CodeSizzler working with customers to understand their business problems and to identify if Machine Learning is the right path to solve those problems. Over the past 5 years , CodeSizzler come up with the solutions for business problems.
Build and deploy models more securely with network isolation and private link capabilities, role-based access control for resources and actions, custom roles and managed identity for compute resources.
Better manage resource allocations for Azure Machine Learning compute instances with workspace and resource-level quota limits.
Hybrid and multi-cloud support
Run machine learning on existing Kubernetes clusters on-premises, in multi-cloud and at the edge with Azure Arc. Use the simple one-click deploy ML agent to start training models securely, wherever your data lives.
Use the central registry to store and track data, models and metadata. Automatically capture lineage and governance data. Use Git to track work and GitHub Actions to implement workflows. Manage and monitor runs or compare multiple runs for training and experimentation.
Drag-and-drop machine learning
Use machine learning tools like designer with modules for data transformation, model training and evaluation or to easily create and publish machine learning pipelines.
Maximize productivity with IntelliSense, easy compute and kernel switching and offline notebook editing.
Automated machine learning
Rapidly create accurate models for classification, regression and time-series forecasting. Use model interpretability to understand how the model was built.