TOP MLOPS BASED TOOLS FOR ENABLING EFFECTIVE MACHINE LEARNING LIFECYCLE

18Dec - by aiuniverse - 0 - In Data Science

Source: analyticsinsight.net

While organizations are increasingly leveraging ML tools, most of the projects fail during the test phase.

Machine learning (ML) has been touted as one of the key enablers of the Fourth Industrial Revolution. In recent times, businesses explore new approaches to maximize their profits and reach, without compromising on customer services. Machine learning helps them mine data from relevant sources and analyze it to understand trends, behavior and more. As IT enterprises integrate ML-driven insights into their organizational framework, MLOps is leveraged to enhance the operations and deployment during the lifecycle of machine learning model development and usage.

Machine learning, is a category of artificial intelligence that enables an AI model or system to learn from the datasets provided and retrain itself from the feedback and analysis carried over time. With the influx of big data innovations and advancements in AI and computing power, the capability of machine learning has grown tremendously over the years. Yet companies struggle to deploy its products or are clueless about where to apply them. This is where MLOps proves to be handy.

In general terms, MLOps is DevOps with enhanced capabilities of machine learning.  It helps data scientists and IT teams to manage the production machine learning lifecycle. This enables automation and monitoring at all steps of machine learning system construction, including integration, testing, releasing, deployment and infrastructure management. Further, it facilitates rapid innovation through robust machine learning lifecycle management. As per the ‘State of AI’ report by Ian Hogarth and Nathan Benaich, MLOps accounts for 25% of GitHub’s fastest-growing projects in 2020.

There are numerous MLOps frameworks for managing the life cycle of machine learning. Analytics Insight brings you a list of top 10 MLOps platform:

MLFlow:

It is an open-source platform for managing the end-to-end machine learning lifecycle including experimentation, reproducibility, deployment, and a central model registry. It provides three primary functionalities: tracking experiments, packaging ML code (projects), and managing and deploying models. This implies that a user can track an experiment, organize it, describe it for other ML engineers and pack it into a machine learning model.

Amazon SageMaker Studio:

This fully managed integrated environment, web-based tool by Amazon, allows data scientists to manage an entire machine learning lifecycle, i.e. from building and training to deploying machine learning models. One can also quickly upload data, create new notebooks, train and tune models, move back and forth between steps to adjust experiments, compare results, and deploy models to production all in one place, thus enabling more productivity.

Kubeflow:

It is a software with the main goal of run orchestration and making deployments of machine learning workflows, primarily on Kubernetes easier. It comprises of services to create and manage interactive Jupyter notebooks. It helps user to handle distributed TensorFlow training jobs. It also offers Multi-framework integration, e.g.  Istio and Ambassador for ingress.

Algorithmia:

It is an enterprise MLOps platform that allows users to deploy, manage, and scale their machine learning portfolio, on-demand (serverless) using CPUs and GPUs. It can either run on its own cloud, on user premises, on VMware, or on a public cloud. Besides, it also maintains models in its own Git repository or on GitHub, manages model versioning automatically, and can implement pipelining.

TensorFlow Extended (TFX):

It is a scalable end-to-end platform, based on TensorFlow, for production and deployment of machine learning pipelines. It provides a configuration framework and shared libraries to integrate common components needed to define, launch, and monitor ML systems. TFX is based on pipeline concept, which helps it define a data flow through several components, with the goal of implementing a specific ML task.

HPE Ezmeral ML Ops:

It brings, DevOps-like agility to the entire machine learning lifecycle at enterprise level using containers. It supports every stage of ML lifecycle—data preparation, model build, model training, model deployment, collaboration, and monitoring. This end-to-end data science solution also offers the flexibility to run on-premises, in multiple public clouds, or a hybrid model and responds to dynamic business requirements in a variety of use cases.

Seldon:

It is an open-source platform that allows data scientists and IT developers to develop core building blocks of machine learning. Seldon’s open-source machine learning deployment platform helps data science teams solve problems faster and more effectively. It’s designed to streamline the data science workflow, with audit trails, advanced experiments, continuous integration and deployment, rolling updates, scaling, model explanations, and more.

Paperspace:

It is a high-performance cloud computing and ML development platform for building, training and deploying machine learning models. Its Gradient feature is a suite of tools for exploring data, training neural networks, and building production-grade machine learning pipelines; while Paperspace Core can manage virtual machines with CPUs and optionally GPUs, running in Paperspace’s own cloud or on AWS.

Azure Machine Learning:

It is a cloud-based environment that companies can use to train, deploy, automate, manage, and track machine learning models. It has its’ own open-source MLOps environment. Also Azure Machine learning, can interoperate with popular open source tools, such as PyTorch, TensorFlow, Scikit-learn, Git, and the MLflow platform to manage the machine learning lifecycle.

Data Version Control (DVC):

It is an open-source tool for data science and machine learning. Its key features include a simple command line similar to Git and it does not require any database or proprietary online services. It enables data scientists to share the machine learning models and make them reproducible. The DVC user interface can cope with versioning and organization of big amounts of data and store them in a well-organized, accessible way.

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