The DataFest Tbilisi Conference 2024, scheduled for September 19-21, covers several topics that may interest professionals working with microservices architecture. While the conference is not specifically focused on microservices, several topics discussed are directly relevant to this architectural approach.
- Data Science and Platforms in the Cloud (Brian Gillikin, IBM) This session explores cloud technologies in data management and analysis, which is critically important for microservices architecture. It will cover cloud design and architectural decision-making, directly related to microservices deployment and management.
- MLOps Platform Development (George Markhulia, Volvo Cars) George Markhulia presents his experience in developing an MLOps platform that combines open-source tools with custom solutions. MLOps practices are closely tied to microservices architecture, especially when integrating machine learning models.
- LLM Systems: Development and Evaluation (Shota Natenadze, EPAM) This session covers the principles of developing LLM-based intelligent systems, including Prompt Engineering, Retrieval-Augmented Generation (RAG), and Fine-Tuning. These topics are relevant in the context of microservices, particularly when integrating AI into a microservices architecture.
- Data Visualization for Machine Learning Adoption in Business (Karim Douieb, Jetpack.ai) This presentation demonstrates how effective data visualization can help understand and utilize complex machine learning models. In the context of microservices, this approach can be used for system monitoring and optimization.
- A 5-Step Process for Better Decisions (Matthew Brandt, Data Educator & Decision Engineer) This workshop offers a methodology for making data-driven decisions. In a microservices architecture, where quick and effective decision-making is often necessary, this approach can be very useful.
- Using Artificial Intelligence in Language Processing (Artur Kiulian, OpenBabylon) This session discusses the training and refinement of large language models (LLMs), especially for resource-poor languages. In the context of microservices, this knowledge can be used to create and optimize language processing services.
- Technology Trends: Learn about the latest trends in data science and artificial intelligence that are impacting microservices architecture.
- Practical Experience: Participate in workshops where you can try out new approaches and tools in practice.
- Networking: Establish contacts with other professionals and share experiences on microservices development and management issues.
- Interdisciplinary Perspectives: Get inspiration from various fields that can be applied to improve microservices architecture.
Although the conference is not exclusively focused on microservices, it offers significant opportunities for professionals in this field to learn about new ideas and approaches that can be used to improve and optimize microservices architecture.
