This weekend I tried to step aside a little bit of my comfort zone to test what MLOps is all about.
First, lets understand some use cases on how Machine Learning is used across different areas. ML adoption requires a cultural shift and a technology environment with people, processes, and platforms operating in the responsive, agile way organizations are looking to operate today.
What is MLOpens?
Machine Learning Operations (MLOps) draws on DevOps principles and practices. Built upon notions of work efficiency, continuous integration, delivery, and deployment, DevOps responds to the needs of the agile business – in short, to be able to deliver innovation at scale.
How Does MLOps Benefit ML?
MLOps applies DevOps principles and best practices to ML delivery, enabling the delivery of ML-based innovation at scale to result in:
• Faster time to market of ML-based solutions
• More rapid rate of experimentation, driving innovation
• Assurance of quality, trustworthiness and ethicalAI
The MLOps process primarily revolves around data scientists, ML engineers, and app developers creating, training, and deploying models on prepared data sets. Once trained and validated, models are deployed into an application environment that can deal with large quantities of (often streamed) data, to enable insights to be derived.
What is necessary for a Customer to take MLOps approach?
1 – Configure Model and Data Environment.
A first step is putting in place an environment that can manage both data and modeling. Platforms and tools need to be able to support deployment to a wide range of target infrastructure and libraries optimized for ML, with multiple local/ cloud-based targets depending on model status.
- Create and configure data stores
- Prepare datasets for training
- Point algorithms and code to the data
2 – Create Pipelines for Training and Inference
We then need to consider how to organize the flow of model creation activities from training, through validation and testing, to operation and inference, as reproducible pipelines
You can use a pipeline to automatically train and deploy machine learning models with the Azure Machine Learning service. check out this short video to see how to build a machine learning model, and then deploy the model as a web service. Then, we’ll end up with a pipeline that we will use to train our model.
Real World Use Cases
One of the companies that applied MLOps is Johnson Controls. MLOps helped put models into production in a timely fashion, with a repeatable process, to deliver real-time insights on maintenance routines. As a result, chiller shutdowns could be predicted days in advance and mitigated effectively, delivering cost savings and increasing customer satisfaction. You can read more about this in http://bit.ly/MLOpsUseCases