MLOps (Machine Learning Operations) is a new and emerging field that is meant to simplify the process of deploying machine learning models. MLOps platforms are designed to make it easier for developers to deploy their models, monitor them, and iterate on them. These platforms are also designed to make it easier for data scientists to create models in the first place. This article will introduce some of the leading machine learning platforms to check out.
Qwak simplifies the deployment and integration of machine learning at scale. Qwak’s ML Engineering Platform empowers data science and ML engineering teams to unblock the full realization of machine learning for the business. By abstracting the complexities of model deployment, integration, and optimization, Qwak brings agility and high velocity to all ML initiatives designed to transform business, innovate and create competitive advantage.
2. Amazon SageMaker
Amazon SageMaker helps data scientists and developers to prepare, build, train, and deploy high-quality machine learning models quickly by bringing together a broad set of capabilities purpose-built for machine learning. To make it easier to get started, Amazon SageMaker JumpStart provides a set of solutions for the most common use cases that can be deployed readily with just a few clicks.
3. Databricks Unified
The Databricks Unified Data Service provides a reliable and scalable platform for your data pipelines, data lakes, and data platforms. Manage your full data journey, so you can ingest, process, store, and expose data throughout your organization.
The Iguazio MLOps Platform transforms AI projects into real-world business outcomes. Accelerate and scale development, deployment, and management of your AI applications with end-to-end automation of machine (and deep) learning pipelines.
DataRobot enables organizations to leverage the transformational power of AI by delivering the world’s only AI Cloud platform combined with an AI-native strategic success team to help customers rapidly turn data into value.