Overview¶
One of the main advantages of the AAW platform is its ability to integrate with popular machine learning platforms such as Databricks and AzureML.
The Advanced Analytics Workspace (AAW) is an open source data analytics platform that is designed to be highly integrable. This means that it can be easily integrated with other platforms and tools to extend its capabilities and streamline workflows.
An example diagram depicting a possible PaaS connection strategy:
Setup: If you need help integrating with a platform as a service offering, we're happy to help!
Integration with External Platform as a Service (PaaS) Offerings¶
Integration is key to success.
Our open source platform offers unparalleled optionality to our users. By allowing users to use open source tools, we empower them to use their preferred data science and machine learning frameworks. But the real power of our platform comes from its ability to integrate with many Platform as a Service (PaaS) offerings, like Databricks or AzureML. This means that our users can leverage the power of the cloud to run complex data processing and machine learning pipelines at scale. With the ability to integrate with PaaS offerings, our platform enables our users to take their work to the next level, by giving them the power to scale their workloads with ease, and take advantage of the latest innovations in the field of data science and machine learning. By providing this level of optionality, we ensure that our users can always choose the right tool for the job, and stay ahead of the curve in an ever-changing field.
We can integrate with many Platform as a Service (PaaS) offerings, like Databricks or AzureML.
Databricks¶
Databricks is a cloud-based platform that provides a unified analytics platform for big data processing and machine learning. With its powerful distributed computing engine and streamlined workflow tools, Databricks is a popular choice for building and deploying machine learning models. By integrating with Databricks, the AAW platform can leverage its distributed computing capabilities to train and deploy machine learning models at scale.
AzureML¶
AzureML is another popular machine learning platform that provides a wide range of tools for building, training, and deploying machine learning models. By integrating with AzureML, the AAW platform can leverage its powerful tools for building and training models, as well as its ability to deploy models to the cloud.
Examples¶
Examples of how to integrate the AAW platform with these and other platforms can be found on the MLOps Github repository.
This repository contains a range of examples and tutorials for using the AAW platform in various machine learning workflows, including data preparation, model training, and model deployment.
Conclusion¶
By integrating with popular machine learning platforms like Databricks and AzureML, the AAW platform provides a powerful and flexible solution for building, deploying, and managing machine learning workflows at scale.
By leveraging the integrations and tools provided by these platforms, data scientists and machine learning engineers can accelerate their workflows and achieve better results with less effort.