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
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:
- Kubeflow includes services to create and manage interactive Jupyter notebooks.
- Kubeflow provides a custom TensorFlow training job operator that you can use to train your ML model.
- Kubeflow supports a TensorFlow Serving container to export trained TensorFlow models to Kubernetes.
- Kubeflow Pipelines is a comprehensive solution for deploying and managing end-to-end ML workflows.
MLFlow
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:
Record and query experiments: code, data, config, and results.
Deploy machine learning models in diverse serving environments.
Store, annotate, discover, and manage models in a central repository.
Pandas Profiling
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:
Unicode Text and Numerical analysis
Customized plots which can be taken from a massive array of tables
Variable Summary reports
Grafana
Grafana is an Open-Sourced multi-platform analytics and visualization web application equipped with modern visualization techniques for efficient model monitoring. Some of the features provided by Grafana are:
Any existing data from cloud, excel sheets, and more, can be directly imported and compiled in a dashboard.
Data can be accessed by everyone in the organization quickly.
Flyte
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:
The package introduces user-friendly SDKs that are highly intuitive.
Automated Caching comes in handy in caching repeated tasks and actions across multiple executions of the pipeline.
Kedro
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
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:
- It can be used as a direct drop-in replacement for NumPy and SciPy.
DVC
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:
A simple command-line interface that makes machine learning projects shareable and reproducible.
It can be linked to many cloud services and local storage devices to perform version control.
Introduces super-lightweight pipelines that reduce friction while deploying projects.
Metaflow
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:
Tries to give a massive array of idiomatic python code architectures towards the programmer and not the machine.
Automatically versions the written code in the form of notebooks for future references.
Pachyderm
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:
Being written in GoLang the package generally works faster at versioning and retracing any iteration of the ML Model.
Can easily handle the largest unstructured and structured datasets.
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
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:- Load models from frameworks like PyTorch, Scikit-learn, etc.
Infer loaded models using Raw data (JPEG, audio files, text files) or pre-processed data (Tensors, PIL images, etc.)
It can connect to the NimbleBox Platform can enable you to:
Train models on high spec cloud machines and deploy them as an API with one line of code
Orchestrate your MLOps pipeline using NimbleBox Jobs.
To learn more about MLOps and general practices in the field, download The Ultimate Guide to MLOps for free!
Written By
Aryan Kargwal
Data Evangelist