![How Google Cloud is Integrating AI and ML into its Services](https://cdn.magzter.com/Linux For You/1661758754/articles/uZBrWZOqD1664197220573/HOW-GOOGLE-CLOUD-IS-INTEGRATING AI-AND-ML-INTO-ITS-SERVICES.jpg)
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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