COO of B2BROKER, Evgeniya Mykulyak answer the questions.

COO of B2BROKER, Evgeniya Mykulyak answer the questions.<iframe loading="lazy" src="https://www.youtube.com/embed/9TAxwQgD-dM" width="560" height="315" frameborder="0" allowfullscreen="allowfullscreen"></iframe>

Full B2BROKER Interview with Dukascopy TV

이 사이트를 사용함으로써 귀하는 당사의 

연락처 양식

업계를 위한 확장 가능한 솔루션, 첨단 기술, 그리고 탁월한 지원 제공

당사의 거래 상품 기술 사양과 단계별 마진 요구사항을 확인해보세요.

다양한 연결 옵션을 제공합니다. 자세히 알아보세요.

비교

강력한 기술력, 빠른 주문 체결, 24시간 지원, 경쟁력 있는 가격으로 최상급 외환 유동성을 제공합니다.

경쟁력 있는 가격과 좁은 스프레드로 롱/숏 포지션에 모두 적합한 금속 현물 유동성을 제공합니다.

글로벌 지수 유동성을 통해 다양한 포지션 구성, 최적의 가격, 깊은 시장 유동성을 확보하세요.

석유, 가스, 대체 에너지 등 다양한 자산군에 대한 거래 솔루션 제공. 실시간 데이터와 분석 기능 포함.

거래소, 비은행 LP, 브로커 등 다양한 출처의 유동성을 결합한 암호화폐 CFD 솔루션을 확인해보세요.

단일 계정으로 전 세계 주식시장에 접근하고 개별 주식 CFD를 통해 거래하세요.

유럽, 아시아, 미국 주요 거래소의 ETF를 통한 분산 투자 기회를 제공합니다.

시장 변동성에 유연하게 대응할 수 있는 다양한 결제일 및 통화쌍 기반의 NDF 상품을 제공합니다.

외환/CFD 마진

유용한 링크

Header | Liquidity new 2024-10-08

유동성

외환 CRM으로 브로커리지 최적화: 운영, 고객 관계, 분석, 리스크 관리를 간소화하세요.

B2CORE

초보 트레이더가 전문가 전략을 모방할 수 있도록 지원합니다. 7일 안에 전폭적인 지원으로 카피 트레이딩 플랫폼을 시작하세요.

PAMM 플랫폼을 통해 트레이더가 투자 펀드를 조성하고 브로커리지 내에서 거래량 또는 수익으로 수익을 올릴 수 있습니다.

고급 MAM 플랫폼으로 다중 계좌 관리, 거래 할당, 블록 주문 체결을 간소화하세요.

B2COPY

B2CONNECT으로 빠른 주문 라우팅, 탁월한 호가 매칭, 유동성 집계 등의 경쟁력을 확보하세요.

B2CONNECT

신속하고 효율적으로 독점 거래 운영을 설정할 수 있는 턴키 솔루션

B2PROP

다중 시장 및 다중 자산 거래를 지원하는 궁극의 플랫폼으로, 귀사의 FOREX 및 암호화폐 브로커리지 비즈니스를 운영할 수 있습니다.

B2TRADER

Header | Products new 2024-10-08

기술

통합 브로커 인프라를 갖춘 cTrader 화이트라벨로 고객에게 프리미엄 거래 경험을 제공하세요.

Traders First™ 철학에 부합하는 고급 서비스를 제공하며, 신뢰할 수 있고 투명한 거래 환경을 트레이더에게 제공합니다.

외환 및 암호화폐 브로커리지를 위한 멀티마켓, 멀티자산 거래 플랫폼입니다.

주요 거래 플랫폼을 나란히 비교하고, 귀사의 니즈에 가장 적합한 솔루션을 선택하세요.

WL 플랫폼

Header | WhiteLabel new 2024-10-08

화이트라벨

저희의 턴키 솔루션을 통해 한 달 이내에 귀하만의 암호화폐 거래소를 구축하세요 — 고성능, 보안성, 사용자 친화적 인터페이스 제공.

안정적인 기술, 24시간 지원, 백오피스 관리 도구가 포함된 턴키 솔루션으로 외환 브로커 비즈니스를 빠르게 시작할 수 있습니다.

당사의 브로커 소프트웨어를 통해 디지털 자산 시장에 진입하세요. 시간, 비용, 리소스를 모두 절감할 수 있습니다.

유동성 공급 비즈니스를 즉시 시작할 수 있는 통합형 턴키 솔루션을 제공합니다.

