At Top Data Science, we have been utilizing the latest and most powerful AI technologies since the company was established. We are continuously following the technology advancements, especially in the areas with the greatest potential value for key industrial market segments. Our insights also include key learnings and best practices for developing and delivering AI-based services successfully to our customers.
AI technology areas that we are happy to share insights include:
The field of computer vision is advancing at an incredibly rapid pace, with new concepts and technologies emerging each year. Keeping abreast of these developments and understanding how they can be applied to your business can be a daunting task. In our projects, we regularly deal with such tasks as Image Recognition, Object Detection, Semantic and Instance segmentation, Image Generation, Object Tracking, Re-Identification, Transfer Learning, and Self-Supervised Learning. We offer professional overviews of these topics tailored to the needs of our audience. With our expertise, you can stay informed about the latest computer vision advancements and leverage them to benefit your business.
Industrial Process Control
The ultimate goal of Industrial Process Control is to improve the plant’s performance in terms of uptime, quality, and production. Model predictive control (MPC) is a well-established technology for advanced process control (APC). It has proven useful in many industrial applications, with multivariable processes with complex constraints, delays and strong interactive feed-forward and feedback loops.
Artificial Intelligence and deep learning neural networks can upgrade MPC from the traditional manually engineered models to more data-driven mechanisms of control. Top Data Science brings its world-class AI expertise, best practices and state-of-the-art tools to help industrial companies succeed with AI-based process optimization.
Machine learning models, when integrated into a solution, system, or service, are expected to maintain their performance and meet the established quality and performance metrics. However, due to the nature of most operative processes, changes in business requirements, data, or technical deployment environment may degrade the performance of the model.
MLOps encompasses a set of practices that aim to reliably and efficiently deploy and maintain machine learning models in production. The term combines “machine learning” with the continuous development practice of DevOps in the software industry. MLOps aims to increase automation and improve the quality of production models, while concurrently addressing business and regulatory demands. While MLOps started as a set of best practices, it is evolving into a standalone approach to manage the entire lifecycle of machine learning models.
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Creative Potential of Generative AI
Generative AI has the ability to create new content that is both original and unique. This technology has a wide range of applications, from art and music to language translation and even drug discovery. Some well-known examples include the language model GPT-3 (used in ChatGPT) and text-to-image models e.g. Stable diffusion and DALL-E. Generative AI has the potential to revolutionize many industries, enabling businesses to create more personalized products, and unlocking new levels of creativity and innovation. However, as with any emerging technology, there are also concerns around the ethical implications of generative AI and its impact on society.
Gaining insights on AI and machine learning technologies that are relevant to your industry
Hearing the latest updates on technology developments, and what additional value they may provide to organizations involved in the development and use of AI
Hearing about the best practices in developing and utilizing AI