Machine Learning & MLOps
Predicting what will happen next...
What is Machine Learning?
Machine Learning in simplest terms is, computing systems that become smarter with experience. What do we mean by experience?
Experience is past data + human input. And that past data is often huge — the quantity of data is doubling about every 18 months. Computers can process far more variables than a human making the same decision.
And what do we mean by human input? Human input takes two forms — the input of the user who is either communicating that the output is what they are looking to see or not. In the case that it’s not, the machine can either self-adjust to deliver better results moving forward or the advanced analytics developer or data scientist can make those changes to the model.
Machine Learning Operations (MLOps) is based on DevOps principles and practices that increase the efficiency of workflows. For example, continuous integration, delivery, and deployment.
MLOps
MLOps initiatives are focused on the following 4 areas:
- Model source control/reproducibility — Processes and procedures to make models reproducible (from source control to data retention policies)
- Model validation — unit testing, functional testing and performance testing — both in isolation and when embedded in an application
- Model versioning & storage — provide a consistent way to store & share models, plus a way to track where models are embedded / running
- Model deployment — describing an efficient process to get a model build into an application or service and leveraged to light up an end-user scenario
We help your organization with developing a strategy and roadmap for deploying MLOps environment and ML predictive models in support your strategic objectives. In addition, we work closely with your team to prepare data, develop machine learning algorithm models and deploy to client based devices, applications and dashboards, so they can make better and more profitable decisions.
Our Partner Services
What are some examples we can help you with?
- Churn Analysis — to predict which customers may leave and help craft strategies to keep them satisfied.
- Patient Outcomes — recognize patterns to improve accuracy of diagnosis, or patient readmissions; prescriptive analytics to assist with preventive care.
- Recommendation — engines which can leverage huge volumes of customer data to offer customers suggestions of what they might want next.
- Fraud Detection — to flag orders or behaviors which are indicative of a scam and help you stay one step ahead of criminals.
- Financial Performance — grow your business by predicting, evaluating, and responding to market opportunities in real-time.
- Revenue Cycle Management — predict which claims require early attention to help mitigate denials, and to forecast financial opportunities.