Advantages of ProActive Machine Learning for MLOps

Integration with existing tools & platforms to ease model deployment
Deploy your machine learning models using existing data science notebooks like Jupyter
Select your favourite libraries
Take advantage of over a hundred included connectors to cloud, big data & machine learning platforms
Integrations to existing engines like Tensorflow, Spark, Flink, Docker
Deploy your ML projects on modern production infrastructures
Open solution with access through GUI, REST API or CLI

Dynamic scalability in production to manage thousands of model pipelines
Scale machine learning applications in production
Execute at large scale on many CPU cores and GPU
Portability with Docker, Kubernetes, Podman & Singularity to scale model deployment & HPC workloads
Deploy scalable machine learning applications anywhere: cloud, on-premise, hybrid
Work with existing ops infrastructures & technologies: modern mechanisms to support LDAP/Active Directory and role-based access control

First-class workflows for end-to-end machine learning orchestration
Minimize the complexity of AI with repeatable & scalable machine learning lifecycle
AI packages & workflows ready to be used in production
Self-service catalog with hundreds of examples of machine leaning & deep learning tasks & workflows
Machine learning models as a service (MaaS)
AutoML to accelerate finding the right parameters, hyperparameter tuning
Automatic data deviation detection
Audit & traceability with incremental archive of model predictions in production

Machine Learning application management, monitoring and optimization through modern portals for all kinds of users
Workflow studio for definition & customization of ML tasks
Integrated scheduler & Resource Manager to automate machine learning workloads, monitor execution & infrastructure, set up alerts, prioritize jobs, access full logs, analyze results
Automation Dashboard with calendar-based workflow automation & project visualization in Gantt mode
Job Analytics to quickly select the best model & view model-specific metrics
Workflow versioning for traceability over code and data