Unlock the value of machine learning in production
Bringing machine learning in production is more difficult than just training ML models and deploying them as APIs for prediction. Only a small percentage of ML projects make it to production because of deployment complexity, lack of governance tools and many other reasons. Once in production, ML models often fail to adapt to the changes in the environment and its dynamic data which results in performance degradation.
To maintain the prediction accuracy of ML models in production, an active monitoring of model performance is mandatory. This allows to know when to retrain it using the most recent data and the newest implementation techniques, then redeploy in production.
To achieve this virtuous circle, an established CI/CD (continuous integration/continuous delivery), as well as continuous model training, suited for ML systems, is necessary. Deploying an ML pipeline that can automate the retraining and deployment of new models will help you adapt to rapid changes in your data and business environment.
Machine Learning Open Studio (MLOS) product from Activeeon helps data scientists and IT operations work together in an MLOps approach allowing to bring ML models to production. It simplifies machine learning application lifecycle management providing end-to-end orchestration, automation and scalability.
MLOps covers the whole machine learning (or deep learning) lifecycle: model generation (ML development lifecycle, continuous integration/continuous delivery), orchestration and deployment, monitoring and analytics. You can deploy, monitor, and manage machine learning models in production, then govern their use in production environments.
Machine Learning Open Studio (MLOS) from Activeeon enables a repeatable and scalable machine learning lifecycle to lower complexity of AI for fast delivery. It helps you create adaptable pipelines in order to work with dynamic models.
Package & deploy
Automate & manage
Monitor & govern
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
Get started with MLOps using Machine Learning Open Studio
More and more companies are investing in MLOps to create enterprise-class models for automation and productivity. MLOps is the best way to bring benefits of AI into production.
Machine Learning Open Studio enables you to automate model training, application deployment, optimization and monitoring in production. Analytics dashboards provide all specific metrics including infrastructure monitoring, resource usage and notifications.
You don’t have to immediately move all of your processes from manual to fully automated pipelines. You can gradually implement MLOps practices to improve the automation of your ML system development and production.
Machine Learning Open Studio (MLOS) was created for data scientists, including citizen data scientists, and IT operations. Its graphical user intefaces and integrations with existing machine learning platforms allow to achieve machine learning pipeline automation using scalable workflows powered by ProActive Workflows & Scheduling.
Contact us to discuss your project: our team will be happy to demo the capacities of our solution with regard to your specific business case.