What are the infrastructure requirements for AI

Artificial Intelligence (AI) & Machine Learning (ML) have been around for a while. With the advancement of hardware, these fields have started to get their importance. Both of these require massive computing power to solve various mathematical equations during the learning stage. This means that organizations will need to consider many factors when building or enhancing an artificial intelligence infrastructure to support AI applications.

In the past, these insights were discovered using manually intensive analytic methods. With data volumes continue to grow as does the complexity of data, such manual analytical methods are no longer feasible. AI/ML are the latest tools for data scientists, enabling them to refine the data into value faster.

Designing an infrastructure that can be used for AI is not a trivial issue. The company’s success with AI/ML will depend on how suitable its infrastructure is such an application. While the cloud is emerging as a major resource for data-intensive AI workloads, enterprises still rely on their on-premises IT environments for these projects.

The Rise of AI/ML

Organizations across all verticals are making significant investments in AI/ML solutions. AI/ML is going to change everything within the organization at the macro as well as micro levels. This will impact the business strategies of each organization, which will create disruptions across all various industries. With such consequential changes happening at such a brisk pace, some of the key aspects of AI/ML are worth keeping an eye on.

Businesses across are developing “data first” as strategies within their business plans and outlook. With over trillions of gigabytes of data out there, but over 90% of it has been collected within the last two years. So the success of such plans will only depend on how efficiently the business is processing their big data.

For instance, a smartphone collects a lot of unstructured data in terms of location, emails, text messages, pictures, etc. analyzing all these data can give that edge to companies like Uber, Amazon, etc. who can make real sense of such data and are able to provide better experiences to their consumers.

The disruptive and more efficient business models are already “depressing industry margins.” Early AI adopters are already moving onto the second wave of AI, which will likely keep them ahead of their competitors for some time to come. 

AI/ML across different verticals

When it comes to AI/ML there is no single solution or a single application. This means a lot of AI/ML solution providers will need to create custom solutions based on their client’s requirements.

• Healthcare – Anomaly detection to diagnose MRIs scans faster
• Automotive – Classification is used to identify objects in the roadway
• Retail – Predictions can accurately forecast future sales
• Contact center – Translation enables agents to converse with people in different languages
• ………etc…….

Regardless of the vertical AI/ML success depends on making the right infrastructure choice, which requires understanding the role of data. AI/ML success is largely based on the quality of data fed into the systems.

AI/ML Infrastructure

Getting the infrastructure for AI/ML Solutions are not trivial issues. An organization’s success with AI/ML will likely depend on how suitable its infrastructural is for such powerful applications. The cloud is emerging as a major resource for data-intensive AI workloads.

AI/ML and data storage

“bad data leads to bad inferences”

AI Industry

The organization who is moving towards AI/ML need to manage their data properly. The type of data will vary from company to company, as the objective of each organization is different. This raises a consideration as to how AI data is stored, specifically the ability to scale data storage as the volume of data grows.

As organizations prepare enterprise AI strategies and build the necessary infrastructure, storage must be a top priority. That includes ensuring the proper storage capacity, IOPS and reliability to deal with the massive data amounts required for effective AI.

Storage type or specific IOPS that an organization will need is the type and when they want to use AI/ML – for instance, if it is for real-time AI/ML processing, the requirement for storage infrastructure is very different than others.

Apart from that organizations will also need to factor in the amount of data their AI/ML will generate. As their data grows there is a need to monitor capacity and plan for expansion.

AI networking infrastructure

AI/ML learning algorithms are usually deployed on massively parallel systems, where communication with all the servers within that setup send data across to each other, making low latency and high bandwidth for such deep learning algorithm becomes a must.

It is not just in the learning phase where networks are important. When companies are processing data in real-time to provide better consumer experience, public networking also needs to be extremely fast.

That’s why scalability must be a high priority, and that will require high-bandwidth, low-latency and creative architectures.