턴키 솔루션

Traders First™ 철학에 기반한 프리미엄 서비스를 제공합니다. 이 플랫폼은 신뢰성과 투명성을 갖춘 거래 경험을 트레이더에게 제공합니다.

프랍 트레이딩 솔루션

Header | Solutions new_2024-10-08

솔루션

저희 솔루션을 통해 MT5* 서버를 손쉽게 설치 및 구성하여 CFD 및 외환 거래에 집중하실 수 있습니다.

MT4* 서버의 구성 및 운영을 최적화하여, 고객님은 CFD 및 외환 비즈니스에 전념하실 수 있습니다.

저희 전문팀이 cTrader 서버를 빠르고 안정적으로 구성해 드리며, 브로커리지 비즈니스에 집중하실 수 있습니다.

OneZero의 연결성과 설정을 최적화하여 브로커 비즈니스 운영의 효율성을 높여 드립니다.

Prime XM의 구축, 유지보수 및 최적화를 전문팀이 전폭적으로 지원해 드립니다.

트레이딩 플랫폼 서버 서비스

Header | Services new 2024-10-08

서비스

미디어

Header | Explore new 2024-10-08

블로그

다년간의 업계 경험을 바탕으로, 우리는 혁신적인 솔루션과 최적화된 고객 서비스를 제공합니다.

다양한 인재로 구성된 팀에 합류하여 폭넓은 채용 기회, 인턴십, 경력 개발 리소스를 경험해 보세요.

우리는 최상위 기술 유니콘 기업 및 개발사들과 협력하여 혁신적인 브로커리지 솔루션을 제공합니다.

저희 제휴 프로그램에 참여하여 고객 소개를 통해 수수료를 받고, 다양한 이벤트에 참석하고, 네트워크를 확장해 보세요.

회사 소개

Header | Company new Company 2024-10-08

회사

Dubai's digital shift: Gov services to accept Dubai crypto payments. See how this changes transactions.

Dubai is taking another clear step into its digital future. The Department of Finance in Dubai Government (DOF) has just 

 a significant team-up with global crypto platform Crypto.com. This new partnership will soon allow people to pay for government services using various cryptocurrencies.

This agreement, officially a Memorandum of Understanding (MoU), sends a strong signal about Dubai's forward-thinking approach to digital money. It’s also a key part of the government's wider 

Top officials from The DOF and Mohammed Al Hakim, who heads crypto.com in the UAE, put pen to paper on the deal. The signing took place during the recent Dubai FinTech Summit, a major event hosted at the Dubai International Financial Centre.

This link-up between the DOF and Crypto.com is all about 

. The idea is to roll out a new digital payment option on its official platforms, making transactions quicker and more efficient for everyone.

At its heart, this initiative is designed to give the Dubai Cashless Strategy a serious boost. This government-led plan aims to dramatically cut down on the use of physical cash. A key target is to have 90% of all payments in Dubai, both public and private, 

By bringing Dubai crypto payments into the mix, the government expects to see even more economic activity. The overall Cashless Strategy is already tipped to add at least AED 8 billion to Dubai's economy each year, thanks largely to new fintech services.

His Excellency Abdulla Mohammed Al Basti, Secretary General of The Executive Council of Dubai, called the move proactive. He noted that using secure crypto solutions shows Dubai is thinking ahead and adapting to global economic changes. This, he added, also lines up with the goals of the Dubai Economic Agenda D33.

Once all the technical details are ironed out and the systems are ready, this new payment method will go live. Both regular customers and businesses dealing with government departments will be able to pay their service fees using their Crypto.com digital wallets.

Crypto.com will handle the backend, securely changing the crypto payments into Emirati Dirhams (AED). This cash will then be sent to Dubai Finance's accounts. The whole process is designed to be smooth, safe, and a step forward for payment systems.

His Excellency Abdulrahman Saleh Al Saleh, who leads the DOF, spoke about the department's significant role in this shift. He stressed that working with Crypto.com fits perfectly with the vision of His Highness Sheikh Mohammed bin Rashid Al Maktoum, Vice President and Prime Minister of the UAE and Ruler of Dubai, to make Dubai a world leader in 

Echoing this, Eric Anziani, President and COO of Crypto.com, praised Dubai's forward-looking plans for a cashless society. He noted that Crypto.com is excited to help make this happen, describing it as potentially the first time a government has implemented digital payments so broadly.

