Machine Learning Operations (MLOps) platform

Unlock the value of machine learning in production

Ease collaboration between data scientists and IT/Ops

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.

ProActive Machine Learning 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.

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machine learning model deployment pipeline

Machine learning model lifecycle automation

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.

ProActive Machine Learning 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.

train model

Train model

hybrid infrastructure management

Package & deploy

job priority

Automate & manage

job priority

Monitor & govern

job priority

Retrain model

Advantages of ProActive Machine Learning for MLOps

Screenshot ProActive ProActive Machine Learning

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

screenshot proactive resource manager

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

screenshot proactive studio workflow

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

screenshot job analytics

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 ProActive Machine Learning

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.

ProActive Machine Learning 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.

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How to integrate machine learning in current automation processes?

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.

ProActive Machine Learning 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.

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Distribute workload over all resources