Posts Tagged ‘machine learning’

As our clients continue their digital transformation journeys, challenges with traditional document capture solutions are coming to the forefront. Managing and configuring classification sample sets and extraction rules in layout-driven, legacy solutions, is time consuming and expensive. Modern digital businesses require systems that can continue to provide accurate results as forms and businesses evolve. Existing […]

On July 23, 2020, Caz Halsey, Director, Life Sciences, Perficient, and Damon Fahimi, Product Manager, Uppsala Monitoring Center, hosted a webinar that discussed WHODrug Koda and how it can be integrated with other systems using an application developed by Perficient. The topics covered include: WHODrug Global and Drug Coding Background An Introduction to WHODrug Koda […]

Tuning Spark for your machine learning pipeline can be a complex and time consuming process. Store and compute play a different role for your Spark cluster in different stages of your machine learning pipeline. Spark defaults are never the right way to go. It makes more sense to know what settings are most effective at […]

What if you had a Wikipedia of all of the topics that are being discussed at work? How would that change your productivity? Imagine a place that had an explanation for the acronyms, pages with information about the projects and processes at work linked to the experts, files, meetings. Imagine a way to navigate who […]

Join us and our partner Amazon Web Services (AWS) for a virtual Q&A session on Wednesday, April 15. AWS will feature one of our experts to speak on a panel about the evolution and progress being made to solve critical business problems such as customer personalization and forecasting through the use of data science. Perficient […]

Previously, I analyzed how dirty, or bad data is the enemy of machine learning. The final blog of this series describes the advantages of artificial intelligence and machine learning in financial services. Many financial services organizations have already begun to take advantage of ML technology because of its proven ability to reduce operational costs, increase […]

Previously, I discussed machine learning and the traits that separate it from artificial intelligence. This blog analyzes how dirty, or bad data, is the enemy of machine learning. While entirely accurate and complete data is the goal of a comprehensive data management program, many firms fall somewhat short. Siloed governance projects and the lack of […]

The term artificial intelligence (AI) was first coined in 1956 by the computer scientist, John McCarty, when he held the first academic conference on the subject at Dartmouth College. McCarty, who is widely recognized as the father of AI, defined it as “the science and engineering of making intelligent machines.” Over time, the meaning of […]

Previously, I dove into how artificial intelligence helps review, and provide statistical analysis to data. The final blog of this series outlines how to be successful with an artificial intelligence implementation. Setting initial expectations and not promising a magic bullet is a key factor in determining the success of an initiative that focuses on deploying […]

Previously, I analyzed fraud detection and content management methods, with the assistance of artificial intelligence (AI). In my final post of this blog series, I explain the opportunities artificial intelligence provides to retirement services transformations. For many 401(k) plans, passing ADP/ACP testing can be a very difficult and challenging endeavor, and there can be severe […]

  Perficient Presents at Oracle OpenWorld 2019 – Live from the show floor, a director in our analytics team delivers an overview of the machine learning capabilities of Oracle Analytics Cloud with its built-in ML algorithms. Machine Learning is a science that existed for several decades. However, it wasn’t until the last 10 years that […]

Many recent machine learning challenges winners are predictive model ensembles. We have seen this in the news. Data science challenges are hosted on many platforms. Techniques included decision trees, regression, and neural networks. And, winning ensembles used these in concert. But, let’s understand the pros and cons of an ensemble approach. Pros of Model Ensembles […]