Machine Learning Model Development

Wavicle’s machine learning model development services help clients throughout the stages of the model development lifecycle — from discovery, through model building and training, and on through deployment.  Our experienced data scientists use industry-leading toolkits, libraries, and frameworks to maximize predictive power and accelerate time-to-value.


Wavicle’s machine learning (ML) consultants can help your enterprise succeed by embedding ML into business processes to deliver cumulative returns over time. Examples include dynamic pricing, recommendation engines, process automation, personalized marketing, fraud detection, and pattern recognition, among other benefits.


From experience, Wavicle understands that model pipelines and coupled data pipelines involve many moving, interdependent components. Our depth of resources in data engineering, DevOps, and data science enables Wavicle’s team-based approach toward helping your business close the gap between your data and the front line of business where value is created.


Industry-wide, data scientists are in short supply. Wavicle’s experts in our near-shore and off-shore locations are available globally to help clients 24/7 with customizable offerings ranging from turn-key solutions to supplementary assistance to aid our client teams.


What are machine learning models?


Machine learning models are algorithms built to collect, curate, and correlate data within a specified data set. Unlike software applications that perform similar functions, machine learning models are designed to run without user input. This allows them to automatically identify patterns or trends in given data sets, in turn providing actionable data for organizations.


These models also allow IT teams to focus on other tasks, since they require minimal user oversight once they’re up and running.


Why do these models matter?


Model development in machine learning is critical to reducing the amount of time required to analyze large data sets and reduce the risk of errors that result from this analysis. They’re extremely valuable in situations that involve repeated use of the same analysis framework using different data.


Consider a healthcare organization looking to improve patient care. By creating a machine learning model capable of analyzing multiple data sources — including physician reports, patient and family feedback, and treatment outcomes — healthcare providers can pinpoint potential problems or identify key trends that could help enhance operations at scale.


How do machine learning models work?


Machine learning model development starts with identifying the question you want the model to answer, which in turn pinpoints the type of data required to build your ML model.


For example, if you’re a financial firm looking to evaluate loan applicant risk, you can build a model using data sources such as current debt load, repayment history, and total assets. This model can then be applied repeatedly to different data from the same sources, in turn making it possible to evaluate hundreds or thousands of potential borrowers quickly and accurately.


It’s also worth noting that there are two broad machine learning approaches that excel in different circumstances: supervised and unsupervised learning. Supervised learning techniques examine multiple datasets to identify dependent variables that exist within the data. The financial example above is a use case for supervised learning. The answer to the question of applicant risk is already contained in the data; machine learning simply makes it easier to find.


Unsupervised learning, meanwhile, is used when answers aren’t in existing data but can be extrapolated using multiple data sources. In the case of an approved loan applicant, for example, unsupervised learning might be used to identify and suggest loan products that best meet their needs based on current finances and future goals.

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