AI for Visual Data: Computer Vision in Business

WebinarOctober 2023

Visual data is becoming increasingly important for business as AI allows you to extract more value from it. However, working with this type of data has a lot of challenges, and mistakes can be expensive. In this webinar, three computer vision executives discuss the best ways to get value from your visual data.

Throughout this session, you’ll learn about high value use-cases for image & video data, best practices for managing and analyzing visual data, and an overview of the latest cutting edge innovations in computer vision.

Key Takeaways:

Learn about high value use cases for image and video data.
Learn best practices for analyzing and managing visual data.
Learn about cutting edge innovations in computer vision.

Additional Resources

[BLOG] What is Image Recognition?

[PODCAST] Expanding the Scope of Generative AI in the Enterprise with Bal Heroor, CEO and Principal at Mactores

Summary

Image and video data in data science have seen significant advancements, making visual data essential for businesses. This webinar brings together experts like Sanjay Pichai from AcroData, Vishakha Guptakledhat from Aperture Data, and Natan from DESI to explore the increasing importance of visual data in various sectors and the problems that come with it. Applications extend from agriculture and manufacturing to retail and autonomous vehicles, demonstrating the versatility of computer vision. The shift from classical computer vision to deep learning has improved the ability to recognize any object, not specific ones. Data scientists and machine learning engineers play an important role in solving issues related to visual data, emphasizing the need for data quality and bias avoidance. Image classification is recommended as a starting point for organizations entering this field. As companies scale up their visual data projects, they face problems in managing and ensuring bias-free data. The webinar emphasizes the need for skill development in Python, data management, and the use of tools like PyTorch and Hugging Face for deep learning, encouraging a practical approach to learning and experimenting with visual data.

Key Takeaways:

Visual data is increasingly important across sectors due to AI advancements.
Moving from classical computer vision to deep learning improves object recognition capabilities.
Data quality and bias are significant challenges in deploying visual data solutions.
Image classification is a recommended starting point for organizations new to computer vision.
Practical learning in Python and deep learning tools is essential for skill development in this field.

Deep Dives

Importance of Visual Data in Business Use Cases

Visual data has become an integral component i ...
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n various sectors, transforming traditional operations and enabling new capabilities. In agriculture, for instance, drones and satellite imagery are employed to monitor crop health, detect pests, and optimize pesticide usage. Retailers utilize visual data for inventory management, cashierless checkouts, and personalized shopper insights. In manufacturing, visual data aids in quality inspection and worker safety by identifying defects on production lines. The medical sector leverages visual data for faster and more precise diagnoses through medical imaging. This shift towards visual data-driven operations is supported by advancements in AI and machine learning, allowing businesses to extract actionable insights and enhance efficiency.

Transitioning from Classical Computer Vision to Deep Learning

The evolution from classical computer vision to deep learning represents a significant leap forward in the field. Classical approaches relied on handcrafted features and custom models, which were effective for specific tasks but lacked versatility. In contrast, deep learning models learn from vast datasets, enabling them to handle a wider range of scenarios and recognize a broader spectrum of objects. As Sanjay Pichai noted, "Deep learning techniques, with neural networks at their core, have revolutionized how we approach visual data." This shift has been particularly impactful in autonomous vehicles and robotics, where diverse and changing environments require adaptable and sturdy models.

Challenges and Best Practices in Managing Visual Data

Ensuring high-quality, unbiased data is a fundamental challenge in visual data projects. Data scientists must carefully curate datasets to prevent biases that could skew model outputs and lead to unfair outcomes. Natan emphasized the importance of maintaining consistent data distributions across training and validation sets, highlighting the potential for bias if certain classes or features are underrepresented. As companies scale their visual data initiatives, they must also address issues related to data storage, labeling, and privacy. This requires a concerted effort across teams, from data engineers to machine learning practitioners, to ensure that the data used in training models is representative and ethically sourced.

Getting Started with Image Classification

For organizations new to computer vision, image classification offers a manageable entry point. The process involves training models to categorize images into predefined classes, a task that can be tackled with ready-made models like YOLO. As Vishakha Guptakledhat pointed out, "Image classification is relatively simple, but it's an essential first step in understanding the potential of visual data." Companies can start by experimenting with small datasets and gradually scale up as they refine their models and processes. This approach allows organizations to gain insights into their data and develop the necessary infrastructure to support more complex computer vision tasks in the future.