Computer Vision in Industry Vs. Academia
This Article is inspired by the great book of Chip Huyen, “Designing Machine Learning Systems”
Stepping from academia into the computer vision industry can feel like a different game. Fresh out of college? Spent time learning through online courses or academic papers? Then you'll know the theory. But applying that knowledge in a real-world job? That's a whole new challenge. Here's the thing: academic computer vision and industrial computer vision are two different worlds. In this article, we'll help you understand these differences. We're all about clear, simple, and honest insights, helping you navigate the industry landscape. So let's dive into the world of "Industrial Computer Vision".
In the world of academia, your mission is clear: develop a groundbreaking model or approach that outperforms previous ideas, typically measured against a benchmark dataset like ImageNet. The primary goal, more often than not, is about accuracy.
However, when you step into the realm of industry, the picture becomes more nuanced. Imagine you're a machine learning engineer at a company that develops surveillance monitoring software. Your primary task is to increase the accuracy of person tracking. The solution appears straightforward to you: employ facial recognition for better precision in tracking.
This seems like a great idea from a Computer Vision standpoint, but the other stakeholders in the company have differing views. The company's law consultant raises privacy concerns as implementing facial recognition to identify individuals violates privacy rules. The product team is also against your proposal, fearing that it might overload the system, causing latency that could negatively impact user experience.
This example underscores a significant difference between academia and industry in the field of AI and Computer Vision: there's more to consider than just accuracy. In industry, you'll be juggling a variety of considerations and coordinating with different stakeholders, each with their own unique goals. As a machine learning engineer working on "Computer Vision in Industry", this is a key part of your role, and understanding these nuances is an essential step away from academia.
In Industry, Deployment Trumps Training
In academia, the spotlight is often on crafting the most advanced solution. This translates into students and researchers dedicating a vast chunk of their time to developing and training Computer Vision models. Their focus is on running a plethora of experiments, testing hypotheses, and aiming for quicker training times to maximize throughput.
Contrast this with the industrial context, where the narrative shifts dramatically. When it comes to 'Computer Vision in Industry,' the emphasis is on model deployment. The success metric now is not how many hypotheses you can test, but how quickly a user gets a response from the deployed model. Every fraction of a second counts when it comes to user experience. A slow response could mean lower conversion rates and fewer users. So, the objective here is to design systems with lower latency, even if it comes at the cost of throughput.
In essence, while training is a primary concern in academic circles, deployment takes center stage in the industrial scenario. Being aware of this shift in priorities is crucial as you transition from academia to the industry.
The Reality of Data in Industry
In the academic sphere, the limelight is mostly on the models. Here, data is often served to you on a silver platter – clean, structured, and consistent. Researchers focus on model development and optimization, seldom thinking about the data they feed into these models. They have benchmark datasets at their disposal that have been preprocessed and cleaned, allowing for easy comparison and evaluation of different models.
However, this idyllic scenario morphs once you step into the world of industry. Suddenly, data is no longer a side player, it's at the center stage. Industrial projects are riddled with data challenges, consuming a significant portion of an engineer's time.
Often, there's no readily available dataset for your project. Data collection, curation, synthesis, and labelling become your responsibility. Even when data is available, it's not served up neatly. You're dealing with noise, missing data, and inconsistencies that need to be tackled before you can even start thinking about model development.
If you're planning to transition from academia to industry, brace yourself for these data-related challenges. Invest time in understanding data management tools and familiarizing yourself with the best practices. Handling real-world data is not as glamorous as developing fancy models, but it's often the bedrock of successful industrial Computer Vision applications.
Ethical and Social Aspects of Computer Vision in Industry
The creation of a Computer Vision algorithm in academia can feel somewhat detached from the real world. You're building a model that, for the most part, won't directly touch a human life. It's more of a conceptual construct designed to outperform others in metrics such as accuracy, precision, or recall. However, the scenario shifts dramatically when your work enters the realm of industry.
Once you deploy your model, it interacts with real people. And with that, ethical and social considerations come into play. Today's Machine Learning and Computer Vision models are notoriously prone to biases. For example, they may identify individuals of certain racial or ethnic backgrounds as potential criminals with a higher probability. This kind of skewed processing can lead to significant societal issues and damage your organization's reputation. Therefore, it's crucial to rigorously test your models against various biases before deploying them.
Another pressing concern in the industry is model explainability and interpretability, particularly in sensitive sectors like healthcare and finance. Users have a right to understand why a model made a specific prediction – why they were denied a loan or diagnosed with a disease. From a development perspective, understanding the inner workings of a model helps debug and improve it. However, the rise of intricate deep neural networks and other 'black box' models makes it increasingly challenging to comprehend what's happening under the hood.
As you transition from academia to industry, be prepared to grapple with these ethical and societal considerations. Balancing technical prowess with ethical responsibility is a critical aspect of industrial Computer Vision.
Transitioning from an academic background to the world of industry can be a challenging yet incredibly rewarding journey. The differences are vast – from divergent objectives and performance metrics to tackling real-world data problems and navigating ethical considerations. However, these challenges aren't insurmountable. With the right mindset, tools, and guidance, you can successfully make this transition.
Remember, what you develop in the industry doesn't merely exist in research papers or theory – it's a real-world application that impacts actual people and businesses. It's essential to be mindful of the broader scope and implications of your work. It's not just about achieving high accuracy; it's about deploying models that are fair, interpretable, and beneficial for society.
As you take your first steps in the industry, equip yourself with a holistic view of computer vision. Understand the business, legal, ethical, and societal dimensions of your work, alongside the technical aspects. Stay open-minded, be ready to learn, and continuously evolve.
With these perspectives and approaches, you'll not only contribute to your organization's success but also the broader development of ethical, effective, and impactful computer vision technologies in the industry.
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