AI/ML Computing Power

Any implementation of AI/ML infrastructure is always a combination of CPU + GPU’s. A CPU based environment can handle AI workloads, but deep learning involves multiple large data sets and deploying scalable neural network algorithms will need GPUs.

Companies rely mostly on repurposed GPUs for their AI/ML learning. A new trend where companies are taking advantage of cloud infrastructure resources, to move their deep learning to Cloud-based GPU infrastructure. Intel and Nvidia both are pushing AI Focused GPUs.

AI/ML & IoT

Rise of Internet of Things (IoT) devices complements AI/ML. IoT devices collect data from countless products, sensors, assets, locations, vehicles, etc. their job is to analyze such data. This is what get’s AI/ML & IoT together. The organization applies intelligence to such massive data inputs from IoT with the use of AI/ML.

 From an infrastructure standpoint, companies need to look at their networks, data storage, computation power, and security platforms to make sure they can effectively handle the growth of their IoT ecosystems. That includes data generated by their own devices, as well as those of their supply chain partners.

Selecting the right infrastructure partner

Not all infrastructure providers are the same. While selecting infrastructure providers it is imperative to choose a provider who understands AI/ML and is able to scale up faster than your data grows.  There are a few tips you can follow when evaluating potential partners to ensure you’re selecting the best platform possible:

Determine Your Specific Needs

Before you go shopping for a provider make a checklist of requirements that your AI/ML will require today and in the future. The future list is more important because your provider will need to grow faster than your AI/ML application. When it comes to AI/ML, as discussed earlier you will need different requirements for different aspects of your system. For instance CPU vs GPU or network speed for real-time analytics, etc. Your infrastructure provider will also need to understand your needs and give you a solution that fits your specific application.

High-performance cloud infrastructure

As mentioned earlier, AI/ML performance is highly dependent on the underlying infrastructure. For example, GPUs are needed for deep learning and CPUs for handing workloads. Underpowering the server will cause delays in learning, while overpowering wastes money. Apart from computing power, the infrastructure will need to have high-speed storage. This requires choosing a vendor that has a broad portfolio that can address any phase in the AI process.

Validated design

Infrastructure is important so is the software. Depending on the software, it can take up to a few months to tune and optimize both the software and infrastructure. Making it a necessity for the organization to choose cloud which is inherently elastic.

End-to-End Management

AI/ML implementations are different for each organization, at the same time as the requirements of the underlying data changes so does the implementation of the infrastructure. A provider who can provide & support bespoke infrastructure requirements is the provider to choose.

Network infrastructure

Network infrastructure is generally broken into two parts, public networks, and private networks. Any AI/ML infrastructure software will need low latency, high bandwidth for both public and private infrastructure.

Security

AI/ML deals with data of your clients, it can include patient records, financial information, personal data, etc. Any organization that deals with data, security becomes important – it is not just for data breaches, but also infusion of bad data (via unethical means) can lead to incorrect inferences thus resulting in wrong decisions.

Broad ecosystem

t’s crucial to use a vendor that has a broad ecosystem and can bring together all of the components of AI to deliver a full, turnkey, end-to-end solution. Choosing a vendor with a strong ecosystem provides a fast path to success.

Final Thoughts

As AI/ML technologies move into the mainstream projects which were previously run by data science specialists, it is quickly transitioning to IT professionals. Organizations should think more broadly about the infrastructure that enables AI/ML. Instead of purchasing servers, network infrastructure, and other components for specific projects, the goal should be to think more broadly about the business’s needs both today and tomorrow. Choosing a cloud solutions provider will be the only way forward. 

How is TD Web Services positions itself towards AI/ML? At TD Web Services, we have a deep understanding of various underlying AI/ML methods and how they work. There are other solutions like automation other platform-related services can be provided. At the same time, we are no directly providing you AI/ML solutions yet. At TD Web Services our current focus is on delivering fast and reliable Cloud Infrastructure which can scale as per client’s requirements.