This latest news isn't a surprise for those watching Dubai. The emirate has been working hard to position itself as a 

, and that includes the crypto world. Dubai has been creating rules and an environment that welcomes virtual asset companies.

Crypto.com itself got its first license to offer regulated crypto services in Dubai back in 2023. Just recently, the company shared that Dubai’s virtual asset watchdog also gave it a limited green light to offer derivatives.

While this announcement is big news, some of the finer points are still to come. For example, it hasn’t been detailed exactly which 

 for these government payments. Also, the specific list of government services that will accept crypto is still awaited.

Getting the technology right will be key to a smooth launch. The real test for this Dubai crypto payments system will be how easy it is to use, how secure it feels, and how well Crypto.com's platform works with the government's existing payment setups.

This partnership between the DOF and Crypto.com is undoubtedly a bold step. It clearly shows Dubai's dedication to financial innovation and its drive to be at the forefront of new tech. It’s another move to make government services more efficient and cater to a population that’s increasingly comfortable with digital solutions.

 This article is for informational purposes only. It is not finance advice and should not be relied upon for investment decisions. Always do your own research and consult a financial advisor before investing.

Dubai to Accept Crypto for Government Fees, Pushing Cashless Ambitions

알렉산더 시시카노프는 암호화폐 및 핀테크 산업에서 몇 년간의 경험을 쌓았으며, 블록체인 기술 탐구에 열정을 가지고 있습니다. 알렉산더는 암호화폐, 핀테크 솔루션, 거래 전략, 블록체인 개발 등과 같은 주제에 대해 글을 씁니다. 그의 사명은 이 새로운 기술이 어떻게 안전하고 효율적이며 투명한 금융 시스템을 만드는 데 사용될 수 있는지에 대해 개인들을 교육하는 것입니다.

알렉산더 시슈카노프는 암호화폐 및 핀테크 산업에서 몇 년 간의 경험을 가지고 있으며, 블록체인 기술 탐구에 열정을 가지고 있습니다.

Alexander Shishkanov 

AI as a Service (AIaaS) delivers cloud-based AI tools for businesses. Learn how AIaaS works, top applications, and the future of AI-driven automation.

Artificial Intelligence (AI) reshapes industries, but building AI from scratch can be costly and complex. AI as a Service (AIaaS) is a 

 that allows businesses to access AI-powered tools without investing in infrastructure or expertise. Just as SaaS revolutionised software delivery, AIaaS makes AI accessible, scalable, and cost-effective for companies of all sizes.

From predictive analytics and NLP to generative AI as a service, AIaaS enables seamless AI integration into workflows, automating tasks, enhancing decision-making, and driving innovation. 

This guide explores how AIaaS works, its major types, key AIaaS applications, and future trends shaping AI automation.

AI as a Service (AIaaS) refers to cloud-based artificial intelligence services that allow businesses and individuals to access AI-powered tools and functionalities without needing to develop, maintain, or host their own 

. AIaaS enables users to leverage AI capabilities on demand without requiring deep technical expertise in AI development.

 provides access to software applications via the cloud, AIaaS delivers AI functionalities — such as machine learning, natural language processing, computer vision, and predictive analytics — through cloud-based platforms. This model helps companies integrate AI into their workflows efficiently, cost-effectively, and with minimal technical complexity.

AIaaS is growing rapidly, with the global market expected 

 as businesses increasingly adopt AI-powered automation.

AI as a Service (AIaaS) allows businesses to access artificial intelligence capabilities via cloud-based platforms without investing in infrastructure or AI expertise.

Operating on cloud computing, AIaaS provides AI functionalities on demand, allowing businesses to scale resources and integrate AI models seamlessly into their workflows. 

The following sections explore the key components of AIaaS, including infrastructure, accessibility, training, deployment, pricing, and continuous improvements.

AIaaS platforms operate on cloud-based infrastructure to deliver high-performance AI capabilities without requiring businesses to invest in in-house hardware or AI teams. These platforms manage and optimise AI models on distributed cloud servers, ensuring seamless scalability.

AI processing requires significant computational resources, which AIaaS providers handle using advanced hardware such as GPUs (Graphics Processing Units) and TPUs (Tensor Processing Units). These specialised components enhance AI model training and inference efficiency, enabling the rapid processing of large datasets and complex computations.

AI models are hosted in the cloud and are continuously optimised to enhance accuracy, reduce latency, and improve real-time decision-making. Regular refinements ensure businesses can always access the most advanced AI capabilities without requiring manual intervention.

