Machine Learning Managed Services: Can Big Tech Provide Alternatives to IIoT Predictive Maintenance Software

in #micromoney3 years ago

By Anton Dziatkovskii — Founder of MicroMoneyMachine Learning Managed Services.jpg

The truth is that Artificial Intelligence (AI) has become one of the leading technologies for businesses.

It has led to the development of such innovations as machine learning and the Internet of Things (IoT), with companies or plants struggling to implement these technologies due to a lack of resources, knowledge, and expenses.

One of the main problems is that even if companies can allocate resources and invest in AI related technologies, they still can’t keep up with the changes in the fast-growing technological sector. MicroMoney has become a decentralized open source credit and big data bureau on the blockchain, and I would like to share my view on how companies can implement these technologies easier.

Today, technology play a big role in a company’s atomization of processes and effectiveness. Many companies have moved to the cloud, drastically reducing expenses and making communication within a team easier.

There is also a considerable need for big data, AI, and machine learning experts on the market. For big enterprises, this is a huge problem as the process of production shouldn’t have limitations and delays. The dearth of qualified big data employees who can develop AI predictive maintenance led to the development of the alternative sources implemented by companies like Google and Amazon.

The main goal for these companies actually makes AI accessible for a wider audience. It says that Cloud Auto-ML “enables developers with limited machine learning expertise to train high-quality models by leveraging Google’s state of the art transfer learning, and Neural Architecture Search technology.” Amazon Machine Learning includes visualization tools and wizards generating machine learning algorithms, where there is no necessity to learn difficult ML algorithms.

Machine learning managed services decrease the need for big data experts

AI tools are undoubtedly crucial for the company’s operations and provide more options in the market. However, generic machine learning developer tools are not enough to underline the need for IIoT predictive maintenance, and can’t replace big data scientists. The issue is industrial plants and companies can build the in-house machine learning predictive maintenance, which requires deep machine learning expertise and experience with the Software Development Lifecycle (SDLC).

This is not easy to achieve as there is a lack of big data specialists on the market, and it’s challenging to build such a team in one place. Another option for companies is to buy IIoT predictive maintenance technology, and this is what Google and Amazon suggest. Google Auto-ML and Amazon Machine Learning bring companies the opportunity to access machine learning tools, and there is no need for deep machine learning maintenance.

Open source machine learning

Open source is a crucial technology in the development of IIoT predictive maintenance software. It’s used by machine learning and AI professionals. Open source signifies computer software with a licensed source code open to the public by its copyright owner. However, Google Auto-ML and Amazon Machine Learning offer generic solutions and use open source.

The negative sides and possible threats to machine learning managed services for IIoT predictive maintenance

Such tools as Amazon Machine Learning and Auto-ML can be implemented only by highly-qualified big data experts. By cutting down the costs and hiring unqualified professionals, the companies face a high risk of failure. It’s not only about finding a source of ML algorithms but a necessity in the adoption of the new Operations and Maintenance (O&M) processes and other serious commitments. If your company can’t hire data science professionals, use simple algorithms for predictive analytics.

MicroMoney uses big data and AI technologies by sharing and exchanging the data we enable financial institutions, banks, retail businesses to efficiently scale. And, consequently, they will get access to new customers and reduce risks while entering new markets.