Disclaimer: The Industry Talk section features insights by crypto industry players and is not a part of the editorial content of Cryptonews.com.
Data science and blockchain/cryptocurrency seem like a match made in heaven. Data scientists have many reasons to pay attention to these new technologies, especially now that crypto and blockchain have gained more mainstream traction. Moreover, there is a ton of data waiting to be leveraged.
Crypto And Blockchain Matter To Data Scientists
Even though many people dismissed crypto as a niche industry that would never appeal to consumers, things look very different today. More specifically, there are more consumer products involving crypto and blockchain than ever before. Therefore, it creates exciting opportunities, mainly where alternative financial products and services are considered.
Contrary to what some may think, crypto and blockchain are about much more than volatile price swings. The technology provides tremendous potential for future consumer applications. However, many apps and solutions used today have issues that need to be addressed. Decentralization may not solve everything right away, but it remains worthwhile to explore the possibilities.
Moreover, the decentralized computational framework provided by blockchain can unlock many use cases. Every decentralized application is a treasure trove of data waiting to be analyzed. Again, not all DApps will succeed, but every experiment provides more knowledge for developers. Additionally, several projects have gained widespread transaction, including NFT marketplaces, decentralized exchanges, wallets, and much more.
Analyzing all of this data is tricky. Although everything is public and independently verifiable, it remains essential to analyze and leverage the available data. Data science can provide a helping hand on that front. Data transparency, combined with innovative technologies like AI, machine learning, and others, will establish a new paradigm. Various projects have already merged data science and crypto/blockchain, confirming the industry’s appeal.
Covalent
The Covalent team aims to combine big data technologies to create meaning from billions of data points across various blockchains and networks. Moreover, Covalent sources data from nodes, chains, data feeds, and sources end-users with individualized data to enhance overall investment performance. Ultimately, Covalent’s infrastructure will become decentralized to ensure its users own and operate the network.
The team’s approach can apply to various use cases, including taxes, decentralized finance, NFTs, DAOs, and more. Using the verifiable data provided by various networks can help users in various ways and provide new means of analysis, achieve real-time data, examine DAO proposals, etc.
Amazon SageMaker
The involvement of Amazon in data science becomes apparent through its SageMaker initiative. The platform builds, trains, and deploys machine learning models for various use cases through a managed infrastructure, tools, and workflows. Although it does not involve blockchain technology, developers could use the SageMaker services to explore opportunities in blockchain and crypto over the coming years. Coinbase, for example, uses Amazon SageMaker.
The downside to this approach is the reliance on a centralized provider. While Amazon has a good reputation for its AW service, giving them even more power over data may not be the best approach. The company already knows most things about its customers and nearly all brands. Its infrastructure can be a valuable ally for data science and blockchain/crypto, but centralization in this industry is counterproductive.
OmniLytics
The OmniLytics project revolves around a blockchain-based data trading marketplace for machine learning applications. Data owners can privately share their data to train a machine learning model requested by clients collectively. It essentially monetizes data without sharing the information with unnecessary third parties. Moreover, OmniLytics will keep data secure from prying eyes and weed out malicious data owners providing faulty results.
Machine learning model owners can split their model into private and public parts. The public part will be published as a smart contract, letting data owners aggregate locally trained models to update the public model and receive compensation for doing so. OmniLytics runs on the Ethereum blockchain and has countermeasures to high gas costs to provide a seamless experience. Ultimately, the boosting of model accuracy will improve as more people share their data.
Morphware
The approach by Morphware is to democratize access to computational resources for data science. More specifically, users who can accelerate the development of machine learnings can earn financial compensation by auctioning off idle computing power. Morphware’s primary user group for computational resources are video game players and others with idle capacity. Moreover, the peer-to-peer solution can prove beneficial to machine learning engineers, computer science students, and practicing data scientists.
In the current landscape, renting a GPU to perform computational tasks remains expensive. Through decentralization, MorphWare established a two-sided marketplace where remote computing power will be accessible at much cheaper rates and through a more user-oriented interface. The marketplace will soon expand the availability to 40 GPUs and aims to attract clients across automative, detection, and healthcare. Moreover, the data is growing and stable, as mining data will help companies stay in business.
Conclusion
Data science and blockchain go hand-in-hand through the power of decentralization. Democratized access to computational power and achieving real-time data streams pave the way for new and exciting developments across numerous industries. In addition, the cryptocurrency aspect can come into play for rewarding users who share their computational power, creating strong economic incentives for user participation.
