“Success in creating Artificial Intelligence would be the biggest event in human history”
- Stephen Hawking
Machine learning and Artificial Intelligence has become extremely hot topics in the past few years. This is mostly attributable to the fact that more companies, organizations and governments are starting to see a long-term strategic value in embedding machine learning capabilities into their operational frameworks and making AI one of the key pillars of their digital transformation.
Currently, AI is being used in a wide range of applications across various industries and it becomes increasingly obvious that AI based technology is slowly and steadily starting to re-shape entire industries. The spread and diversity of these industries is truly impressive:
Healthcare: continuous patient monitoring systems, preventive care applications, diagnosis support systems
Logistics and Transportation: self-driving cars and trucks, self-delivery, smart routing, predictive demand and inventory planning, automated warehousing
Retail: fully automated self-service retail stores such as Amazon Go, personalized offers and discounts
Manufacturing: smart quality control system, production processes tuning recommendation engines, predictive maintenance
Banking: fraud detection applications, customer chatbots, speech and voice services
This list could be filled with endless examples, however just these use cases give an understanding of how AI has genuinely become a cross-industrial discipline. Given the current efforts put into AI research, data volumes, data storage and computing power, we may not fully realize what lies ahead for AI related opportunities.
As a testament to the enormous potential that Machine Learning brings to the table, the investments pouring into this emerging field keep on piling up every consequent year. Tech giants such as Google and Amazon are spending a sufficient fraction of their R&D budgets on AI research. Governments are also realizing the potential economic gains of AI in leadership. For example, Canadian and French governments have officially announced their commitment to support and promote AI related development. Furthermore, China has existing ambitious plans to become an international AI innovation hub by 2030.
Despite of all of the above, there is a tiny fly in this fascinating AI ointment though. To date, many companies in a number of non-AI industries struggle to implement and benefit from the value provided by AI due to its complexity, its onerous integration process as well as a lack of skill and expertise. Such complexities pave the way for relatively new solutions, such as Machine Learning as a Service (MLaaS). MLaaS is supposed to democratize implementation of top-notch AI capabilities for organizations working in various domains. The advantages of MLaaS systems are that they significantly simplify AI implementation in organizations whilst considerably reducing the costs of development, deployment and maintenance of such systems. It accomplishes this by hiding all implementational, scientific and operational complexity inside while utilizing efficient hi-performance cloud computing infrastructures. With all this being said, chances are, MLaaS is likely to become the next buzz word after SaaS or IaaS.
A “one-click” AI deployment approach imposes challenging requirements on MLaaS solutions in many ways. Their architecture should flexible enough to support different and ever enhancing algorithms and scientific methods supported by various frameworks. These systems should have access to powerful hardware to support accelerated computations and massive data storage. Finally, machine learning could not be possible without clean and prepared data. In essence, this means that, among their key components, MLaaS applications should possess heavy data integration, management and processing functionality.
There is no doubt that many, if not all, MLaaS vendors are working hard to address all these and new challenges. There is strong evidence that MLaaS is expected to expand in upcoming years. It is expected to deliver more value to consumers as well as shape the way businesses apply the power of AI to make their businesses more effective and profitable.
Ildar Abdrashitov, Business Intelligence Analyst Missing Link Technologies