With the ever-growing trend of the development of artificial intelligence and machine learning-based applications, enterprises are looking for ways to scale and commercialize their machine learning development efforts. Kubernetes is a great production-grade container orchestration system to deploy machine learning models for production, and it does meet the scalability requirements. But for the development of production-grade machine learning applications, organizations require something else as well. To accelerate the model development, they need a repeatable process to take care of several pre-development and post-development activities. Several open-source tools offer flexible and automated integration of machine learning workflows along with the entire cluster’s lifecycle. Here are some tools to help you automate machine learning model development and deployment on the Kubernetes platform.
MLflow is an open-source platform and one of the major players in the data analytics industry. It is a single Python package that manages the machine learning lifecycle in a simple and reproducible manner.
MLflow allows users to develop projects in the local directory and track the project runs in a remote archive through a simplified logging process. It packages data science code in a reusable and reproducible way. This helps enhance governance and the core proposition of model management. MLflow offers an enhanced ability for data scientists to collaborate and try other’s projects in a way that focuses on experimentation with a model. The process is perfect for exploratory data analysis (EDA).
MLflow currently offers four components: MLflow Tracking, MLflow Projects, MLflow Models, and Model Registry. MLflow Tracking provides APIs and UI for logging the results to the local system and then comparing the results of multiple runs. The MLflow Projects packages data science code in a format to reproduce runs on any platform. The MLflow Models component deploys machine learning models in diverse serving environments. The Model Registry component stores, annotates, and manages models in a central repository, enabling better management of the full lifecycle of the MLflow Models.
MLflow can be integrated with TensorFlow, PyTorch, Keras, Rapids, and others. It is a lightweight tool that can help enhance the tracking and archiving capabilities of machine learning projects. Its flexibility makes it useful for a large number of scenarios for individual as well as team projects.
Kubeflow is a free and open-source machine learning toolkit for Kubernetes. Kubeflow is used to deploy machine learning pipelines to orchestrate complicated workflows across various environments on Kubernetes. It is backed by Google. The Kubeflow project provides a way to deploy open-source systems for machine learning to diverse infrastructures such as on-prem, GCP, AWS, and Azure.
Kubeflow is an orchestration tool, which is a combination of open-source libraries. These libraries depend on a Kubernetes cluster to provide a computing environment for machine learning model development and production tools.
Kubeflow offers a central dashboard that provides access to all Kubeflow components across the cluster. Kubeflow offers services for spawning and managing Jupyter notebooks allowing multiple users to contribute to a project simultaneously. Standard notebook images can be used across organizations, which can be used to set up role-based access controls and credential management across individuals and teams. KFServings can be used to enable serverless inferencing on Kubernetes clusters, while TensorFlow Training (TFJob) can be used to run TensorFlow jobs on Kubernetes. The data pipeline’s prior steps are completed by BigQuery, Dataproc, or containerized scripts.
Kubeflow 1.3 was released on April 23 via an official Kubeflow blog post. It is available through the public GitHub repository.
Pachyderm is an open-source data science platform designed for large-scale collaboration.
Pachyderm has been packed with advanced features, unlimited scalability, and robust security controls. It acts as the data layer that powers the machine learning lifecycle and automates and unifies the MLOps toolchain. It offers automatic data versioning and data-driven pipelines. It is used to process the unstructured and structured data sets quickly. It implements automatic parallel and incremental processing that does not require any code changes. Pachyderm enables the reproduction of machine learning project runs, allowing faster turnaround while meeting audit and data governance requirements.
Pachyderm offers Hub Edition, Pachyderm Community Edition, and Enterprise Edition. The Hub Edition is a freeware version used for building end-to-end pipelines and enhanced capabilities like data versioning. The Community or Local Edition can quickly and easily build, train, and deploy data science workloads on various Kubernetes deployments. The Enterprise Edition is designed for large-scale collaboration in highly secure environments. It also offers additional features such as a rich DAG UI, advanced metrics, auth integration, and more. It also offers advanced features like unlimited scalability and robust security control for organizations looking for industry-leading performance, flexibility, and security.
Pachyderm offers integrations with companies such as Adarga, Hover, Woven Planet, LivePerson, and LogMeIn. Pachyderm is convenient for data science teams with an engineering background cooperating on complex data pipelines across projects.
DataRobot is a leading augmented intelligence platform. It is available on-premises or on any cloud platform of the user’s choice. It can also be consumed as a fully managed AI service.
Its Enterprise AI platform allows customers to prepare their data, create and validate machine learning models — including time series models. It also allows users to deploy and monitor those models in a single solution. It supports a wide variety of data, including tabular data, free-form text, images, and geospatial data. It delivers AI technology and ROI enablement services to global enterprises with its no-code AI App Builder.
It deploys and updates remote models with MLOps management agents. It also communicates the most important details of the project to stakeholders with new AI reports. All the key stakeholders can collaborate in extracting business value from data with DataRobot’s enterprise AI platform and automated decision intelligence.
DataRobot offers access to time series Eureqa models. Its AI Heroes helps companies become more innovative, collaborate more effectively with their partners, and improve any business function.
Companies such as Kroger, Humana, USBank, PNC, and Lenovo leverage DataRobot’s leading enterprise AI platform. It serves data scientists, business analysts, the IT team responsible for governance and compliance. It also helps business executives and analytics leaders who derive business impact from the deployed models.
Volcano is a batch scheduling system built on Kubernetes. It provides a suite of mechanisms for compute-intensive workloads which are natively not available with Kubernetes. These workloads include machine learning, deep learning, bioinformatics, genomics, and other Big Data applications.
Volcano builds upon a wide variety of high-performance workloads at scale using several systems and platforms, combined with best-of-breed ideas and practices from the open-source community.
Volcano ecosystems include spark-operator, kubeflow/tf-operator, kubeflow/arena, Horovod/MPI, PaddlePaddle, and Cromwell. These applications typically run on generalized domain frameworks such as TensorFlow, Spark, PyTorch, Kubeflow, and MPI. Volcano is integrated with these frameworks to allow remarkable batch scheduling.
A sandbox project of the Cloud Native Computing Foundation (CNCF), Volcano has been widely used around the world in a variety of industries such as the Internet, cloud, finance, manufacturing, and medical. More than 20 companies and institutions are end-users and active contributors. Among its supporter companies are Huawei, Tencent, Biss, Vivo, and SHAREfir.
Yes, machine learning and Kubernetes can work together
A full-scale machine learning lifecycle can be handled and managed using deployment tools and frameworks listed in this article. These tools can help you create robust machine learning models and deploy them quickly and with ease. Before choosing the final tool, it is suggested to know the pros and cons of all tools and match them with your requirements. The right tool can give you the ability to do channel automation and add flexibility to business workflows.
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