The Ozon Tech team has presented the key themes for the upcoming E-CODE 2026 conference, which will be held on September 12–13 in Moscow. The primary emphasis in the ML/DS track will be on the transition from theoretical LLM demonstrations to their deep integration into real production processes through agentic architectures and model fine-tuning methods.
What Happened
The E-CODE 2026 conference program includes presentations dedicated to industrial machine learning, prompt engineering practices, model fine-tuning methods, and the design of complex agentic systems. The event is oriented toward solving the challenges of scaling ML solutions in high-load e-commerce systems.
Context
The industry is seeing a shift in focus from simply using ready-made prompts to creating fully autonomous agents and continuous model fine-tuning for specific business contexts (RAG + fine-tuning). Major tech players like Ozon are implementing these technologies to automate complex workflows in retail and logistics.
Why It Matters for the Industry
For the market, this is a signal of the transition to the industrial implementation stage of generative AI. The development of such technologies creates demand for new tools to automate the lifecycle of AI agents and specialized platforms for managing prompt engineering in high-load environments. The experience of major e-commerce players may set the standards for system construction within the Russian tech sector.
Why It Matters for Users
ML and DS specialists will gain access to real-world cases of implementing LLMs and agents in high-load systems from Ozon engineers. This raises the qualification requirements for engineers: skills in not only writing prompts but also designing complex agentic architectures and performing fine-tuning are becoming increasingly in demand.
What Is Not Yet Known / Limitations
There is a difference in the assessment of the event's significance: while for founders and product developers it is a signal of new market tools, for solo developers, the conference may represent primarily corporate interest, offering few ready-made solutions for individual use.
Sources
Author
Look at AI, Editorial Team