MLOps: Operationalize Machine Learning Models
MLOps: Operationalize Machine Learning Models
Overview
Artificial intelligence & machine learning are transforming organizations and industries. In the current environment, organizations are focused on existing technology lifecycle and project best practices, that fit DataOps, but does not fit as is to Data Science. Unfortunately, without strong data science framework, most projects fail to deliver their value. AI/ML is difficult to implement and new to many organizations.
Organizations should adopt MLOps (machine learning operations) practices to make their AI and ML initiatives successful.
When AI/ML projects lack a framework and architecture to support model building, deployment, and monitoring – they fail. To succeed, it needs collaboration between data scientists and data engineers for automating and productizing machine-learning algorithms.
MLOps is the process of operationalizing your machine learning models. It's an extended framework of DataOps.
For MLOps to be implemented correctly, the framework relay on specific roles, such as Data Engineer, Data Scientist, Subject Matter Experts and Business Owner, again an extension of DataOps.
The MLOps Framework
There are five steps that form the framework for successful MLOps.
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Understand Your Business KPIs.
Organizations need to define key metrics and KPIs. This is a non technical phase primarily requires collaboration between data stewards and subject matter experts. The interaction will result in joint understanding and clear definition of “what success means”.
Acquire Data
In the data acquisition phase, data scientists, data engineers, subject matter experts collaborate on discovering the data needed for machine learning, ingesting and integrating it into the cloud data lakehouse, ensuring the data quality rules are applied, and data is prepared and ready for modeling. During this phase data will be labeled, and be divided into Test and Train.
Develop ML models
Model development is the core of the MLOps framework. Model development is taken care of by data scientists, and using the Test and Train data, as well as the defined metrics and KPIs, the models are iterated and fine tuned.
The Informatica Data Engineering portfolio integrates with ML development tools and processes to support this step.
This is done in a Non Production environment.
Deploy ML Models
The data engineer integrates the ML model developed by the data scientist and validates it against the production data. This involves further validating OKRs and KPIs. The data pipeline is then deployed into production with DataOps teams for continuous use and monitoring.
Monitor and Retrain ML Models
During the model monitoring phase, a deployed pipeline is integrated with a metrics monitoring mechanism. Data Scientists will continue to retrain the model based on an agreed upon frequency. The DataOps team can then monitor the pipeline metrics, ensuring continued value and increasing confidence in ML.
In Summary, a well thought approach as given in the MLOps framework above is essential to the success of your AI/ML use cases. aiData Works’s Services provide end-to-end functionality for MLOps.
MLOps require the orchestration of multiple, disparate tools and the skills to integrate them while managing it across a complex environment. aiData Works's MLOps Consulting helps you to Deploy, monitor, manage, and govern your entire AI/ML pipeline in one singular place with machine learning operations with the help of Informatica Products, as well as, when needed, help with the Data Science side of the house: analysis, model development and training.