Understanding artificial intelligence and machine learning models can help when implementing next-generation machine vision technologies that use AI modeling, suggested Edge AI and Vision Alliance.
How AI helps machine vision insights
- The 2026 Embedded Vision Summit, May 11-13 in Santa Clara, California, will address deep learning and visual AI, model training fundamentals and applications, among other topics.
- Trends and presentation topics include understanding basics of deep learning for machine vision and model training help for machine vision.
- Other trends and topics include machine learning model case studies, pitfalls solutions, strategies and how quality datasets help multimodel AI applications.
Machine vision can be helped by artificial intelligence (AI) and machine learning (ML) technologies, according to Erik Peters, director of ecosystem and community engagement, Edge AI and Vision Alliance. In a recent Edge AI and Vision Alliance newsletter, Peters said upcoming presentations at the 2026 Embedded Vision Summit, May 11-13 in Santa Clara, California, will address deep learning and visual AI, model training fundamentals and applications, among other topics.
Understanding basics of deep learning helps machine vision
Mohammad Haghighat, senior manager for CoreAI at eBay, in an “Introduction to Deep Learning and Visual AI: Fundamentals and Architectures,” expects to explore types of deep learning architectures, including fully connected networks, convolutional neural networks (CNNs), recurrent neural networks (RNNs), 3D CNNs and transformers, along with most common use cases and applications. Because AI helps machine vision technology applications, it’s also useful to understand the basics of AI, deep learning and machine learning. Visual AI helps image and video analysis, Haghighat said, so understanding the CNN building blocks and example architectures. Vision transformers (ViTs), hybrid architectures and vision-language models (VLMs) provide a solid understanding of the fundamentals of deep learning and visual AI, along with recent advancements and trends in machine vision.
Need smarter machine vision? Don’t be overwhelmed by model training
“Introduction to DNN Training: Fundamentals, Process and Best Practices” from Kevin Weekly, CEO of Think Circuits, said training a model is a crucial step for machine learning. The topic can be overwhelming for beginners, but it doesn’t need to be, Weekly, said. First examine the fundamentals of model training. Then look at different types of training, such as supervised, unsupervised and semi-supervised learning. By looking at training processes, including error surfaces, optimization methods and back-propagation, it’s easier to understand key concepts such as trainable parameters and data requirements. Variables that control the training processes include hyperparameters, regularization and batch normalization. Don’t overlook metrics to monitor during training, including loss curves, model accuracy and precision. Common training problems may include overfitting and underfitting. Popular training frameworks are among resources for further learning, Weekly suggested.
Machine learning model case studies, pitfalls solutions, strategies
Caterpillar senior data scientist, Paril Ghori, is expected to cover the machine learning model building process, providing data scientists and ML engineers with practical insights and strategies for success. Ghori’s presentation, “Mastering the End-to-end Machine Learning Model Building Process: Best Practices and Pitfalls,” said he would examine each phase of the model life cycle: data ingestion, pre-processing, feature engineering, model selection, training and fine-tuning. Best practices for model evaluation, validation and deployment include effective machine learning operations (MLOps) integration to ensure seamless model monitoring and scalability. Ghori highlights real-world case studies and common pitfalls encountered during model development, offering actionable solutions and strategies to overcome challenges. Optimizing workflows can improve performance and ensure reproducibility in complex projects, Ghori said. A deeper understanding of the model building process helps build robust, efficient and scalable models to drive impactful business outcomes and support continuous innovation, he said.
Quality datasets help multimodel AI applications
“Multimodal Enterprise-scale Applications in the Generative AI Era” looks at how AI is making rapid strides in use of large language models, and how the need for multimodality arises in multiple application scenarios, said Mumtaz Vauhkonen, senior director of AI at Skyworks Solutions. Similar to the way humans use multiple sensory systems to solve problems and arrive at decisions, in many applications AI problem-solving is enriched by using multimodal inputs, she said. Explore the process of building multimodal applications at scale by focusing on the core aspects of quality dataset creation, multimodal data fusion techniques and model pipelines for enterprise applications. Vauhkonen also examines the challenges that arise in bringing these applications to production and techniques for addressing these challenges.
Erik Peters, director of ecosystem and community engagement, Edge AI and Vision Alliance; edited by Mark T. Hoske, editor-in-chief, Control Engineering, WTWH Media, [email protected].
Keywords
Machine vision, AI and machine learning, AI model training
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The Embedded Vision Summit is organized by the Edge AI and Vision Alliance.
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