This article presents five technologies that every data engineer should know and master for his daily work. Spark as a data processing tool in the big data environment, Kafka as a streaming platform, Airflow, and serverless architecture for coordination and orchestration are presented. Before that, the importance and role of SQL (Structured Query Language) and relational databases will be discussed.
The interest in data science has not only increased enormously in recent years, but it has also changed and developed considerably. To best prepare your company for upcoming data science trends, we have identified 7 key trends in our whitepaper and for each trend we have identified 3 key actions you can take today. Get ready and use the promising future perspective of data science to create sustainable data-driven value for your business.
In this article, our colleague Stephan is first going to explain how GANs work in general. Afterward, he will discuss several use cases that can be implemented with the help of GANs, and to sum up, he will present current trends that are emerging in the area of generative networks.
Data science and artificial intelligence are the key to future value creation and competitiveness for many organizations. Therefore, efficient change management to transform thinking in the top-down process is urgently required in the context of AI initiatives. Successful change management to establish the data mindset requires a clear leadership commitment and is characterized by six key factors, which we deal with in this whitepaper.
The objective of this article is to show that fine-tuning Tesseract OCR on a small sample of data can already dramatically improve its OCR performance.
To successfully establish artificial intelligence in an organizational context, one should first understand the current status of the AI adoption. For this purpose, we at STATWORX have developed a maturity model for AI. It is based on our AI strategy approach and can determine the level of AI maturity in organizations, divisions and departments. Thereby, the company can be classified into one of four distinct maturity levels. Furthermore, we provide a actual/target matrix to illustrate possible development paths for AI within the organization.
More and more companies recognize the potential of artificial intelligence and develop their own ML models. At the same time, these companies are often faced with the challenge of making these models available to users internally and thereby generating added value from the model. In this article, we show what such challenges can be and how Docker enables companies to meet them.
Artificial intelligence (AI) has become one of the essential drivers of digital change in business and society. Companies face the challenge of establishing AI as an integral part of their corporate strategy to remain competitive in the future. Based on our many years of AI project experience, we have identified 6 key elements that companies need to consider when developing an AI strategy.
Here at STATWORX, a Data Science and AI consulting company, we thrive on creating data-driven solutions that can be acted on quickly and translate into real business value. We provide many of our solutions in some form of web application to our customers, to allow and support them in their data-driven decision-making. Containerization Allows Flexible Solutions At the start of …
In this blog post, Jannik will show you how to deploy your machine learning models as a REST API and how to make requests to the API from within your Python code.