Given the ever-changing needs of ML projects, it is considered safe to go with open-sourced MLOps tools
ML models are easy to design when the only factor for consideration is the ability to predict the outcome. Continual learning, considered as the fundamental step towards artificial intelligence is achieved by redesigning the ML models used for training. With zillions and zillions of bytes of datainvolved and tasks distributed across number of teams it becomes a wild goose chase when it is time for debugging or adapting to changed parameters. To incorporate scalability, flexibility and retractability in an ML model, developers usually go for MLOps frameworks. Given the ever-changing needs of ML projects, it is considered safe to go with open-sourced MLOps tools. Here is the list of top 10 open-source MLOps tools you can use for your upcoming ML projects.
Meta flow:
A Python compatible and R supportive MLOps tool, it is widely considered suitable for machine learning projects handled by large number of teams. Initially developed by Netflix to help its data science projects, now it has developed the capacity to provide AWS integrated ML services. It comes with some amazing features like handling external dependencies, managing compute resources, replay and resume workflow runs, perform containerized runs, etc.
MLflow:
This versatile tool comes in four components, viz. MLflow tracking, MLflow projects, MLflow models, model registry, and providing comprehensive solutions for ML model building challenges. It is designed to work with different libraries, clouds and ML frameworks like Spark, TensorFlow, and SciKit-Learn, with an ability to scale to Big-Data with Apache-Spark framework.
Data Version Control:
An open-source code-based tool for version control in datasets, machine learning models, uses Amazon S3, Microsoft Azure Blob Storage, Aliyun OSS, HDFS, HTTP frameworks. It facilitates model development teams to collaborate and develop shareable and reproducible ML projects.
Kubeflow:
This open-source MLOps tool comes with smoother orchestration and deployment of Machine Learning workflow abilities. Its unique features help integrate different phases of MLOps such as training, pipeline creation, and managing Jupyter notebooks integrate.
Pachyderm:
An open-source MLtool written in Golang and built on Docker and Kubernetes, helps run and deploy Machine Learning projects to any cloud platform. This is one tool which makes sure, every bit of data that is fed into the model is versioned and retractable.
Kedro:
A modular, reproducible and maintainable data science code, is primarily used for creating reproducible and maintainable Data Science code. It combines software engineering practices with machine-learning code to perform versioning, modularity, and separation in machine learning projects. The additional functionalities include pipeline visualization, project templating, and flexible deployment of data science projects.
MLRun:
MLRun an open source MLOps framework that helps you manage your Machine Learning pipeline from the development phase all through the deployment into production. MLRun introduces tracking, automation, rapid deployment, management, and easy scaling of models into your Machine Learning pipeline.
Seldon Core:
Equipped with advanced metrics, logging, testing, scaling and conversion abilities, it is one of the best suited MLOps tools developed for streamlining Machine Learning workflows. It is easy to containerize ML models, test usability and security of models, with Seldon Core. In addition, it serves models built in any open-source or commercial building framework.
Flyte:
An open source MLOps tool designed to support complex ML workflows written in Python, Java, and scala, makes for a great support in tracking, maintaining and automating Kubernetes-native ML workflows. It is basically used for ensuring retractability of code, versioning, and containerising the model.
ZenML:
It is an extensible and open-source MLOps framework used to create production-ready ML pipelines. It is compatible with almost all the tools and cloud environments which have interfaces catering to ML workflows. ZenML works through ML-specific workflows by sourcing data, splitting, preprocessing, training, and evaluation to provide a standard abstraction to the ML workflow.
Source: analyticsinsight.net
