Why organizations fail in scaling Visual AI

07.09.20 08:09 PM By viso.ai
Developing a scalable Visual AI solution requires expertise with Deep Learning tools and deployment environments. Edge Computing requires the management of thousands distributed edge devices and data analysis to provide real-time insights.

Artificial Intelligence is present in many areas of our lives, providing visible improvements to the way we discover information, communicate or move from point A to point B. AI adoption is rapidly increasing not only in consumer areas such as digital assistants and self-driving vehicles, but across all industries, disrupting whole business models and creating new opportunities to generate new sources of customer value.

Focusing on Visual AI, the number of use-cases for applying AI that performs at human level or better is increasing exponentially, given the fast-paced advances in Machine Learning.

Visual AI encompasses techniques used in the image processing industry to solve a wide range of previously intractable problems by using Computer Vision and Deep Learning.

However, high innovation potential does not come without challenges. The following paragraphs briefly summarize some of the major pain points in building, deploying and managing Visual AI solutions at scale.

AI inference requires a considerable amount of processing power, especially for real-time data-intensive applications. AI solutions can be deployed in cloud environments (Amazon AWS, Google GCP, Microsoft Azure) in order to take advantage of simplified management and scalable computing assets.

Nevertheless, there are many circumstances where Cloud is not the adequate environment for deploying Artificial Intelligence.

  • What if your solution needs to run real-time and requires fast response times?
  • How to overcome the challenge if the system is mission critical and running off-grid?
  • What about the huge operating costs of analyzing big volumes of data in the cloud?
  • What about data privacy if sending and storing video material in the cloud?
Moving to the Edge
For most use-cases, deploying AI solutions on Edge devices is the only reasonable way to solve the challenge. A fine example is a farming analytics system. The system has to capture and do inference for 30 images per second per camera. For an average setup of 100 cameras, we get a volume of 259.2 million images per day. This is highly inefficient to process in the cloud. The best option for this use-case is to run inference in real-time at the Edge. Analyse the data where it is being generated! And only communicate key data points to the cloud backend for data aggregation and further analysis. Edge computing is considered to be a current key trend in IT industry.
Considering the rapid growth of AI inference capabilities in Edge hardware platforms (Intel NUC, Intel NCS, Nvidia Jetson, ARM Ethos), transferring the processing requirements from Cloud to Edge becomes a very attractive option for a wide range of businesses.
Most Organizations Fail at Scaling Visual AI

However, even with the promise of great hardware support for Edge deployments, developing a Visual AI solution remains a complex process.

In a traditional approach, several of the following building blocks may be necessary for developing your solution at scale:

  • Collecting input data specific to the problem
  • Expertise with Deep Learning tools like Tensorflow, PyTorch, Keras, Caffe, MXnet for training and evaluating Deep Learning models
  • Selecting the appropriate hardware (e.g. Intel, NVIDIA, ARM) and software platforms (e.g. Linux, Windows, Docker, Kubernetes) and optimizing DL models for the deployment environment
  • Managing deployments to thousands of distributed edge devices
  • Managing updates, data analysis and real-time insights
  • Knowledge about data privacy and security best practices

There is a high level of development risk associated with this approach. Especially when considering development time, required domain experts and difficulties in developing a scalable infrastructure.

Fortunately, there’s a finer path to make yourvision a reality, one that allows you to reduce your development costs and time to market by an order of magnitude.

Enter viso.ai and get access to our Deep Learning Tools

viso.ai is an end-to-end cloud platform built to solve AI vision use-cases, with focus on ease-of-use, high performance and scalability. Our platform is industry agnostic. It provides Deep Learning tools to build, deploy and operate deep learning applications in a low-code environment.

viso.ai provides an extensive set of features to help you at every step of your development cycle. Here are a few of our key components:

  • A visual, modular approach to build complex Visual AI solutions on the fly
  • Deploy solutions to a large number of edge devices at the click of a button
  • Benefit from many pre-existing software modules to build your own case
  • Add your own algorithms or integrate with pre-built Deep Learning Models
  • An easy-to-use visual interface for all your solutions and devices

Get started now and be the first to hear exciting news and updates from us about the public release of the viso.ai platform!