Building a scalable MLOps system with Vertex AI AutoML and Pipeline
When you build a Machine Learning (ML) product, consider at least two MLOps scenarios. First, the model is replaceable, as breakthrough algorithms are introduced in academia or industry. Second, the model itself has to evolve with the data in the changing world. Google can handle both scenarios…
Running AlphaFold batch inference with Vertex AI Pipelines
Today, to accelerate research in the bio-pharma space, from the creation of treatments for diseases to the production of new synthetic biomaterials, Google are announcing a new Vertex AI solution that demonstrates how to use Vertex AI Pipelines to run DeepMind’s AlphaFold protein structure predictions at scale. Once a protein’s structure…
MLOps in BigQuery ML with Vertex AI Model Registry
Without a central place to manage models, those responsible for operationalizing ML models have no way of knowing the overall status of trained models and data. This lack of manageability can impact the review and release process of models into production, which often requires offline…
Using Vertex AI for rapid model prototyping and deployment
Bringing AI models to a production environment is one of the biggest challenges of AI practitioners. Much of the discussions in the AI/ML space revolve around model development. As shown in this diagram from the canonical Google paper “Hidden Technical Debt in Machine Learning Systems”,…
Extending Network Reachability of Vertex AI Pipelines
Vertex AI Pipelines and jobs use the Service Networking API to configure their networking. As such, they will run in a Google-owned VPC network in tenant project that is peered to the network in your project. This peering is subject to the following standard VPC Peering networking constraints;…
Orchestrating PyTorch ML Workflows on Vertex AI Pipelines
Previously in the PyTorch on Google Cloud series, Google trained, tuned and deployed a PyTorch text classification model using Training and Prediction services on Vertex AI. In this post, we will show how to automate and monitor a PyTorch based ML workflow by orchestrating the pipeline in a serverless manner…
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