2017 was marked as the year of AI and Machine Learning development. From massive platform vendors to early-stage startups, AI and metric capacity unit became the key focus areas. VCs invested billions in funding AI-related startups. Platform corporations enlarged their R&D budget to accelerate analysis in AI domains. Finally, there’s no single industry that’s didn’t experience the influence of AI.
What is what?
Amazon Web Services (AWS) is a secure cloud services platform that provides computing power, access to storage, databases, content delivery services, and other functionality that helps in scaling and growing the business.
Microsoft Azure (Windows Azure) is the name of the Microsoft cloud platform. Provides the ability to develop and execute applications and store data on servers located in distributed data centers.
Artificial Intelligence (AI) – the science and technology of creating intelligent machines, especially intelligent computer programs; the property of intellectual systems to perform creative functions that are traditionally considered a prerogative
DevOps for Data Science
An information researcher is characterized as a person who is preferred in measurements over a normal software engineer and a superior developer than a normal analyst. Information researchers decisively concentrate on finding concealed examples in informational indexes. They apply demonstrated measurable models to present day informational collections to take care of business issues.
In spite of the fact that information researchers manage Python, R and Julia to make machine learning models, they are not capable to manage the framework and conditions required for creating and conveying ML models.
During the development phase, ML models will be moved back and forth between local development environments and cloud-based training environments where GPU-based VMs are used for scale.
If before the creation of a system of quality machine translation required a decade, now startups that have just started, a year later can offer consumers a fairly tolerable competitive product in this area.
Machine learning is a new approach working with information, it very quickly turns machines into intelligent devices. In many ways, the development boom of programs based on machine learning is related to the fact that almost everything necessary for this can be found among free software.
It’s enough to download the development environment, several libraries, read the manual and go ahead. For a week or two you can write, for example, a program for recognizing wine labels or even persons.
Cloud for exuberant dreams?
The complexity of machine learning algorithms and automatic robot control systems is such that for their operation it is required either to equip machines with powerful computational hardware, or to connect them to the cloud infrastructure.
It seems that humanity has decided, that the development of robotics will go along the second path – the electronic mechanism will install a communication module and a computer with a small computing power.
Device management, knowledge accumulation, brain renewal, interaction with other machines will occur through the “cloud”. Thus, having bought an unpretentious machine, over time, a person will be able to update it to super-brain, paying for a more intelligent firmware.
For example, a home robot with a universal set of sensors and manipulators can temporarily turn into a chef at the French Michelin restaurant.
Now we use cloud photo editors and file sharing, and soon we will subscribe to special software services for our robots, for example, to make them dance a waltz or portray a fight when we get bored. Training robots for collective behaviour is another trend in the industry.
Together – we are force (IoT and AI at your service)
For a long time, engineers have been working to teach the machines to work together harmoniously. For example, fly one group, demonstrate aerobatics, synchronously dance and generally move around. Why is this necessary?
First, it’s beautiful. In fact, of course, such a skill of robots will be vitally important for us, in the future.
For example, the smooth movement of self-propelled vehicles on intelligent roads cannot be established without the organization of interaction between the “smart” infrastructure and moving connected cars and their communication with each other. In the future, drones-postmen, having the ability to communicate with their own kind, will negotiate with police quad copters not to interfere with their work.
Robots-loaders in ports will be able to prepare containers for placement on a barge, receiving a signal long before it approaches. Smart house will turn on the warming up of the car and start to brew coffee at the moment when you take the toothbrush in your hands.
By the way, such interaction of machines is very necessary at industrial facilities. Automation in production increases year by year, and everything goes to replacing people with robots in all operations, leaving several people to monitor the order and in case of emergency situations.
AI creates the opportunities, what’s next?
The possibilities of AI opened such a universe, which humanity will master not even for decades, but for centuries. This means that robots become smarter and learn on their own. They are even able to transfer their knowledge to each other. For this, of course, we will need a communication infrastructure. With its help, the program that invented recently a new universal language, could train him other cars.
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