AI models rely on vast amounts of structured and unstructured data. AIaaS platforms provide secure cloud storage and high-speed processing solutions, allowing businesses to efficiently analyse and manage extensive datasets for machine learning, automation, and analytics.

Accessing AIaaS: APIs, SDKs, and Web Interfaces

AIaaS solutions are designed to be accessible through multiple interfaces, allowing businesses to integrate AI functionalities differently.

 enable businesses to integrate AI-powered capabilities into their applications without requiring in-depth AI expertise. Data is sent to cloud-based AI models, which return processed insights or results in real time.

 provide developers with pre-built AI tools and libraries, making integration into applications seamless. These kits offer ready-made functionalities to facilitate speech recognition, natural language processing, and predictive analytics.

For businesses without in-house AI expertise, web-based platforms offer intuitive interfaces that allow users to train, test, and deploy AI models with minimal technical knowledge. These platforms support AI-driven automation, analytics, and decision-making.

AIaaS platforms offer two primary approaches for businesses: pre-trained AI models for immediate deployment and custom AI training for specialised applications.

These AI models come pre-built for common applications such as 

, sentiment analysis, and recommendation systems. They require minimal setup and can be integrated directly into business workflows.

For businesses with unique datasets or specific needs, AIaaS allows the customisation of AI models. Companies can train models using proprietary data, fine-tuning them for industry-specific use cases. This approach ensures AI solutions are optimised for particular business requirements.

Once an AI model is developed or selected, it must be deployed and integrated into business operations. AIaaS platforms offer multiple deployment methods based on business needs.

AI models run entirely on cloud servers, minimising infrastructure management. Businesses access AI capabilities via cloud-based interfaces, ensuring flexibility and scalability.

AI models can be optimised for local devices such as smartphones, 

, and on-premise systems. This approach reduces latency and enhances real-time processing by limiting dependence on cloud-based computing.

Businesses can use a combination of cloud-based AI and local processing to balance performance and cost-efficiency. 

 can be leveraged for periodic updates and large-scale model management, while local processing can handle real-time interactions.

AIaaS platforms offer flexible pricing structures, allowing businesses to pay based on usage or subscribe to ongoing AI services.

This model allows businesses to pay only for their AI services, making it a cost-effective choice for startups or companies with fluctuating AI requirements.

For businesses that require consistent AI functionalities, subscription plans provide access to AI services for a fixed monthly or annual fee, ensuring predictable costs.

Some AIaaS providers offer free-tier access to basic AI functionalities, allowing businesses to experiment with AI tools before committing to full-scale deployments. This is particularly useful for startups and developers exploring AI integration.

Continuous AI Model Updates and Enhancements

AIaaS providers regularly improve AI models to ensure businesses benefit from the latest AI-driven automation, analytics, and decision-making advancements.

AI models continuously evolve with new data inputs, improving accuracy and adaptability to changing market conditions. Regular updates enhance performance and enable AI to remain relevant over time.

Cloud-based AI platforms apply regular security patches and compliance updates to safeguard AI models against vulnerabilities. These platforms adhere to global data protection regulations, ensuring secure AI operations.

AI models learn from real-world interactions and user feedback, enabling continuous refinement. This iterative improvement enhances 

, fraud detection, recommendation engines, and other business applications.

AIaaS solutions can be categorised into different types, each serving distinct business needs such as machine learning, natural language processing, computer vision, predictive analytics, and automation.

Below is a detailed explanation of the major types of AIaaS solutions, their use cases, and real-world applications.

Machine Learning as a Service (MLaaS) provides businesses with cloud-based machine learning tools for data analysis, automation, and decision-making. Instead of developing ML models from scratch, companies can use pre-trained models or train their own using AIaaS platforms. These services simplify ML adoption by offering user-friendly interfaces, drag-and-drop tools, and scalable infrastructure.

MLaaS works by providing AI-powered models for various applications such as fraud detection, recommendation engines, and predictive analytics. Businesses can use pre-trained models or upload their own data to refine them for specific needs. This allows even non-technical users to leverage machine learning without deep expertise.

, where banks use AI models for risk assessment and fraud detection. 

 and predictive analytics, while e-commerce companies rely on AI-driven recommendation systems. 

For instance, Amazon SageMaker encourages businesses to build, train, and deploy ML models seamlessly, helping financial institutions detect fraudulent transactions in real-time.

