Infrastructure challenges and opportunities for AI startups

Artificial intelligence has disrupted many industries, enabling organisations around the world to improve their workflows, create operational efficiencies and gain new insights into consumer behaviour. The early successes and unprecedented scope of AI has in turn encouraged more entrepreneurs and problem-solvers to develop new and innovative ideas, and as a result, the number of startups in the industry has boomed.

However, AI startups face numerous challenges, from limited human resources to high operating costs and compatibility issues with technology standards, all of which add to the difficulty of developing a minimum viable product. A number of these challenges revolve around technological infrastructure and systems support, but with an awareness of common problems and hidden opportunities, entrepreneurs, technology teams, founders – not to mention the AI systems themselves – can thrive.

High operating costs

Building and maintaining AI systems can be expensive. AI technologies are often ‘trained’ on very large datasets, allowing them to generalise their operations to other fields – this is known as a ‘foundation’ model. Similarly, the day-to-day operation of AI systems are also very compute-intensive, and tend to run on high-performance GPUs. İndeed, many sources estimate that an AI-powered search query requires ten times the power and compute resources than a traditional search query.

Although startups will often have generous seed funding and access to compute resources from providers, both the intensive training and the operation of AI models has a very real impact on cost. At the same time, many startups will be opex- rather than capex-driven, and cloud native by default, which means that they will incur monthly recurring costs. Although this does allow them to benefit from high performance infrastructure without incurring high up-front charges, it makes the choice of provider critical if costs are not to spiral out of control.

Data management

As we have said, training AI requires a large amount of data to build a foundational model. There are a number of different foundational model varieties on the market, each using a different approach to learning. For example, semi-supervised models use a mix of ‘labelled’ (i.e. meaningfully tagged) and ‘unlabelled’ (untagged) data, using the already-meaningful (labelled) data to train the AI and improve performance on processing the unlabelled data.

All of this data must be sourced and managed effectively, which poses a number of challenges, including:

• Data acquisition: Startups must acquire high-quality, relevant data to develop their AI solutions effectively. However, acquiring large amounts of data can be expensive and time-consuming, particularly for niche industries.
• Data storage and security: Once data is acquired, it must be stored securely and efficiently. During the training period, retaining data on high availability systems can be expensive, and if any of this data is sensitive (for example, within the healthcare or finance industries) it must be protected adequately.

Backwards compatibility issues

AI frameworks such as TensorFlow and PyTorch are continually evolving, with new versions and updates released frequently. A number of these frameworks are not backwards compatible with previous versions, which puts pressure on founders and technical teams to continually update their applications. Failure to update to the latest versions can lead to problems with functionality, user issues, or quite simply, downtime. In such a new industry, although functionality issues are to some extent expected, each glitch can erode an AI firm’s reputation, and founders may feel like they can see their potential funding pots dwindle with every error!

AI in Practice: Yepic.AI achieves breakthroughs with OVHcloud

Yepic.AI is a startup that has used the power of artificial intelligence to create innovative video and voice solutions. These solutions take text of any language, then generating ultra-realistic avatars and providing simultaneous translations. In short, with Yepic, any written content can now be turned into a narrated video of any language, without the need for talent or voice acting, removing the need for many expensive and skilled hours of labour.

However, developing and testing these algorithms requires access to powerful computing resources, which is where Yepic.AI faced significant challenges.

Initially, Yepic.AI turned to cloud services provided by American hyperscalers to meet their computing needs. However, they soon realized that these services were not sufficient to support their growing business. They needed a more reliable and efficient GPU infrastructure to generate the high-quality videos they desired.

This is when Yepic.AI turned to OVHcloud, Europe’s largest cloud provider. OVHcloud provided Yepic.AI with access to NVidia’s Tesla V100S GPU, which offers double the memory of the V100 version provided by other cloud providers. With this extra memory, Yepic.AI was able to generate ultra-realistic videos at 60 frames per second, which is essential for their business.

In addition, Yepic.AI benefited from the integration between OVHcloud AI Notebooks and OVHcloud ML Serving. This integration allowed Yepic.AI to quickly develop and test their machine learning algorithms, bringing new features to market more quickly. With the help of OVHcloud, Yepic.AI was able to reduce its operating costs while increasing productivity, a key benefit for a startup with limited resources.

Yepic.AI’s success is a testament to the importance of selecting the right hosting solutions for AI startups. By partnering with OVHcloud, Yepic.AI was able to overcome significant computing challenges and develop cutting-edge solutions that are changing the way we communicate. As AI continues to transform industries, choosing the right hosting solutions will be critical for startups looking to leverage this emerging technology.

Why choosing the right hosting solutions is critical?

Selecting the right hosting solution is very important for AI startups. For many, hosting solutions on prem is quite simply out of the question, making them cloud native by necessity. For others, cloud offers a way to avoid high capex costs and put lower, recurring opex costs on the balance sheet. Furthermore, it can help small organisations to avoid managing the security and day-to-day aspects of their infrastructure, removing much of the administration burden and helping them to focus on creating new AI solutions.

The right hosting solutions can help to create a stable, secure, and efficient AI system, and enable startups to innovate and stay competitive in the market.

Hosting solutions provide a range of benefits, including:

• Scalability: Hosting solutions allow startups to scale their infrastructure up or down as needed. This ensures that they have the resources required to develop and test AI solutions effectively. Hosting providers can offer the ability to instantly provision additional resources, allowing startups to handle unexpected spikes in demand without delays or additional costs.
• Flexibility: Hosting solutions provide startups with the flexibility to choose the resources they need. This enables them to optimize their infrastructure for their AI solutions. They can choose the type and amount of computing resources they need, such as GPUs or high-performance processors, depending on their specific requirements. They can also mix and match infrastructure, tailoring solutions to their needs without physically acquiring hardware.
• Cost-effectiveness: Hosting solutions can be more cost-effective than building and maintaining an on-prem infrastructure. This can help startups reduce operating costs and focus resources on developing their AI solutions. Hosting providers can offer various pricing models, and many offer credits for small companies, allowing startups to choose the most cost-effective solution for their specific needs.

Scaling AI innovation with cloud hosting

The AI industry today faces numerous challenges, from issues of ownership to ethics, technology and beyond. Although technology and choosing the right cloud provider cannot answer the complex philosophical questions, it can at least help startups to focus less on day-to-day infrastructure management, and more on creating innovative AI solutions. From scaling their infrastructure to optimizing their resources, the right hosting solution can help AI startups overcome the hurdles that stand between them and success, turbocharging their business and helping them to shape the future of the industry.

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• Source: https://dataconomy.com/2023/05/30/infrastructure-challenges-and-opportunities-for-ai-startups/