Productionizing Machine Learning with Observability, Quality and Flexibility at Scale

Hear how leading AI teams:

• Bridge the gap between development and production:

  Understand how to scale ML workloads from your laptop to the cloud with no code changes.

  With a single script prepare data, tune, train and scale your workloads.

• Scale across multiple dimensions:

  Hear how organizations are benefitting from embarrassingly parallel experiments and

  scaling across multiple cores, nodes, and data sources.

• Increase developer velocity and speed experimentation:

  See how to speed model development and iterations without scaling complexity.

  Visualize, optimize, collaborate and standardize models

  and data pipelines.

• Understand model drift:

  Track distribution changes in upstream data, predictions and actuals to

  proactively gauge

  model performance and find retraining opportunities.

• Automate monitoring at scale:

  Catch performance degradation of key metrics and

  surface unknown issues

  with performance, drift, and data quality monitors.

• Find and fix problems faster:

  Reduce time-to-resolution

  for even the most complex models with purpose-built workflows for root cause analysis.