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New ways to cost-effective machine learning
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New ways to cost-effective machine learning

Machine learning is a costly industry right now. Find out what opportunities there are to make such a business more cost-effective with tokenization. Machine learning is one of the most innovative niches right now. ML has long gone beyond scientific laboratories. Today it is used in a large number of areas, from medicine to photo […]

Machine learning is a costly industry right now. Find out what opportunities there are to make such a business more cost-effective with tokenization.

Machine learning is one of the most innovative niches right now. ML has long gone beyond scientific laboratories. Today it is used in a large number of areas, from medicine to photo and video processing. The machine learning market size is $15.44 billion and is expected to rise to $ 209.91 billion by 2029. However, many companies that work in the field of machine learning face many challenges. In this article, we’ll talk about ways to cost-effective machine learning and how tokenization can help.

Challenges machine learning projects face 

One of machine learning companies’ main problems is the high equipment cost. Machine learning requires the purchase of a large amount of equipment, including the most powerful video cards and CPUs, peripherals, and so on. Also, companies need cloud computing and datasets, and have ongoing expenses. This all means high costs for machine learning companies. In addition, there has been a shortage in the chip market in recent years due to the pandemic. Thus, companies are forced to pre-order the necessary equipment, which means they have to plan expenses in advance.

Another problem is slow implementation. Machine learning gives amazing data accuracy, but processing datasets today takes a very long time. Professional programs are slow, the data is overloaded, and it takes a very long time to achieve the desired result. And as we know, time is money. Machine learning projects need constant monitoring and maintenance to get the final result.

The third problem that directly affects the machine learning cost is the electricity cost. Using GPUs or CPUs for machine learning is a lot like using such hardware for crypto mining or 3D rendering. This means the operation of the equipment at the limit of its capabilities and huge utility bills. Businesses are constantly looking for places with low-cost electricity or green energy options for their data centers.

In general, running costs, energy, actions to reduce CO2 emissions, and time make machine learning very expensive. Have you ever wondered what the GPU machine learning cost is? For a 110 million parameter model, the cost will be up to $50,000. For a 340 million parameter model, it will cost $10,000 – $200,000. If you are doing a very complicated task and need to process a dataset with 340 million parameters, the cost of such a project can be up to $1.6 million. 

How can tokenization help with ML tasks cost reduction?

In fact, it all comes down to raising funding for companies involved in machine learning. If a company has sufficient funds, it can purchase state-of-the-art chips and use toolkits that save time and money.

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However, it is always difficult for innovative companies to attract financing, as banks and institutional investors often refuse. To receive investments from a fund or a loan from a bank, you must prove that your project is really worthy and has clear plans to become profitable. Another faster and more efficient path for many companies could be tokenization.

Tokenization is the transfer of rights to assets on the blockchain. As a result, you are actually issuing tokenized shares of the asset (in the form of tokens). To attract funding, the main tool today is an STO – security token offering. In this case, the company issues security tokens, which in many jurisdictions are considered by regulators as traditional securities. This provides a completely legal basis for relations between the issuing company and investors and also significantly reduces risks.

By launching an STO, a company can relatively quickly raise the necessary amount of money to upgrade equipment, purchase the most high-performance and cost-effective chips, or open a data center in a more favorable location regarding energy costs.

There is also a second way to use tokens for cost-effective machine learning. In parallel with security tokens, you can issue other types of tokens – for example, utility tokens, and use them as the platform’s internal currency. One such solution might be to lease your machine learning capacities to other clients or to perform specific tasks for clients for utility tokens. Implementing gamification and other custom solutions designed to increase business profitability and ensure stable earnings is also possible.

Benefits of tokenization for ML projects

  • Data storage on the blockchain. Blockchain is a decentralized registry that does not have a single control center. The blockchain cannot be hacked, which means that all assets’ and owners’ records are completely safe.
  • Tokenization complies with legal regulations in many countries. While mining and cryptocurrencies are banned or discouraged in some countries, tokenization is completely legal. Stobox is ready to help you choose the most suitable jurisdiction for holding STOs or other types of token offerings.
  • Opportunity to attract more investors. In the case of an STO, there are no strict regional restrictions. In addition, the target audience for companies conducting STOs is small private investors who are interested in innovative companies. You can raise funds from hundreds of investors around the world instead of knocking against the thresholds of investment funds and banks. It is also possible to create white lists when you collect funds during the STO only from a predetermined circle of investors.
  • Possibility of custom solutions for tokenization. As we wrote above, there are many ways to use different types of tokens for different business models. STO and security tokens are just one of the ways. Our experts will be happy to help you find the best option to improve the efficiency of your business.
  • Relatively fast launch. Preparation for STO or other types of offerings takes an average of several months and does not require global changes in the company, unlike traditional IPOs.

Summary

Machine learning is a promising technology already penetrating every sphere of human life. However, at the moment, it is a very costly area. Machine learning companies are constantly looking for solutions to make processes more cost-effective. One of the ways to ML tasks cost reduction could be tokenization. The transfer of rights to assets to the blockchain allows you to raise funds from investors, as well as develop new business models based on various types of tokens. If you are ready to learn more about tokenization and its possibilities for your business, contact our experts in any convenient way.

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