Productizing ML and DL is a collective-action problem that MLOps platforms attempt to solve
ML and DL models burn in production because some of us are better at training algorithms to interact well with one another than getting along with other team members.
Communicating the breadth and depth of model deployment requirements over and over again can be daunting at times. An MLOps platform introduces CI/CD best practices and promotes knowledge sharing between the data scientists and Ops specialists. With collaborative workspace, version control, and model registration, developers can better pass critical knowledge to the operations teams.
Model management and monitoring tools, in turn, help the Ops people stay abreast of the models’ performance, accuracy, and resource usage and report any deviations rapidly back to the development team to drive continuous improvements.
Deploy ML models with confidence and scale your AI capabilities without any constrains
Unlike other proprietary MLOps platforms Neu.ro places no constraints on how you can deploy your models. Rely on containers or serve your models as API services using the framework you prefer — Flask, Spring, or TensorFlow.js.
Got another approach? We accommodate that too.
At any rate, we’d help you set up semi-automated pipelines for risk-averse model deployments and make sure that your models can be easily integrated with or within other apps. In the same vein, we help you stay flexible with your choice of supporting libraries, tools, notebooks, or cloud computing resources.
We can add, upgrade, or replace any component of your MLOps platform as per your latest needs to ensure that your team has access to the exact resources they need for the upcoming project.