Yandex Blog

Jane Zavalishina is the CEO of Yandex Data Factory, a spin-off of Yandex founded in 2014 to provide machine learning solutions to enterprises. In this post, Jane explains how YDF’s business has evolved since its launch, and why industrial AI is now in focus of its strategy.

Since its inception, Yandex Data Factory (YDF) has pioneered an innovative way to create value for companies by applying our expertise in machine learning and artificial intelligence (AI) to help solve their business needs. YDF arose as a solution to the problem many businesses faced at the peak of the big data craze. Essentially, businesses had begun amassing huge amounts of information, but were struggling to extract tangible value from this data.

The solution, of course, is in machine learning. Our parent company, Yandex, was an early leader in machine learning technology and today, machine learning powers 70 percent of Yandex’s products and services. We realised that wherever large stores of data exist, so does the opportunity to use that data to reach measurable business improvements. The same algorithms that power Yandex’s services, can be used to help other businesses improve their operations, revenues and profitability.

Over the past two years, we have worked with a number of companies across multiple industries on various successful projects. Together with our clients, we discovered the best use cases where machine learning can be applied to increase the efficiency of existing processes in a measurable way – be it predicting demand for a retail chain, or using computer vision to cut moderation costs for online service. Along the way, we accumulated a huge amount of expertise on merging data science with business.

One such case included our work with Magnitogorsk Iron & Steel Works (MMK), that marked one of the first ever collaborations of its kind between a technology company and steel company. MMK, one of the world’s largest steel producers, wanted to reduce its production costs while maintaining the same high-quality product. YDF developed a machine learning-based service that recommends the optimal amount of ferroalloys—the ingredients needed to produce specific steel grades. Our predictive system demonstrated the reduction of ferroalloy use by an average of five percent, equating to annual savings of more than $4 million in production costs, while consistently maintaining the same high quality of steel.

Similarly, we are now optimising the operations of a gas fractionation unit for a petrochemical company. Our solution recommends the fractionation unit parameters for maintaining the best performance and energy savings, decreasing costs in the process. Last week, we also signed a collaboration agreement with Gazprom Neft, an integrated oil company. We plan to apply our technologies to well drilling and completion, and other production processes. These successful efforts demonstrate the high potential for collaboration between artificial intelligence and industrial manufacturing.

The industrial sector – responsible for one-third of global GDP – has proven to be the ideal vertical, perfectly positioned for the effective application of our technologies. The industrial sector has become YDF’s focal point through the combination of our own successful application of predictive analytics with industrial data and the fit of the industry. Put simply, manufacturers know the value of optimisation at their hearts. Industrial manufacturing is also a unique cultural fit. They value measurements above opinions, they have perfected integrating new technologies in the existing processes, and they know how to estimate their effect through properly designed experiments.

For decades, the cornerstone of competitiveness in manufacturing has been centered on the optimization of existing processes, reaching for each tenth of a percent of efficiency in each step. And when all traditional optimisation means have been applied, the next efficiency leap of five to ten percent is often prohibitively expensive and equally time-consuming. These improvements typically consist of equipment upgrades with multi-million dollar investments, years spent on construction, rigorous training and implementation, and a lengthy delay before seeing any tangible financial return. Compared to this, receiving the same level of optimization via machine learning in a matter of months with minimal upfront investment is nothing short of revolutionary.

These long-term benefits extend far beyond a simple profit and loss sheet, and can help conserve both human capital and natural resources. By training machines to focus on the mundane, routine decisions that keep a factory running, artificial intelligence and machine learning allow human employees time to tackle more important tasks. By applying these technologies to oil and gas, companies not only achieve time and material savings, they can also reduce their energy consumption by up to 25 percent.

Our AI-enhanced models create endless opportunities to add value to the manufacturing industry. These benefits are especially noticeable in process manufacturing, where materials and mixtures – metals, chemicals, etc. – are produced. Essentially, these are also the industries responsible for the highest resource consumption.

The AI revolution in manufacturing is happening right now, and we are thrilled to be leading the charge. As this future becomes a reality, we’ll be there – at the forefront – blazing new trails in the industrial sector and delivering far-reaching effects for both the companies we work with and the larger communities they serve.