Natural Language Processing as a Service (NLPaaS)

NLPaaS empowers businesses to analyse and process text and speech data using AI. This includes sentiment analysis, speech-to-text conversion, language translation, and chatbot automation. By integrating NLPaaS, companies can extract valuable insights from text-based data and improve customer communication.

NLPaaS operates through AI-powered models that process and interpret human language. Businesses can integrate speech recognition, real-time translation, and chatbot functionalities into their applications to enhance user experiences. Companies in customer service use 

 to handle inquiries, while marketers rely on sentiment analysis tools to monitor brand reputation.

For example, IBM Watson NLP offers AI-powered language processing tools that help businesses analyse customer feedback, detect emotions, and automate multilingual support. A global e-commerce platform can automatically leverage NLPaaS to translate customer inquiries, ensuring seamless communication across different markets.

CVaaS simplifies AI-powered image and video analysis, allowing businesses to implement facial recognition, object detection, and image classification into their applications. Security, healthcare, and retail industries utilise CVaaS for automated visual processing.

CVaaS solutions analyse images and videos to detect patterns, objects, and movements. Businesses use AI-driven facial recognition for authentication, while e-commerce companies implement image-based searches for product discovery. Healthcare providers use AI-assisted diagnostics to analyse medical images more accurately.

One example is Microsoft Azure Computer Vision API, which provides businesses with AI-powered image recognition and tagging. A security company can integrate CVaaS into its surveillance system to detect unauthorised access and monitor suspicious activity in real-time.

Predictive Analytics as a Service enables businesses to use AI-driven insights to forecast trends, detect anomalies, and optimise decision-making. This service is widely used in finance, marketing, healthcare, and logistics to improve efficiency and risk management.

AI models analyse historical data to identify patterns and make accurate predictions. Businesses can use predictive analytics for credit risk assessment, demand forecasting, and customer retention strategies. AI-powered analytics tools help organisations make data-driven decisions with real-time insights.

For example, Google Cloud AutoML Tables allows companies to create AI-powered forecasting models. A logistics company can leverage predictive analytics to optimise delivery routes, reduce fuel costs, and anticipate supply chain disruptions.

AI-powered chatbots and virtual assistants automate customer interactions, provide instant support, and enhance user experiences. Businesses use AI chatbots to handle inquiries, schedule appointments, and assist with transactions, reducing the need for human intervention.

These chatbots process user inputs using NLP models and respond in real-time. Virtual assistants continuously improve by learning from past interactions and providing more accurate and personalised responses. Companies integrate AI chatbots into websites, mobile apps, and messaging platforms to offer 24/7 customer service.

Google Dialogflow powers AI-driven conversational agents that businesses use to automate customer support. For example, a telecommunications company can implement an AI chatbot to assist customers with billing inquiries, reducing wait times and improving service efficiency.

Robotic Process Automation (RPA) with AI helps businesses automate repetitive and rule-based tasks, improving operational efficiency and reducing human errors. It is widely used in 

, HR, customer service, and supply chain management.

AI-powered RPA automates data entry, invoice processing, and document management tasks. Businesses can integrate AI-driven bots with existing software to streamline workflows, saving time and resources. Intelligent automation enhances decision-making by allowing AI to adapt to new data patterns and exceptions.

For example, UiPath’s AI-powered RPA solutions help companies automate financial document processing. A corporate finance department can use RPA to approve invoices, detect inconsistencies, and minimise errors, improving overall productivity.

As artificial intelligence continues to evolve, AI as a Service (AIaaS) is expected to experience significant advancements, making AI more accessible, scalable, and intelligent across industries. The future of AIaaS will be shaped by technological breakthroughs, automation, security improvements, and expanded industry applications. 

Below are the key trends and innovations defining the next phase of AIaaS:

AI-Driven Automation and Autonomous AIaaS

AIaaS platforms are transitioning towards self-learning AI models capable of automating workflows without human intervention. Future AI systems will execute tasks and optimise their performance, reducing the need for manual retraining. 

Automated Machine Learning (AutoML) will streamline AI model development, allowing businesses to implement AI solutions with minimal expertise. Additionally, autonomous AI agents will take over complex decision-making, analysing real-time results and refining strategies independently.

AIaaS is shifting away from cloud-only models to edge computing-based AI deployments, allowing AI processing to occur closer to the data source. This approach particularly benefits IoT devices, autonomous vehicles, and real-time security systems, where speed and efficiency are critical.

