Tag: Machine Learning

From transforming one-time sales into recurring revenue streams to harnessing the power of generative AI, this interview recap unveils a glimpse in our future of software engineering. Watch the video to discover how our expertise — combined with FPT's resources — opens doors to unprecedented innovation.

Our data-driven world increasingly requires tools to understand customer sentiment, public opinions and market trends. Enter sentiment analysis. Check out how our product engineering experts leveraged the powerful capabilities of Amazon SageMaker to conduct a sentiment analysis initiative analyzing tweets discussing the 2020 U.S. presidential election.

The future of investigative surveillance is now. Join us as our client Dan Coleman of Creative Solutions Investigative Services discusses rewriting the rules of surveillance technology on the Revenue Fuel podcast.

Generative AI offers new capabilities for database analysis but integrating the innovative technology into products remains difficult. In this blog post, our engineering experts dive into the promise and pitfalls of building products harnessing generative AI for database analysis.

From predictive modeling in financial analytics to image recognition in healthcare and environmental monitoring, our informative blog post showcases diverse machine learning for signal processing applications. Dive in to navigate this exciting intersection of technologies.

Exploring TinyML: From quality control to energy optimization, embedded machine learning at the edge provides numerous benefits. Discover the benefits, applications, tools and technologies to get started.

The healthcare industry is undergoing a digital transformation. IoT healthcare devices enhance patient safety, improve care and provider decision-making, and drive better health outcomes. As healthcare delivery extends beyond the walls of hospitals, understanding the different product design and development challenges for Internet of Things in healthcare is vital for a successful launch.

With the ongoing proliferation of IoT devices, understanding how to build edge ML/AI applications using tools like STM32Cube.AI will only become more important. Our engineering expert tackles the process in this tutorial, outlining the steps necessary for building ML/AI applications at the edge.

As IoT devices continue to proliferate, knowledge of how to build ML/AI applications at the edge will increase in importance. In this post, our expert engineer highlights the steps involved in building such applications utilizing the OpenMV Cam.

Our expert engineer highlights how variability can negatively impact the algorithms powering devices leveraging artificial intelligence and machine learning in the real world.

Here’s a quick rundown from a tech perspective of what Google announced last week at its I/O conference: Google.ai Google bases most of its service model on artificial intelligence (AI). In this year’s I/O, they announced some really cool new data-center clusters called Tensor Processing Units (TPUs): huge, heavy-duty number-crunching machines that each provide up... View Article