Yandex Maps the Landscape of Machine Learning Development Skills

In an innovative step towards demystifying the evolving field of machine learning (ML), Yandex has unveiled a comprehensive map detailing the technical skills of ML developers. This dynamic tool, derived from analyzing programmers’ queries, serves as a guide to the technologies and methods that are currently in vogue within the machine learning domain.

The skill map visually represents the popularity of various ML skills, with the size of each skill name indicating the volume of queries related to it. This interactive feature allows users to delve into specific skills, offering detailed descriptions, the trajectory of popularity since 2010, and a list of alternative methods, technologies, libraries, and frameworks. Such granularity not only enhances understanding but also aids in identifying emerging trends and potential areas for development or research.

In addition to the skill map, Yandex has compiled a rating of skills based on their growth in interest. The data reveals a keen industry focus on generative models and neural networks, particularly in object recognition. The top technologies and methods that have seen a notable surge in interest in 2023, as compared to the previous year, include a diverse array of libraries and frameworks:

  • langchain
  • python-polars
  • faiss
  • sentence-transformers
  • huggingface-datasets
  • jax
  • stable-baselines
  • onnxruntime
  • huggingface-transformers
  • pytorch-geometric

Moreover, the rating highlights the prominence of large-language models, stable diffusion, OpenAI API, GPT-3, fine-tuning techniques, generative art, text parsing, HDBSCAN, transformer models, and NLP question-answering. These insights not only reflect the current priorities and interests of ML developers but also suggest the direction in which the field is heading.

Yandex’s initiative to map out ML developer skills underscores the company’s commitment to fostering a deeper understanding of the technological landscape. By providing this resource, Yandex aims to support programmers in navigating the complex and rapidly changing world of machine learning, encouraging informed decision-making regarding skill development and project focus.

This endeavor aligns with Yandex’s broader investment in digital innovation and security, as evidenced by its significant investment in digital security initiatives in 2023. As machine learning continues to shape various sectors, from healthcare to finance, resources like the ML developer skill map will be invaluable for professionals seeking to stay at the forefront of technological advancement.

Dan Taylor is the founder and editor of International Search Insights (ISI), the publication behind ReadIsi, and a Partner and Head of Innovation (Search & AI) at SALT.agency. He has worked in search for more than a decade, in-house and agency-side, and is regarded as one of the West’s leading voices on search engines beyond Google, including Yandex, Baidu, Naver and Cốc Cốc. On ReadIsi, Dan covers the engines and AI answer platforms that English-language coverage tends to miss: Baidu and ERNIE, Yandex and Alice AI, Naver, Cốc Cốc, Shenma, Sogou, WeChat and Weibo search, plus DeepSeek and Alibaba’s Qwen. He also keeps dated records of documented changes, such as the Yandex, Baidu and Naver algorithm update histories and the DeepSeek, ERNIE and Qwen model update histories. His approach is to take learnings from global search engines and apply logic with a high evidence threshold. A claim needs a source, patterns seen in one market are treated as a hypothesis until they are checked in another, and where the evidence is thin he says so. For brands, that means advice built on what can be shown, with measurement that separates being cited from being chosen. Dan is credited with coining the term “edge SEO” and won the inaugural TechSEO Boost research competition in 2018 for his work on CDN and edge changes for SEO. In 2026 he was credited with coining “Infinite Tail” to describe how keyword research is changing with AI search. He is the author of AI Search Marketing For The Travel Sector, writes for Search Engine Journal, Search Engine Land and Travel Marketing Guy, and has advised technology, SaaS, travel and enterprise ecommerce brands, including Cloudflare, GitLab and Airbnb. He has given multiple talks and panels at the International Search Summit in Barcelona, and has also spoken at Optimization Moscow, brightonSEO, Google Search Central Live Deep Dive Asia in Bangkok, Athens SEO and the Asia SEO Virtual Summit 2026, where his talk looked at how Baidu, Naver, DeepSeek, Tencent, Cốc Cốc and Yandex built their own AI answers.