AWS AI/AZURE AI
Use the power of AWS AI services and Azure AI services to lower the technical threshold, development costs, and decrease go-to-market time for your AI-based product.
AWS, Amazon Web Services and Microsoft Azure are cloud computing services meant for developing, testing and deploying applications. By providing pre-trained AI modules and emulating most of the attributes of real computers, on-demand cloud computing platforms assist the development of Artificial Intelligence solutions in business. AI solutions aid an array of industries where the capabilities of cloud computing services drive business growth and transformation.
Microsoft’s Azure AI is a three-pillar solution: Azure Cognitive Services and Bot Service, offering pre-built models, Azure Cognitive Search and Form Recognizer and the most advanced custom AI dedicated to Machine Learning - Azure Databricks, Azure Machine Learning and Azure AI Infrastructure. Azure Cognitive Services powers most of the AI’s in Microsoft’s products - Xbox, Bing and Teams to name a few and is used by companies such as Toyota, Tetra Pak and ASUS.
AWS offers an array of tools: Amazon Personalize, Amazon Comprehend, Amazon Rekognition and other pre-trained AI Services. AWS AI services provide ready-made AI to streamline business processes and address various challenges in applications and workflows. Among companies benefitting from AWS AI solutions are enterprises such as Netflix, Siemens and PwC.
Artificial intelligence confers various advantages from increasing operational efficiency, boosting productivity, supporting faster decision-making, as well as automating and optimizing mundane tasks.
Cloud-based platforms come integrated with data science virtual machines, tools for learning, modelling, analysis and development of data-based projects. Adopting data analytics and building predictive models leads to a better understanding of customer behaviour, improving customer retention, boosting cross-selling and reducing risk due to generating credit scores. Using pre-build elements from cloud platforms of AWS AI and Azure AI puts the right tools in enterprise owners’ hands in the run to the top of their industries.
Computer vision algorithms serve several roles, aiming at accurately recognizing visual content. Inseparably bound to data analysis, applications support making better business decisions. Computer vision allows object detection, image classification and segmentation, as well as face and optical character recognition, improving overall task and process automation. The precision and ongoing improvement of processes is possible thanks to applied AI, as shown by the example of an AR project for industrial process automation, carried out by our R&D team.
AWS and Azure offer cloud-based services for the sake of developing machine learning solutions, leveraging analysis in various industries. Machine learning allows computers to learn from data and improve from the experience of previous iterations, without the need for extensive programming background. Constantly improving, ML extends its learning capabilities and develops with minimum human supervision. Machine Learning solutions enable building models employed in chatbots, boosting e-commerce, improving services (e.g. smart homes and home security) and building neural networks.
AI solutions in service of creating unique experiences and building loyalty. Thanks to utilizing data on customer behaviour and analysing action patterns, the system can predict future steps and get ahead of the user, providing a tailor-made solution. Recommender Systems are one of the elements of our extensive LMS serving 40,000 users globally. Recommendation engines support the delivery of desired products and services, recognizing growing trends and answering to the ever-changing needs of clients, contributing to overall service improvement.
NLP is applicable in language detection, key phrase extraction, sentiment analysis, document categorization. By employing NLP solutions, business benefits from various technologies enabling computers to understand and potentially generate human language data. Understanding and analysis of natural languages facilitate human-computer interactions supporting solutions like e-learning platforms, employing e.g. task recognition mechanisms.
Easy implementation and flexibility
Both AWS AI and Azure AI come with extensive documentation including references and tutorials, supporting the implementation of components. Solutions available for all skill levels backed by open-source frameworks accelerate workflows and integration with applications on your companies’ side. Pre-trained models are accessible without machine learning experience and facilitate jump-start in AI implementation.
Lower tech threshold
Artificial Intelligence solutions provided by AWS and Azure streamline the development process and include a range of ready-to-use algorithms. Offering a set of elements configured through drag and drop, Azure and AWS open the gateway to incorporating Artificial Intelligence in business without acquiring expertise in algorithms. Pre-defined elements contribute to generating revenue through remarkable scalability and ease of use.
Optimization of implementation costs
Using pre-built components lowers the general costs of AI-supported products and solutions. Using prefabricated blocks involves little to no work with the configuration only. Without the need to design elements from scratch, the applications’ development process is more efficient and shortens the time-to-market, lowering overall costs. It is a game worth playing, as AI components enable building a working solution in a month or two, instead of a year or even more of Greenfield development.
Dynamic platform development
Artificial Intelligence platforms grow at an incredible rate - forecasts state that the whole AI market may reach the $500 billion benchmark in 2024. Microsoft developing Azure and Amazon with Amazon Web Services aren’t standing still and consistently provide new algorithms. The plethora of solutions and applications create immense potential for software development and the growing tendency won’t halt anytime soon.
Pre-built components of Azure AI and AWS AI carry a great potential for prototyping solutions. Trying out hypotheses on AI mock-ups is easier and faster than building the product from scratch, enabling early detection of challenges and receiving the client’s feedback on the solution. When the prototype’s performance is confirmed and the need for dedicated algorithms proven, custom AI for the product in question is built.
Computing power consumption
Building products and services with the use of AWS AI or Azure AI solutions requires less spendings on computing power. AI building platforms offer flexible computing, autoscaling and pay-as-you-go pricing. Not requiring extensive in-house investments, cloud services come with scalable resources, easily adaptable when the project’s needs grow.
Our development process consists of four stages that let our clients minimize the risk and costs of their AWS AI or Azure AI projects.
We analyse the challenge and decide if we can solve it.
We recommend algorithms, technologies and tools to be used.
We propose an estimated time of delivery and costs while creating a work plan.
We engage in the production phase, tests and final deployment.