Top 23 Python Mlops Projects
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label-studio
Label Studio is a multi-type data labeling and annotation tool with standardized output format
Project mention: [D] Portals for outsourcing preliminary data labeling | reddit.com/r/MachineLearning | 2022-01-13Not exactly for this solution, but I have really liked this tool. https://labelstud.io/ It is open source and can be self hosted if needed
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Project mention: [P] Deepchecks: an open-source tool for high standards validations for ML models and data. | reddit.com/r/MachineLearning | 2022-01-06
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SonarQube
Static code analysis for 29 languages.. Your projects are multi-language. So is SonarQube analysis. Find Bugs, Vulnerabilities, Security Hotspots, and Code Smells so you can release quality code every time. Get started analyzing your projects today for free.
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Project mention: Best job scheduler in 2022? (Airflow / Dagster / Prefect / Luigi / other) | reddit.com/r/dataengineering | 2022-01-18
Can I give a plug for Metaflow. It's particularly well suited to data science and ML workflows, with great tooling that's basically just annotations on python functions that gives you: - DAG orchestration - parallelism - cloud integration - data flow through DAGs — very very useful imo for data science teams trying to migrate their existing scripts to (and write new ones on) Metaflow
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Project mention: [Discussion] Applied machine learning implementation debate. Is OOP approach towards data preprocessing in python an overkill? | reddit.com/r/MachineLearning | 2021-11-03
I'd focus more on understanding the issues in depth, before jumping to a solution. Otherwise, you would be adding hassle with some - bluntly speaking - opinionated and inflexible boilerplate code which not many people will like using. You mention some issues: non-obvious to understand code and hard to execute and replicate. Bad code which is not following engineering best practices (ideas from SOLID etc.) does not get better if you force the author to introduce certain classes. You can suggest some basics (e.g. common code formatter, meaningful variables names, short functions, no hard-coded values, ...), but I'm afraid you cannot educate non-engineers in a single day workshop. I would not focus on that at first. However, there is no excuse for writing bad code and then expecting others to fix. As you say, data engineering is part of data science skills, you are "junior" if you cannot write reproducible code. Being hard to execute and replicate is theoretically easy to fix. Force everyone to (at least hypothetically) submit their code into a testing environment where it will be automatically executed on a fresh machine. This will mean that at first they have to exactly specify all libraries that need to be installed. Second, they need to externalize all configuration - in particular data input and data output paths. Not a single value should be hard-coded in code! And finally they need a *single* command which can be run to execute the whole(!) pipeline. If they fail on any of these parts... they should try again. Work that does not pass this test is considered unfinished by the author. Basically you are introducing an automated, infallible test. Regarding your code, I'd really not try that direction. In particular even these few lines already look unclear and over-engineered. The csv format is already hard-coded into the code. If it changes to parquet you'd have to touch the code. The processing object has data paths fixed for which is no reason in a job which should take care of pure processing. Export data is also not something that a processing job should handle. And what if you have multiple input and output data? You would not have all these issues if you had kept to most simple solution to have a function `process(data1, data2, ...) -> result_data` where dataframes are passed in and out. It would also mean to have zero additional libraries or boilerplate. I highly doubt that a function `main_pipe(...)` will fix the malpractices some people may do. There are two small feature which are useful beyond a plain function though: automatically generating a visual DAG from the code and quick checking if input requirements are satisfied before heavy code is run. You can still put any mature DAG library on top, which probably already includes experience from a lot of developers. Not need to rewrite that. I'm not sure which one is best (metaflow, luigi, airflow, ... https://github.com/pditommaso/awesome-pipeline no idea), but many come with a lot of features. If you want a bit more scaffolding to easier understand foreign projects, you could look at https://github.com/quantumblacklabs/kedro but maybe that's already too much. Fix the "single command replication-from-scratch requirement" first.
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Activeloop Hub
Dataset format for AI. Build, manage, & visualize datasets for deep learning. Stream data real-time to PyTorch/TensorFlow & version-control it. https://activeloop.ai (by activeloopai)
Project mention: The hand-picked selection of the best Python libraries released in 2021 | reddit.com/r/Python | 2021-12-21Hub.
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Using a general-purpose framework such as FastAPI involves writing a lot of boilerplate code just to get your API endpoint up and running. If deploying a model for a demo is the only thing you are interested in and you do not mind losing some flexibility, you might want to use a specialized serving framework instead. One example is BentoML, which will allow you to get an optimized serving endpoint for your model up and running much faster and with less overhead than a generic web framework. Framework-specific serving solutions such as Tensorflow Serving and TorchServe typically offer optimized performance but can only be used to serve models trained using Tensorflow or PyTorch, respectively.
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clearml
ClearML - Auto-Magical CI/CD to streamline your ML workflow. Experiment Manager, MLOps and Data-Management
Project mention: [D] Drop your best open source Deep learning related Project | reddit.com/r/MachineLearning | 2021-12-30Hi there. ClearML is our open-source solution which is part of the PyTorch ecosystem. We would really appreciate it if you read our README and starred us if you like what you see!
