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Providing architecture and technology recommendations for implementing the desired enterprise AI capabilities into your IT environment. Advising business leaders and data scientists on building ecosystems required to accelerate the development and productionizing of machine learning models and AI applications.
Ensuring an efficient flow of data is a critical component of a successful AI solution. Provide guidance on how to build a complete, efficient, secure, and flexible architecture for data collection, preparing data, data management, and storage. Supporting data engineers and big data experts in creating data platforms allowing for turning unstructured and structured data into insights and leveraging them to maximize business value.
Partnering with leading enterprise AI and data science platform providers to stay at the forefront of the data analytics technology landscape. Grape Up certified consultants with expertise in Dataiku, Microsoft Azure, and AWS environments can help business leaders navigate their journey to enterprise AI and ensure end-to-end platform deployment.
Building scalable, performant, and secure data streaming platforms and data lakes based on a modern cloud technology stack. Integrating AI and machine learning platforms with data sources, existing systems, and other elements of the enterprise AI environment allowing companies to automate processes, deploy models and build AI-driven applications at a large scale.
Maintaining and operating cloud-based analytics and data infrastructure. Ensuring reliability and security for mission-critical solutions, enterprise AI platform providers (including Microsoft Azure, Dataiku, AWS), open-source platforms, and making machine learning developers and data scientists work easier.
Following MLOps practices to design and build automated pipelines and workflows that standardize and streamline the deployment of machine learning, data science, and AI-driven solutions to production. Enabling enterprise companies to focus on artificial intelligence software development and problem-solving while the deployment process is automated.
Enterprise companies willing to deliver advanced analytics solutions at scale need solid foundations based on the combination of resilient cloud infrastructure and performant enterprise AI technologies and AI platforms.
Read about delivering enterprise AI platforms that empower data engineers, data scientists, and machine learning engineers to productionize machine learning models and AI applications.
Grape Up developed a platform that empowers DBS to utilize data from numerous data sources. By building a scalable, efficient, and robust data system, the leading Asian bank can effectively provide its services to various markets and handle the growing demand in digital banking products.
Grape Up developed a platform that empowers DBS to utilize data from numerous data sources. By building a scalable, efficient, and robust da...
Machine Learning combined with edge computing gains a lot of interest in industries leveraging AI at scale – healthcare, aut
We’re connected. There’s no doubt about it. At work, at home, in town, on holidays. Our life is no longer divided into off
Learn more about a broad range of solutions empowering data science, artificial intelligence, and machine learning experts to deliver state-of-the-art, next-generation AI-powered software. See how established companies develop intelligent applications at an enterprise scale.
Leveraging artificial intelligence to enhance delivered software and improve business processes has become a priority for many innovative companies. Their teams ensure the proper environment to automate the way application developers create solutions and deploy models to production. Using cutting-edge AI and machine learning platforms, enterprises gain greater efficiency, reduce time and costs, and accelerate the process from ideation and testing to deployment.
Machine Learning is a self-learning AI technology that develops itself autonomically by analyzing experiences, examples, and data, without a programmer's intervention or assistance. ML-enabled applications learn by observing behaviors and getting insights from provided inputs. Such apps use collected insights to make better decisions and improve operations. Machine Learning analyzes large volumes of data, using computational intelligence to accelerate the pace the knowledge is used to determine business opportunities, improve customer experience, minimize risks, or accelerate digital transformation.
AI operationalization is a process of applying ML models to particular use cases and adopting AI experiments to reliable production models. Developing an AI-enabled application requires learning and validating phase. Such an application cannot be directly deployed to production. Every ML model needs a proper implementation phase when the solution is verified and adjusted to the environment. It's a critical process to implement AI in an enterprise and obligate both Data Science proficiency and software development expertise at scale.
Natural language processing combines AI, computer science, and computational linguistics, allowing computers to analyze human language. Using NLP techniques, computers extract data and insights from various content (including voice calls, text messages, feedback gained by virtual assistants) and provide enterprises with precise recommendations to improve customer experience and business operations.
Data science enables companies to gain insights and improve their business processes with data-driven decision making. To get the most out of the collected data, enterprises build the proper environment allowing for transforming raw data into predictive models and complete business information that can be used by machine learning and big data applications.
MLOps standardizes and streamlines the lifecycle of machine learning solutions in production. MLOps combines the knowledge gathered by DevOps engineers, machine learning specialists, and data scientists to automate and accelerate the operationalization and development of AI and ML applications. MLOps practices allow companies to control the full lifecycle of the machine learning models, including the development phase with training, packaging, and validation, and the production phase with deployment, monitoring, and retraining.
Defined as a machine's ability to learn, understand, and solve problems similarly to humans, Artificial Intelligence is one of the driving forces of innovation. With the continuous development of AI technologies, enterprises across industries use their features to improve customer service, increase safety, accelerate business operations, and automate repetitive tasks. AI, with its capability to process large volumes of data and learn at a rapid pace, has become a crucial factor in software development.
Computer vision is an AI technique that extracts data and information from visual content, enabling AI-powered tools to provide recommendations and improve decision making.