How Google Cloud is Integrating AI and ML into its Services
Open Source For You|September 2022
This article provides an overview of the native AIML services offered by Google Cloud.
Dr Anand Nayyar and Dr Magesh Kasthuri
How Google Cloud is Integrating AI and ML into its Services

Development, training, tuning, and deployment of machine learning (ML) models are all time-consuming tasks. These can be simplified with an integrated ML based deployment pipeline activity termed as MLOps. A Google Anthos based template can help to create Juniper notebooks for building and deploying ML models using a predefined data model and a four-stage approach, as follows.

Prepare: To enable data modelling, load/ingest data for model preparation, query data sets, and create model storage in a cloud storage service.

Build: Create deployment notebooks using predefined Juniper notebooks, which contain PySpark, conda framework or TensorFlow to quickly create a development workspace for building ML models. There are predefined containers or VM images available, which can be leveraged for quickly building an ML environment.

Validate: Use the What-if Tool (WIT) for testing the performance of ML models and Vizier black-box optimisation framework to optimise complex ML models.

Deploy: Create predictive ML models from the build templates and configuration to enable real-time deployment of ML models for production-ready activities and end user usage.

Figure 1 highlights the different stages of Google’s AI platform.

Using traditional CI/CD pipelines needs a lot of scripting and coding to create templates for ML model development, preparation, build and deployment. MLOps helps to reduce this effort with a unified architecture for ML models using a no-code platform, robust governance and managed services for faster ‘time to market’ by leveraging the full power of Google’s AI platform.

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