ML Ops implementation

• reduce manual or script driven processes during pre model deployment phases
• continuous training for models: Practices to automatically train model using fresh data based on live pipeline triggers
• active monitoring the quality of your model in production & check for model staleness
• new experimentation iteration retraining of the model on new data
• continuous delivery of models

on-demand professional services

We get it, not everything can be automated and some situations require ad-hoc custom support

• talk to our ML experts via flexible custom ‘tech blocks’
• on-demand architectural consulting or hands-on support for those times when you need more hands on deck to get things completed faster