Ml Ops Services
ML Ops Services
In the Big Data and AI Executive Survey 2021, they evaluated that while 92% of organizations are accelerating the pace of AI investments only 29% are achieving transformational business outcomes.
When we consider the reality presented in the results of this survey, it is a sobering reminder that new technologies come with significant risks and costs to implementation! New technologies implemented poorly may find themselves neither fit for purpose nor fit for use. The implementation of technology must always serve the business's short-term operational and long-term strategic needs.
Only a small portion of Machine Learning implementations are included in the development and training of the ML model. There is significant amount of technical debt associated with Machine Learning model implementation which account for the majority of time spent in development. Configuration, Serving Infrastructure, Data Collection, Feature Extraction, Data Verification, Monitoring, Analysis, Process Management, and Machine Resource Management are many critical components that overshadow the implementation.
This is where Machine Learning Operations (MLOps) come into play. MLOps seeks to unify ML system development and ML System Operations. Through simplified model deployment, machine learning monitoring capabilities, production life cycle management, and production model governance, MLOps helps to ensure your implementation of Machine Learning is successful.
MLOps is a requirement if you intend to scale up the number of machine-learning-driven applications you have in operations. MLOps also serves to reduce or eliminate the training-serving skew, which represent differences between the training set and operational environment, where drops in accuracy are commonly experienced.
Perhaps the biggest benefit of MLOps however is that it develops trust in AI and ML technologies within the organization. This is achieved through the automation and monitoring of ML systems integration, testing, release, deployment, and infrastructure management. Following the implementation of MLOps, your organization will see quantifiable value in its machine learning implementations, and in their scalability. Additionally, the successful implementation of machine learning and MLOps will give time back to ML development staff, that they would otherwise dedicate to micromanaging the models.
At ChAI, we have 15+ years of combined experience in leading MLOps implementations. Please use the contact form below to request your free initial consultation, so that we may discuss how our team can help you to achieve a successful implementation