Posts Tagged ‘machine learning’

In a landmark effort to both guide and spur artificial intelligence (AI) and machine learning (ML) development in the medical field, the US Food and Drug Administration (FDA) has released an Artificial Intelligence/Machine Learning (AI/ML)-Based Software as a Medical Device (SaMD) Action Plan. Background The plan was created by the FDA’s Digital Health Center of […]

Managing an analytics ecosystem for an organization is a mission critical responsibility. Whether it’s the accuracy of the reporting data, the latency of data refreshes, security enforcement, dashboard performance, turnaround of enhancement requests, scalability, or adherence to regulatory and organizational policies, there are various aspects that come into play when it comes to successful adoption […]

As AI continues to expand its reach into the enterprise, so does the desire to maximize its full potential. But the journey to realizing all of your AI aspirations can be a bumpy road with twists and turns if you don’t have an experienced consulting partner to help navigate. Technology maturity, opportunity cost, fast-changing regulations, […]

According to the IDC’s Worldwide Artificial Intelligence Spending Guide, implementing AI has become a necessity for businesses to become more agile, innovate, and scale. And it appears that more companies are coming to terms with this as global spending is expected to reach more than $110 billion by 2024. In season 1 episode 3 of […]

The stakes to evolve your business and adapt to new realities have never been higher. How do you accelerate and scale your transformation cost-effectively? The answer – optimized global delivery. Follow this series and learn more about our nearshore software development capabilities from members of our global consulting team in Colombia. When coordinating software development […]

We are excited to be listed by Forrester in its recent report The Forrester New Wave™: Computer Vision Consultancies, Q4 2020 – The 13 Computer Vision Providers That Matter Most and How They Stack Up. The New Wave™ report differs from the independent research firm’s traditional Forrester Wave™ report in the way that it only […]

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 […]