R&D - laboratories
R&D - Research and Development. Such labs often involve basic ai research and the development of new algorithms and machine learning architectures. Specialists who are required in such areas are much more specialized and dig deeper than classical data scientists. R&D professionals often refer to themselves as machine learning or deep learning engineers, or simply as mathematicians.
Here are a few examples of tasks:
- developing trainable agents in computer games,
- robotic control, drones,
- autonomous driving.
Product Startups
The startup era is not over yet. Startups and venture investing are still a popular topic. The main peculiarity of this sphere is orientation on the whole product. Machine learning, if it is used, the main factor of its usefulness is to improve product usability and user experience (UX).
For example, an augmented reality mobile app for children. The popularity of such an app may largely depend not on a "soulless" quality metric, but on the brightness and entertainment of the picture. Another example:
>A chat-bot for learning English or just for "fun".
>Quality metrics are not at all obvious.
>The chat-bot can say something off-topic, but it will sound "cool" and get views, clicks, and likes. It's not hard to guess what I'm getting at here.
>At the very least, these kinds of apps or sites can make money from advertising.
Integrators, IT consulting
Integration companies and consulting services are in demand because they aggregate experience and knowledge. Their main value lies in their human capital. Launching any project with automation and machine learning requires the knowledge of many professionals from completely different fields. No one person can combine expertise in best industry practices and knowledge of the entire technology stack. A striking example is the MLOps practice offered by Neosoft. An alternative way to move a business to the next level is to hire an entire team, and this is done in "two clicks of the fingers".
Vendors and software developers
Business automation and modernization are built on the one hand on ready-made solutions, but on the other hand, can't do without customization. Of course, the task of customization for a particular infrastructure and business model can be solved by integrators at the level of customization of ready-made software. But often, in order to gain a competitive edge, a company must bring to the market its own unique service or product offerings. For example, even companies like Google or Facebook hire ai developers like EPAM.
Tech giants and platforms
Among the technology giants are:
- the "search engines" (Google, Yandex),
- online commerce (Amazon, Alibaba),
- social networks (Facebook, Instagram, WeChat).
These guys, if they want something, often buy startups and companies in their entirety and turn them into their own internal divisions.
A steady trend in recent years has been the move of anything and everything to cloud platforms. As a result, entire ecosystems of partner services based on such platforms as Azure, AWS or Google Cloud are being built. In particular, these services offer customized access to machine learning and data mining capabilities.