The ability to acquire, process, and manage data and information is essential for today’s organizations. While databases and data warehouses are well-known and established technologies, newer trends such as artificial intelligence, data analytics, data insights, advanced analytics, and self-service business intelligence enable companies to move ahead of their competitors.
I published numerous articles in English and German – and a book on how to run AI projects (“Managing AI in the Enterprise“), ML Ops, and how to set up a successful AI organization. In short: 505 grams that change your life as an AI specialist!
My articles in English about AI & Strategy:
- Becoming a Data-Driven Company – Understand the concept of a data-driven company!
- Artificial intelligence in data-driven companies – what is the role of AI for/in data-driven organizations?
- Innovation and the AI technology stack – an article helping you to align your AI teams’ and data scientists’ work with strategic company goals and to prevent ivory tower projects from wasting time and money.
- AI and Ethics: Risks and dilemmas – understand the Timnit Gebru versus Google controversy and how to avoid such clashes in your organization!
- Artificial intelligence creativity: Ready for the enterprise world? – learn whether computers can be creative and how companies can benefit from that.
My articles in English about AI Concepts and Methodologies:
- A risk and security perspective on AI – a general overview on the security risks of AI organizations
- Achieving productivity gains in data science – which tools allow AI and data science teams to work more efficiently
- Securing AI systems: Addressing data piling and data access in AI training environments – another article on securing the IT assets of an AI organization focussing more on specific data piling related risks
- Data catalogs: Enablers for AI projects – an article you might want to read before starting any data catalog project in your organization and before even choosing the software solution.
- Trends in Data Management and Analytics – a high-level architectural view on trends in analytics, AI, data management, and data warehousing
- The intricacies of model management – in academia, projects end after building a suitable AI model, but in a corporate world, models are in use for years and have to be maintained. This article unveils the mysteries!
- Quality Assurance and Machine Learning Models – a testing and quality assurance perspective on the work of AI projects and data science teams.
- Making CRISP-DM Work for Embedded Analytics discussed how to successfully organize the interplay of AI projects and software development projects when working on one joined vision and solution. To download a pdf version, click here.
My articles in German: