NashTech

AI and machine learning

We work with your company to develop the infrastructure, data culture, and technological environments to fully utilise your data assets. Doing so can open up new markets for goods and services and enable improved real-time decision-making.

With the help of our expertise in data strategy and insights, artificial intelligence, and machine learning, as well as our more than 20 years of outstanding software engineering, we collaborate with you to amplify opportunities for innovation and spot risks.

Decision science

We are passing up scalable learning by emphasising a machine-centric strategy. In the era of digital platforms, we have never been closer to our clients. Many businesses have thousands or millions of contacts with customers daily, but frequently these interactions are not designed to help customers learn new things. If done wisely, we can simultaneously learn about our customers and optimise for any company metric. These are not mutually exclusive; rather, we have tools at our disposal that enable us to learn new things while also improving our companies. We are losing out on those opportunities because of misunderstandings about artificial intelligence.

Our goal here is to present enhanced strategies that enable organisations to comprehend novel methods for the effective use of AI, particularly in the area of decision science in identifying valuable cause-and-effect relationships.

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Data science and modeling

NashTech’s data sciences solution uses data modelling and statistical methods to address particular business issues. To simplify information sharing, interpretation, and decision-making, we combine the same with the reporting and visualisation framework. Self-service is another element that allows for more end-user control. Our data sciences solution covers the entire data lifecycle, including data preparation, data enrichment, exploratory analysis, model building, and model validation, as well as integration with business processes, reporting, and visualisation.

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AI platform foundations

Our goal here is to present enhanced strategies that enable organisations to comprehend novel methods for the effective use of AI, particularly in the area of decision science in identifying valuable cause-and-effect relationships.

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Intelligent products

We specialise in optimising everything from product creation to operations to pricing and strategy. AI and machine learning may enhance a company’s strategy while enhancing the excellent work already being done there.

All the way from opportunity identification to actualisation. The best minds in strategy, design, behavioural science, machine learning, data science, and engineering are among those we hire. We think a cross-functional strategy is necessary to overcome the most difficult problems.

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MLOps

NashTech makes it easier and faster to get started with ML/AI projects and seamlessly scale them to production deployments. Taking a modern machine learning application from research and ad-hoc code to a robust and scalable platform remains a key challenge for experienced data science and engineering teams.

NashTech simplifies this process with a complete suite of tools to manage, deploy, and monitor your machine learning applications, with expertise garnered over 23 years of developing ML-enabled products. With enterprise-grade security and instant deployment to cloud-native services, taking your machine-learning application from prototype to production has always been challenging.

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Frequently asked questions

Machine learning (ML) solutions are a comprehensive suite of intellectual property, cutting-edge tools, and advanced software that foster innovation and growth in the realm of AI.
The key components of AI solutions are Machine Learning, Natural Language Processing, Computer Vision, Robotics, and Expert Systems. These components empower machines to achieve unprecedented levels of learning, comprehension, and interaction with their surroundings

The most prominent examples of AI and machine learning are Siri, chatbots for customer support, expert systems, online gaming, and intelligent humanoid robots. Machine learning is widely used in online recommender systems, Google search algorithms, and Facebook’s auto friend tagging suggestions. Take a look how NashTech built an AI platform to find and analyse content across traditional data silos to drive new insights for Elsevier in our case study: Elsevier Case Study.

MLOps is different from DevOps as it places a strong emphasis on efficient data management and model versioning, while DevOps prioritises overall application performance and reliability. MLOps specifically focuses on optimising model performance in production environments and implementing robust monitoring strategies. Additionally, it involves critical tasks such as automated testing and deployment, ensuring seamless integration between data science and software engineering workflows.

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