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Scout APM
Less time debugging, more time building. Scout APM allows you to find and fix performance issues with no hassle. Now with error monitoring and external services monitoring, Scout is a developer's best friend when it comes to application development.
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evidently
Interactive reports to analyze machine learning models during validation or production monitoring.
Project mention: The hand-picked selection of the best Python libraries released in 2021 | reddit.com/r/Python | 2021-12-21Evidently.
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flyte
Kubernetes-native workflow automation platform for complex, mission-critical data and ML processes at scale. It has been battle-tested at Lyft, Spotify, Freenome, and others and is truly open-source.
Project mention: Hacktoberfest: Flytesnacks Project "update tuple output examples" | dev.to | 2021-11-01I chose the flytekit project, which is one of the component repos of flyte and is the python SDK and tools of the Flyte project
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Project mention: ZenML helps data scientists work across the full stack | news.ycombinator.com | 2022-01-05
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Project mention: Show HN: Deploy ML Models on a Budget | reddit.com/r/patient_hackernews | 2021-02-01
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Recently I've been learning MLOps. There's a lot to learn, as shown by this and this repository listing MLOps references and tools, respectively.
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ploomber
Write maintainable, production-ready pipelines using Jupyter or your favorite text editor. Develop locally, deploy to the cloud. ☁️
Project mention: Simple workflow orchestration tool with Jupyter Notebook support | reddit.com/r/dataengineering | 2022-01-14Ploomber builds on top of papermill to provide a more streamlined experience, you can also open .py files as notebooks to have a nice git diff. https://github.com/ploomber/ploomber
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deepchecks
Test Suites for Validating ML Models & Data. Deepchecks is a Python package for comprehensively validating your machine learning models and data with minimal effort.
Project mention: Test suites for machine learning models in Python (New OSS package) | dev.to | 2022-01-18And if you liked the project, we'll be delighted to count you as one of our stargazers at https://github.com/deepchecks/deepchecks/stargazers!
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Project mention: Rubrix: Open-source tool for building NLP training sets (now with weak supervision) | reddit.com/r/LanguageTechnology | 2022-01-19
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ck
Collective Knowledge framework (CK) provides a common set of automation recipes, APIs and meta descriptions to enable collaborative, reproducible and unified benchmarking and optimization of ML Systems across continuously changing models, data sets, software and hardware: (by mlcommons)
Project mention: Research software code is likely to remain a tangled mess | news.ycombinator.com | 2021-02-22– Their solution product https://cknowledge.io/ and source code https://github.com/ctuning/ck\
I guess it should be helpful to the researchers community.
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Project mention: Deployment automation for ML projects of all shapes and sizes | news.ycombinator.com | 2021-06-09
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Project mention: Monitor the stability of a pandas or spark dataframe | news.ycombinator.com | 2021-09-15
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chitra
A multi-functional library for full-stack Deep Learning. Simplifies Model Building, API development, and Model Deployment.
Another way of doing this is to use CHITRA
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fastapi-template
Completely Scalable FastAPI based template for Machine Learning, Deep Learning and any other software project which wants to use Fast API as an API framework.
Project mention: Clean and Scalable Code Architecture for ML/DL and NLP driven micro-service | reddit.com/r/FastAPI | 2021-10-04Working on this opensource item to create a Clean/Scalable API for ML/DL-based projects using FastAPI. Have a look and please do give feedback: https://github.com/99sbr/fastapi-template
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Project mention: [P] Model Performance Monitoring in Production | reddit.com/r/MachineLearning | 2021-11-01
And the logger repo is https://github.com/graphsignal/graphsignal.
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Project mention: Anyone use Pyspark notebook in production ? | reddit.com/r/dataengineering | 2021-12-19
Python Mlops related posts
- Rubrix: Open-source tool for building NLP training sets (now with weak supervision)
- Test suites for machine learning models in Python (New OSS package)
- How to trust your machine learning model with Deepchecks
- [P] Open-source tool for building NLP training sets with weak supervision and search queries
- Simple workflow orchestration tool with Jupyter Notebook support
- Deepchecks: Open-source tool for high standard validation of ML Models and Data
- [P] Deepchecks: an open-source tool for high standards validations for ML models and data.
Index
What are some of the best open-source Mlops projects in Python? This list will help you:
| Project | Stars | |
|---|---|---|
| 1 | label-studio | 7,435 |
| 2 | great_expectations | 5,921 |
| 3 | metaflow | 5,178 |
| 4 | Kedro | 4,829 |
| 5 | Activeloop Hub | 4,200 |
| 6 | BentoML | 3,132 |
| 7 | clearml | 2,932 |
| 8 | evidently | 2,072 |
| 9 | flyte | 1,853 |
| 10 | zenml | 1,593 |
| 11 | budgetml | 1,252 |
| 12 | awesome-mlops | 999 |
| 13 | ploomber | 925 |
| 14 | deepchecks | 901 |
| 15 | rubrix | 723 |
| 16 | ck | 452 |
| 17 | bodywork | 316 |
| 18 | popmon | 221 |
| 19 | chitra | 170 |
| 20 | fastapi-template | 111 |
| 21 | graphsignal | 102 |
| 22 | ml-template-azure | 80 |
| 23 | dbx | 75 |
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