Machine Learning, by definition, is the process of devising an algorithm that will help the machine go from input to output. Seems simple right? Today, with over 137,000 different packages in Python Language, developers develop new packages every day to make ML development easier. But, how many packages for the wonderful intersection of ML and DevOps?
Let us embark on a journey to look at some of these very packages that help you bring out the best of the two fields. By the end of this blog, you will be able to leverage the beautiful gifts of open-source and developers to code robust data pipelines, tracking, development, deployment, and monitoring systems for your machine learning venture and scale it without spending a fortune.
You may have already heard about some of these from The Ultimate Guide to MLOps, but let us break down these packages further and see how they can aid in scaling up your startup!
Kubeflow is an open-sourced end-to-end MLOps tool that makes orchestration and deployment of Machine Learning workflows based on Kubernetes easier. Some of the features provided by Kubeflow are:
MLFlow is an open-sourced MLOps tool that caters to the entire machine learning pipeline by including automation and modularity in experimentation, reproducibility, deployment, and a central model registry. Some of the features provided by MLFlow are:
Pandas Profiling is an open-sourced Python module coded meticulously for efficient Data Exploration with just a few lines of code. Moreover, the package helps you generate interactive reports that can be easily interpretable to someone oblivious to Data Science in general.
Multiple versions of the package can be integrated into other frameworks like Streamlit. It can act as a rudimentary way to present data. Some of the features provided by Pandas Profiling are:
Flyte is yet another open-sourced workflow automation tool that helps at delivering complex and critical Machine Learning and Data related scaling. Actively used by industry giants like Lyft and Spotify, the package comes under the Apache License to further aid its resilience towards better functionality. Some of the features provided by Flyte are:
Kedro is an open-sourced Python package that can create data science code that is reproducible, maintainable, and modular. It borrows concepts from software engineering best-practice and applies them to machine-learning code; applied concepts include modularity, separation of concerns, and versioning. Some of the features provided by Kedro are:
Automatic resolution of dependencies between pure Python functions and data pipeline visualization using Kedro-Viz.
Data and model versioning for file-based systems.
CuPy is an Open-Sourced array library for GPU-accelerated computing with Python. CuPy utilizes CUDA Toolkit libraries including cuBLAS, cuRAND, cuSOLVER, cuSPARSE, cuFFT, cuDNN, and NCCL to make full use of the GPU architecture. Some of the features provided by CuPy are:
DVC is an open-sourced python tool that can perform version control in machine learning and data learning projects. Following a Git-like model, DVC provides management and versioning of datasets and machine learning models. Some of the features provided by DVC are:
Initially introduced by Netflix, Metaflow is an Open-Sourced python package that manages enterprise machine learning and data science projects. Compiling various Python-based ML, DL, and Data Science libraries, the package provides a common platform for smooth model development. Some of the features provided by Metaflow are:
Pachyderm is an open-sourced version control tool that works similarly to DVC. However, it wins over DVC by providing direct support to run and deploy ML projects to any cloud service. Some of the features offered by Pachyderm are:
Now that you have gone through the "Top 10 Python Packages for MLOps" according to us, we have a bonus package for you! Straight from the developers here at NimbleBox, we present nbox. Read more about it below!👇
nbox is an open-source SDK designed to make Machine Learning inference simple. It supports loading models from any other frameworks and runs inference tasks on the model in any format you want to. It also provides support for orchestrating these tasks using the NimbleBox Platform. nbox helps you:
It can connect to the NimbleBox Platform can enable you to:
To learn more about MLOps and general practices in the field, download The Ultimate Guide to MLOps for free!
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Want to learn more? Let’s discuss how NimbleBox can support your ML project.