Whether you're starting your journey by deploying your first ML workload on AWS or if your company is looking at expanding your ML footprint, the value of ML Ops cannot be understated. Bringing the discipline and efficiency of DevOps into your ML practices creates a system process for moving your models to production, at scale and in real time, and delivers optimal results through managing and monitoring your ML models over time.

In this consulting offer, TensorIoT consulting will work with your company to create a standard for managing ML pipelines, where you can catalog models for production, manage model versions, associate metadata with models, manage the approval status of a model, deploy models to production, and automate model deployment with continuous integration and continuous delivery (CI/CD).

As more and more companies embark on creating Machine Learning based solutions, ML Ops becomes a core need to enable the developers and support personnel to create a stable and scalable solution.

In this consulting offer, TensorIoT consultants will work with your company to develop a custom ML Ops pipeline from the ground up. TensorIoT consultant will work with your team to understand your business needs, understand your current development and monitoring tool chain, identify a target ML application, and design and implement a ML Ops pipeline that includes toolsets for ML code repo, build and deployments. The solution will also incorporate model monitoring and reporting on model performance. This will enable your development team to rapid release cycles on ML projects and best practices based model monitoring capabilities.