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Top 23 Jupyter Notebook Mlops Projects
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However, let's focus on how to get the assignment done with.
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amazon-sagemaker-examples
Example ๐ Jupyter notebooks that demonstrate how to build, train, and deploy machine learning models using ๐ง Amazon SageMaker.
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evidently
Evidently is โโan open-source ML and LLM observability framework. Evaluate, test, and monitor any AI-powered system or data pipeline. From tabular data to Gen AI. 100+ metrics.
Project mention: Evidently: An open-source ML and LLM observability framework | news.ycombinator.com | 2024-08-20 -
superduper
Superduper: Integrate AI models and machine learning workflows with your database to implement custom AI applications, without moving your data. Including streaming inference, scalable model hosting, training and vector search.
Project mention: Build fully portable AI applications on top of Snowflake with SuperDuperDB | dev.to | 2024-06-26Customize how AI and databases work together. Scale your AI projects to handle more data and users. Move AI projects between different environments easily. Extend the system with new AI features and database functionality. Check it out: Blog: https://blog.superduperdb.com/version-02 Github: https://github.com/SuperDuperDB/superduperdb (leave us a star โญ๏ธ๐ฅณ)
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Project mention: Should I Use a Framework to Build an Agent? Code vs. LangGraph vs. Workflows | news.ycombinator.com | 2024-10-02
LangGraph and LlamaIndex Workflows are generating a lot of buzz right now, and we wanted to see how they measure up in practice versus just writing the code. To do that, we took a straightforward agent architectureโone we've built and deployed in code without a frameworkโand implemented it using LangGraph and Workflows. Our main goal was to explore how these frameworks translate a simple agent design into their abstractions and assess the impact on the development and debugging process.
We want to share our findings with the community, providing practical examples and honest observations about these frameworks where they introduce friction and where they shine. Thereโs a lot of hype out there, and we hope to offer some clarity with real code examples and unbiased perspectives.
For context, weโve been running our own Co-pilot agent/assistant in production for about eight months. Weโve also helped clients troubleshoot their assistants at scale, so weโve seen a wide range of use cases and challenges.
The architecture we tested is a single-tier LLM routerโa pattern we often see in various client implementations. It involves a single LLM router that uses function calling to route tasks or skills, which might include another LLM call before returning control to the router. Itโs a simple but versatile pattern.
Hereโs a Towards Data Science write up we did on the project: https://towardsdatascience.com/choosing-between-llm-agent-frameworks-69019493b259
Full code: https://github.com/Arize-ai/phoenix/tree/main/examples/agent_framework_comparison
Hot take #1: For experienced developers, framework abstractions can add unnecessary complexity.
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hands-on-llms
๐ฆ ๐๐ฒ๐ฎ๐ฟ๐ป about ๐๐๐ ๐, ๐๐๐ ๐ข๐ฝ๐, and ๐๐ฒ๐ฐ๐๐ผ๐ฟ ๐๐๐ for free by designing, training, and deploying a real-time financial advisor LLM system ~ ๐ด๐ฐ๐ถ๐ณ๐ค๐ฆ ๐ค๐ฐ๐ฅ๐ฆ + ๐ท๐ช๐ฅ๐ฆ๐ฐ & ๐ณ๐ฆ๐ข๐ฅ๐ช๐ฏ๐จ ๐ฎ๐ข๐ต๐ฆ๐ณ๐ช๐ข๐ญ๐ด
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Project mention: Ask HN: Daily practices for building AI/ML skills? | news.ycombinator.com | 2023-12-14
coming 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.
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whylogs
An open-source data logging library for machine learning models and data pipelines. ๐ Provides visibility into data quality & model performance over time. ๐ก๏ธ Supports privacy-preserving data collection, ensuring safety & robustness. ๐
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Featureform The success of a machine learning model relies on the quality of data and, hence, the features fed to the model. However, in large organizations, members of one team may not be aware of good features developed by other teams in the organization. A feature store helps eliminate this problem by providing a central repository of features that are accessible to all the teams and individuals within an organization.
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hamilton
Hamilton helps data scientists and engineers define testable, modular, self-documenting dataflows, that encode lineage/tracing and metadata. Runs and scales everywhere python does.
Yep. When I was designing https://github.com/dagworks-inc/hamilton part of the idea was to make it easy to understand what and where. That is, enable one to grep for function definitions and their downstream use easily, and where people can't screw this up. You'd be surprised how easy it is to make a code base where grep doesn't help you all that much (at least in the python data transform world) ...
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vertex-ai-samples
Notebooks, code samples, sample apps, and other resources that demonstrate how to use, develop and manage machine learning and generative AI workflows using Google Cloud Vertex AI.
Google Gemini API Key: Create an account at Google Cloud Console to get your Gemini API key for tweet generation.
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Project mention: Efficient Deep Learning Systems Course (Yandex/HSE) | news.ycombinator.com | 2024-01-19
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mlops-python-package
Kickstart your MLOps initiative with a flexible, robust, and productive Python package.
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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-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
๐ Examples of how to use Neptune for different use cases and with various MLOps tools (by neptune-ai)
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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).
Jupyter Notebook Mlops discussion
Jupyter Notebook Mlops related posts
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Should I Use a Framework to Build an Agent? Code vs. LangGraph vs. Workflows
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Wk 4: Deployment - MLOPs with DataTalks
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Wk 2 Experiment Tracking: MLOPs with DataTalks
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Wk 1: MLOPs with DataTalks
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Created a plastic object detection model using AWS technologies ๐๐ค
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Created a plastic object detection model using AWS technologies ๐๐ค
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Created a plastic object detection model using AWS technologies ๐๐ค
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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 | 37,139 |
| 2 | mlops-zoomcamp | 11,012 |
| 3 | amazon-sagemaker-examples | 10,018 |
| 4 | evidently | 5,196 |
| 5 | superduper | 4,665 |
| 6 | phoenix | 3,528 |
| 7 | hands-on-llms | 2,996 |
| 8 | mlops-course | 2,838 |
| 9 | whylogs | 2,635 |
| 10 | featureform | 1,801 |
| 11 | MLOps | 1,775 |
| 12 | hamilton | 1,752 |
| 13 | vertex-ai-samples | 1,651 |
| 14 | efficient-dl-systems | 648 |
| 15 | mlops-python-package | 634 |
| 16 | serverless-ml-course | 529 |
| 17 | fsdl-text-recognizer-2022-labs | 446 |
| 18 | mlops-with-vertex-ai | 348 |
| 19 | MLSys-NYU-2022 | 334 |
| 20 | fake-news | 133 |
| 21 | serving-pytorch-models | 101 |
| 22 | examples | 76 |
| 23 | whylogs-examples | 48 |
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