Dotscience announces advancements to deploy and monitoring for ML models to unblock AI in enterprises

Dotscience announced on Wednesday new platform advancements that offer the easiest way to deploy and monitor machine Learning models on Kubernetes clusters, making Kubernetes simple and accessible to data scientists. New Dotscience Deploy and Monitor features simplify the act of deploying ML models to Kubernetes and setting up monitoring dashboards for the deployed models with cloud-native tools Prometheus and Grafana, reducing the time spent on these tasks from weeks to seconds. 

Dotscience now also enables hybrid and multi-cloud scenarios where, for example, model training can happen on-prem using an attached Dotscience runner, and models can then be deployed to a Kubernetes cluster in the cloud for inference using a Dotscience Kubernetes deployer. 

Dotscience also announced a joint effort with S&P Global to develop best practices for collaborative, end-to-end ML data and model management that ensure the delivery of business value from AI.

While other solutions on the market aim to solve only specific parts of ML development and operations, requiring further integration work in order to provide end-to-end functionality, Dotscience enables data science and ML teams to own and control the entire model development and operations process, from data ingestion, through training and testing, to deploying straight into a Kubernetes cluster, and monitoring that model in production to understand its behavior as new data flows in. 

Furthermore, alongside the built-in Jupyter environment, Dotscience users can now use any development environment they like by using the Dotscience Python library.

Data science and ML teams can use Dotscience to ingest data, perform data engineering, train and test models and then deploy them to CI for further testing before final deployment to production with a single click, command or API call where the models can then be statistically monitored.

Dotscience’s Deploy gives users the ability to handle both building the ML model into a Docker image and deploying it to a Kubernetes cluster; hand the entire CI/CD responsibility over to existing infrastructure, if preferred, or use lightweight built-ins; and track deployment of the ML model back to the provenance of the model and the data it was trained on to maintain accountability across the entire ML lifecycle.

Dotscience’s statistical monitoring feature allows ML teams to define which metrics they would like to monitor on their deployed models and then bring those metrics straight back into the Dotscience Hub interface where the team first developed the model. This allows ML teams to “own” the health of the model throughout the entire development lifecycle and avoids integrations with other monitoring solutions and costly handovers between teams. 

By enabling data science teams to own the monitoring of their models, Dotscience brings the notion of integrated DevOps teams to ML, eliminating silos, maximizing productivity and minimizing mean time to recovery (MTTR) if there are issues with a model.

“While there are visionaries like S&P in the market who also recognize the need for reproducibility, provenance and enhanced collaboration in the model development phase of the lifecycle, our push to simplify deployment and monitoring of AI/ML is based on the market insight that many businesses are still struggling with deploying their ML models, blocking any business value from AI/ML initiatives,” said Luke Marsden, CEO and founder of Dotscience. 

“In addition, monitoring models in ML-specific ways is not obvious to software-focused DevOps teams. By dramatically simplifying deployment and monitoring of models, Dotscience is making MLOps accessible to every data scientist without forcing them to set up and configure complex and powerful tools like Kubernetes, Prometheus and Grafana from scratch,” Marsden added.