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What are the Infrastructure Requirements for Artificial Intelligence?

Introduction

With the demand for Artificial Intelligence powered solutions growing by the day, so does the need for companies to understand the right technology and infrastructure required to apply these solutions. The AI pipeline consists of several steps from data collection and preparation, model creation, model training and evaluation, and finally inference. Whether the solution is implemented on-premise or in the cloud, the above steps have to be accomplished for you to deliver a working AI product. Due to the nature of AI, requiring a lot of parallel computation both during training and inference, the hardware infrastructure selected needs to meet the minimum hardware and software requirements for effective and efficient delivery. Let us expound on the requirements for some of the most common stages of the AI pipeline along with infrastructural requirements for some specific implementation case studies.

Infrastructural requirements for artificial intelligence

Requirements for Data Storage:

AI especially in the form of deep neural networks is notorious for requiring huge amounts of data for the model to attain a decent level of accuracy. An infrastructure that could be used to store the model data is highly dependent on the size of the dataset. For a small amount of data, a standard hard disk or SSD of any size would be okay as long as it is capable of storing the data without consuming much of the storage space. However, for an application such as retail analytics, any cloud storage service provider and analytics service such as Big Query could be the most suitable option since the data generated from your clients could be in large volumes. Cloud storage solutions have pricing plans that scale according to your storage requirements. Cloud Storage is not only suitable for storing AI training data, but some solutions enable synchronization so that your data is always up to date. Note that the choice of data storage on the cloud is dependent on the nature of the data. If you have data in the form of images, then any cloud storage would be suitable, but if the data is in tabular forms, such as time-series data, then you could explore solutions such as Big Query, that offer analytics capabilities.

Requirements for Training:

AI training as highlighted in the beginning is a highly computationally intensive task. The infrastructure used for training has to be able to achieve parallel computation at a decent scale. Commonly, Graphics Processing Units are used to be able to achieve this. Specifically, NVIDIA graphics cards are the most popular GPUs used and are compatible with popular machine learning libraries like Tensorflow. However, Central Processing Units, CPUs are applicable for training in some cases. Recently Tensor Processing Units (TPUs) a product of Google, have also emerged as an option for training models on Google Cloud. Training can be performed on the premise if you can be able to assemble a custom machine, with a decent amount of RAM and CPU, as well as GPUs. However, there are many IaaS providers such as Microsoft Azure and Google cloud that offer options that could enable you to assemble machines with all your requirements allowing you to specify RAM, CPU cores, GPUs, and also TPUs. For experimentation, Google collab provides a free GPU, TPU, and CPU in a python Jupyter Notebook environment. This is useful as it can enable you to prototype models and train them freely, of course under certain conditions but it is a great way to start exploring the use of these devices before purchasing or renting them from an IaaS provider.

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Requirements for Inference:

After training and evaluation of your AI model, it is time to launch it. Depending on your use case, GPUs are the most robust devices that can achieve this. Whether it’s in retail analytics, mobile, or IoT, GPUs are the most suitable hardware to launch these AI models. For edge deployments, edge TPUs are also available to enable inference at a small scale for IoT devices. Other IoT devices that can be used for AI inference at the edge include the NVIDIA Jetson nano. If your AI solution is implemented on mobile the standard mobile GPU can provide an inference boost and can be applied in parallel with the CPU, which you can unload common non AI tasks on, to give your product a performance boost. For large-scale AI inference applications such as retail analytics, or product recommendation system, cloud service providers offer tailored Machine Learning as a Service, (MLaaS) solutions. For example, Google Cloud has a Recommendations AI product that enables you to offer product recommendations to your clients for some e-commerce platforms. Microsoft Azure machine learning, Amazon Sage maker, IBM Watson, Amazon Machine Learning for predictive ML, are just some of the other Cloud Services that enable tailored machine learning inference at scale, without you having to worry about the infrastructural requirements.

Requirements for Data preparation:

Data preparation for AI tasks such as Lidar data labeling, also require a decent GPU. Additionally, they require a device with a decent amount of RAM preferably 8 Gigabytes or more. A large amount of RAM is required for the data that needs to be loaded in memory, for it to be processed, and it is commonly done in batches.

Networking infrastructure:

AI systems whether implemented on edge or cloud, need to send and receive data. Since AI depends on large amounts of data, the networking infrastructure selected needs to handle a large bandwidth effectively, and also have low latency. In some use cases such as computer vision networking infrastructure also needs to facilitate real-time data transmission.

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Conclusion

There are several steps involved in the creation and application of AI models. Through each of these steps, some infrastructural requirements need to be met for you to launch your AI product or service. Before you determine the infrastructure, you should perform System Analysis effectively and beware of the costs for each requirement. AI application can end up being a costly affair if the wrong tools are selected for the job. Application of AI on the premise is much more affordable for a small business but much more difficult to scale the infrastructure, however, the Cloud services scale much better but could end up being costly, if not well applied. Due to the complexity involved in setting up an AI system, you could find a software company, such as a Custom Software Development Company in Dallas Texas, to guide you through selecting the infrastructural requirements, and determine the best way for you to implement your AI solution

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