlops-python-package | 337 |
| 17 | mlops-with-vertex-ai | 326 |
| 18 | MLSys-NYU-2022 | 238 |
| 19 | fake-news | 128 |
| 20 | serving-pytorch-models | 100 |
| 21 | examples | 65 |
| 22 | whylogs-examples | 47 |
| 23 | ml-pipeline-engineering | 36 |
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