Getting the Most From Your Google Vertex ML Architecture

  • Ability to train models specific to business needs with minimal ML expertise
  • Support for custom frameworks and standard frameworks (SK-learn, Pytorch, TF, XGBoost, R) for model development
  • Accelerated training with integrated APIs for image, video, and text processing
  • Integrated Jupyter notebooks
  • Easy feature engineering and management
  • Easy experiment tracking and tuning
  • Limited model observability in production so data scientists and ML engineers have difficulty tracking the ongoing performance and accuracy of live models
  • Runtime is compute-intensive, particularly for complex models
  • Can’t deploy ML into non-GCP clouds, on-prem or at the edge without significant re-engineering
  • Observability: An interface that allows your team to define metrics and analytics to measure, track, and improve your ML’s performance. Model observability insights provide visibility into how your model’s behavior might be affecting your business outcomes. Automated notifications and alerts for drifts and anomalies allow data science teams to supervise models at scale with no operational constraints.
  • Deploy Anywhere: Whether you’re planning to deploy in an environment that’s mostly connected to the cloud, at the edge or on-prem, Wallaroo can handle the work. Wallaroo is designed to seamlessly connect into your existing ecosystem with a standardized process for deploying your models across various platforms, clouds and environments. Not only that but the variety of connectors Wallaroo offers will seamlessly connect our platform with your production data so you’re up and running in minutes.
  • High Performance Inferencing: The success of your ML deployment is based on the performance of your ML models when generating inferences on production data. When measuring model performance, you should take into account the latency, throughput and computational efficiency of your inferences. For running ML models live in production, Wallaroo generates 10x more inferences per second than Vertex while requiring 90% less compute (see below).

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Wallaroo

Wallaroo

Wallaroo enables data scientists and ML engineers to deploy enterprise-level AI into production simpler, faster, and with incredible efficiency.