Artificial Intelligence (AI) and Computer Vision have revolutionized the way computers perceive and interpret the visual world, bringing us closer to the realms of science fiction. From recognizing faces in our photos to guiding self-driving cars, AI Computer Vision is now an integral part of our lives. But it is the ‘how’ that is far more fascinating. Hence, this article will succinctly explore how AI and Computer Vision come into play and ultimately elucidate how automatic machine learning (autoML) and No-Code are game changers in the industry. 

The Visual Learning Process:

Have you ever wondered how computers learn to 'see' and 'understand' images and videos, much like we do with our eyes and brains? Think of it in this way: you are teaching a computer to recognize things in pictures or videos by showing it several examples, so that the computer could eventually learn from such examples. Once it is taught or trained, it can perform the task on its own. For instance, it could inform you if a picture contains a cat, or not. First, let us explore the whole cycle of the AI computer Vision process step by step: 1) Data Collection: We start by gathering a large collection of images or videos relevant to the task we want the computer to perform, such as recognizing objects or faces. 2) Data Labeling: Each image or video is carefully labeled, which means that we must precisely tag or mark objects in the images. For example, in a picture of a cat, we will label the cat figure as ‘cat.’ Inaccurate Data Labeling can be considered a crime, click here to learn how. 3) Training The Computer: We will now feed these labeled images to a computer system. The computer analyzes the images and learns patterns and relationships, to find out what features are important in recognizing different objects. 4) Adjusting and Testing: The computer goes through several rounds of adjustments and testing to become better at recognizing objects. It learns from its mistakes and improves. 5) Deployment: Once the computer has learnt and completed its adjustments, it is ready to be used. It can now look at new, unlabeled images and accurately identify objects; for example, by looking at new pictures, it could tell you if a cat is present or not. On a bigger picture, we are trying to teach a computer to see and understand the world, just like we do with our eyes and brain.

AutoML: A Game-Changer For Fast Prototyping & Iterations

In recent years, the field of AI Computer Vision has experienced the emergence of a powerful ally known as Automated Machine Learning - abbreviated as AutoML. AutoML streamlines the training process, making it more accessible and efficient. With AutoML, the system itself takes care of many of the intricate details to select the most suitable algorithms and fine-tuning parameters. Preprocessing, training, and evaluation are experimental and iterative processes that require multiple trials until achieving satisfactory results. Because these tasks tend to be repetitive, AutoML can help automate these steps. In addition to automation, optimization techniques are used during the training and evaluation process to find and select algorithms and hyperparameters. This automation not only saves time but it also reduces the barrier to entry for those who may not be AI experts.

Adding Human To The Loop:  

By incorporating AutoML into the training process, each industry expert is able to build AI Computer Vision models without the need for AI experience. They could simply utilize their knowledge in labeling datasets according to their standards, and independently train these data using AutoML tools that select the best and most accurate ML models. Adding AutoML to the scene enables businesses and researchers to accelerate the development of AI Computer Vision models, increasing its accessibility to a wider audience. It is like having an AI expert guide to assist in the intricate dance of teaching computers to 'see' and understand the visual world.

AutoML Tools For Building AI Computer Vision Models:

Whether you are looking for open-source libraries or ready-made tools, there are a myriad of tools out there to choose from. Auto-Sklearn and Auto-Keras are, for example, open-source libraries that are being used by many developers. But what if the user is a non-developer or a non-AI Expert? What do they do then? Introducing No-Code tools. These are powerful tools that are designed to simplify the process of building and deploying ML models through its user-friendly interface that automates various tasks involved in the ML workflow. Tuba.ai is a perfect example of a No-Code AutoML tool that is tailored especially for non-developer users, to build the whole process of AI Computer Vision: labeling, training, and deploying their ML model. By combining automated hyperparameter tuning and neural architecture search, its AutoML engine intelligently searches through thousands of possible models, and selects the most fitting one for each case. What is even more alluring is that Tuba.ai is NVIDIA Cloud Validated.

📣https://t.co/N5AqOWzFjb is @NVIDIA Cloud Validated! It means that you will get to enjoy a faster, more efficient, and a highly optimised #training process with the cutting-edge capabilities of @NVIDIA GPUs.

Sign-up now: https://t.co/JZyKwR5Vsy pic.twitter.com/WguNd2JFAm — DevisionX (@devisionx) October 17, 2023

In other words, you will get to enjoy a faster, more efficient and highly optimized experience in developing your AI Computer Vision model, with the cutting-edge capabilities of NVIDIA GPU’s. What difference does this make?[/vc_column_text][vc_column_text]Let us for example take an agricultural engineer/researcher who is not a developer nor an AI expert. Such a person can leverage Tuba.ai to streamline and enhance various aspects of agricultural work, including the development of predictive models to forecast crop yields, disease outbreaks, pest infestation and weather patterns. Tuba.ai can also be used by an agricultural engineer to develop models that analyze soil conditions, determining possible nutrient deficiencies (if present) as well as recommending remedies to establish the desirable soil condition for harvesting crops.

Tuba.AI: A Seamless AI Computer Vision for Everyone

Tuba.ai is putting the power of AI and Computer Vision in the hands of each industry to design and build solutions for their daily challenges, easily and sustainably. It provides a seamless AI Computer Vision Process for both, developers and non-developers.[/vc_column_text][vc_column_text]For a non-AI expert, you will benefit from the No-Code experience, as you will be exposed to a seamless, user-friendly interface that will guide you in training and building your desirable AI Computer Vision model, in an effortless and immaculate manner. [/vc_column_text][vc_column_text]As for developers, Tuba.ai provides a bundle of SDKs for you to choose from, that best suits your needs in training and developing the AI Computer Vision model that you want. From Classification Models Training SDK, to Object Detection and Segmentation Training SDK. Tuba.ai has it all.[/vc_column_text][vc_column_text]DevisionX is taking part in an AI-Café session, as its CEO, Mahmoud Abdelaziz, will be discussing the ‘Effect of No-Code/Low-Code AutoML Solution on The AI Computer Vision Industry’. This session is funded by AI4Media, and will take place ONLINE, on Wednesday, the 25th of October, from 2:00 to 3:00 PM CEST. Register here: https://t.co/migAWRY1O1 The invitation: https://t.co/kO9iFS8141

Join our founder, @mahmoudaziz, as he discusses the 'Effect of #No-Code/#Low-Code #AutoML Solution On The #AI Computer Vision Industry’ on Wednesday, 25th October. Funded by @ai4mediaproject. Register here. The Invitation: https://t.co/kO9iFS7tet pic.twitter.com/POojLghACm — DevisionX (@devisionx) October 19, 2023

Update:

Here is the video recording of the AI-Café session:

Explore Tuba’s various output Machine Learning models on HuggingFace. Sign-up to Tuba.ai now and embark on the journey it paves for you. Stay tuned for more interesting read by following us on Medium. You can also follow us on: Twitter, Facebook, LinkedIn.