To facilitate this shift, AIaaS providers will optimise AI models for local execution on smartphones, industrial sensors, and embedded systems. These compressed AI models will operate with minimal power consumption while maintaining high processing capabilities.

Additionally, businesses will deploy hybrid AI systems that balance cloud computing for large-scale data processing and edge AI for instant decision-making.

AI increasingly merges with blockchain technology and DeFi, enhancing financial transactions' security, automation, and transparency. One major innovation is AI-powered 

, which will become more adaptive and self-executing, responding dynamically to real-time conditions.

Another significant application will be fraud detection and risk analysis in cryptocurrency transactions, where AI models will identify fraudulent activities through pattern recognition and anomaly detection. 

AIaaS will also support decentralised AI models, assuring that data privacy and security are preserved while enabling AI-driven analytics.

AIaaS in Explainable AI (XAI) and Ethical AI

As AI adoption expands, transparency and fairness are becoming key concerns. AIaaS providers now focus on 

 (XAI), ensuring that AI-driven decisions are interpretable, accountable, and bias-free.

Future AIaaS platforms will provide interpreter tools that explain how AI models reach decisions, improving user trust and regulatory compliance. In addition, bias detection frameworks will help businesses identify and correct unfair AI predictions, ensuring AI systems are free from gender, racial, and economic biases.

Quantum AIaaS – The Intersection of Quantum Computing and AI

Quantum computing is set to revolutionise AIaaS by exponentially improving AI's ability to process large datasets, optimise algorithms, and enhance encryption. Future AIaaS platforms will integrate quantum-powered AI model training, enabling machine learning systems to analyse complex datasets at unprecedented speeds.

Quantum cryptography will also enhance AI security, allowing AI-driven communications to be quantum-encrypted, making them virtually unbreakable. 

Additionally, AIaaS platforms will enable AI-driven quantum simulations, helping industries like drug discovery, climate modelling, and financial analytics leverage quantum computing for advanced research.

AIaaS for Hyper-Personalisation and Adaptive AI

AIaaS is evolving towards adaptive AI systems capable of dynamically adjusting AI-driven recommendations, services, and interactions based on real-time user behaviour. AI-powered personal assistants will become more intuitive and capable of understanding user emotions, context, and preferences to provide human-like interactions.

Future AIaaS platforms will also support self-learning personalisation systems, where AI models adjust recommendations based on continuous feedback. This will significantly improve customer engagement, e-commerce suggestions, and digital marketing by delivering tailored, context-aware experiences.

Adaptive AI will also play a key role in content creation, education, and entertainment, enabling AI-driven systems to generate dynamic content, personalise learning experiences, and provide real-time adjustments based on user interactions.

AIaaS for Cybersecurity and Threat Detection

With cybersecurity threats on the rise, AI-driven security solutions are becoming essential for protecting digital infrastructures. AIaaS will be critical in cyber defence, enabling real-time threat detection and automated responses to security breaches.

AI-powered intrusion detection systems will instantly identify and mitigate 

, while automated security response mechanisms will neutralise cyberattacks before they cause damage. 

AIaaS will also introduce self-healing security systems, where AI continuously detects vulnerabilities, applies security patches, and strengthens defence mechanisms without human intervention.

AI as a Service (AIaaS) is revolutionising AI adoption by offering scalable, cost-effective, and easy-to-integrate AI solutions. From cloud-based automation to edge AI and blockchain-driven AI marketplaces, the future of AIaaS is brighter, faster, and more accessible than ever.

As AIaaS evolves, businesses embracing AI-powered automation will gain a strategic edge, leveraging AI-driven decision-making, predictive insights, and adaptive AI solutions. 

Whether you're exploring AIaaS companies, business models, or real-world AIaaS case studies, now is the time to consider AI-as-a-Service for your needs.

What is AI as a Service (AIaaS)?

What are some AIaaS examples?

What is the AI as a Service business model?

How do AIaaS companies improve AI models?

How is generative AI used in AIaaS?

AI as a Service (AIaaS) — What Is It And How Does It Work?

근면하고 목표 지향적이며 다재다능한 사람으로서, 저는 맡은 일 각각에 항상 질 좋은 작업을 하기 위해 노력합니다. 삶의 도전 과제에 직면했을 때, 합리적이고 창의적으로 사고하여 문제를 해결하는 습관을 키웠으며, 이는 저를 인간적으로 또 전문가로서 성장시키는 데 도움을 줍니다.