On-premise or Cloud? Where Should Manufacturers Implement Machine Learning Platforms?

A customer contacted me recently regarding the machine learning platform. As a manager, he manages not only the operation and maintenance of the platform but also compliance and user requests. Sometimes, it can be challenging to fulfill the expectation of different stakeholders. Moreover, there’s a remarkable trend for businesses turning to the cloud. “Is there any best practice for enterprise to choose between on-premise or cloud? “ he said.

In this article, we will cover the following topics

• What is Machine Learning
• What’s the benefit of implementing Machine Learning?
• What are the scenarios in manufacturing?
• Comparison between on-premises and cloud from financial perspective
• Comparison between on-premises and cloud from application perspective
• Summary

What is Machine Learning?

Machine Learning is part of artificial intelligence. It is the study of computer algorithms which can make prediction and decisions based on amount of sample data. The algorithm evolves as the amount of data grows. For example, social media such as Instagram predicts our preference based on our views and likes, even put tags on our profile. As time goes by, Instagram will have a more comprehensive tagging and customer profile, which leads to a more precise prediction.

What’s the benefit of implementing Machine Learning platform?

As with most of the technologies, ML helps us to create more value in two ways. First is increasing revenue through increasing capacity, creating new services or products, and enhancing customer engagement. Another angle will be decreasing cost through streamlining the business process and improving efficiency.

Let’s see what are the scenarios that Machine Learning can help manufactures.

What are the scenarios in manufacturing?

Manufacturing process can be very complex throughout the raw materials to final products. In general, there are big phases including raw material extraction, manufacturing production, transportation, disposal recycling. If we take a closer look into manufacturing production, it contains product design, scheduling, manufacturing itself, and supply chain management. In this article, we will focus on how to use machine learning to reduce error and increase capacity. If you are interested in learning more about different scenarios, feel free to check here.

Comparison between on-premises and cloud from financial perspective

The most important factor for enterprises when evaluating whether turning to cloud is probably the finance aspect. There are different top of mind for rapidly growing company, mature company, and conservative company:

Rapid growing company focuses on revenue growth

Rapid growing company focuses on how to acquire more customers. Many companies would gain more resources, including talent and capital, to achieve their goal through fundraising. For example, Shopify needs to expand its global footprints for acquiring more customers. Also, spending on R&D for new features to differentiate themselves among competitors. All these need resources. The investor would evaluate the company to decide how much they should invest. For the company at this stage, it is more important to focus on business development rather than worry about IT infrastructure planning in a global scale. Cloud service providers are good partners to team up with as they have data center coverage globally.

Mature company wants to keep margin

Mature company has a rather stable business model. The top of mind of the C suite would be maintaining the margin. Let’s take Foxconn as an example, when the sales of its electrical components have been stable, the next question would be: how to keep the cost low? One of the nature of cloud computing is flexible cost structure, which would help companies lower the cost while the sales decrease, so to keep the margin.

Conservative company optimizes free cash flow

Rick management would be crucial for conservative companies, especially during the special situation. The company prefers to have enough free cash flow for uncertainty so that it can keep operating in the market. For example, many companies are affected during the pandemic. They can’t keep business running due to the lockdown. If they don’t have enough cash to pay the bill, such as salaries or rent, it would be an alert. Cloud computing turns CAPEX into operating cost to avoid upfront cost and keep cash on hand.

Comparison between on-premises and cloud from other perspectives

There are other benefits when considering turn to the cloud:

Flexibility

In the past, IT expense was considered as CAPEX, which are funds used by a company to acquire, upgrade, and maintain physical assets such as technology or equipment. The company needed to plan ahead for forthcoming demand, such as storage and computing power, from the business side. The consequence of this approach was the waste of idle machines and budget. Take World Cup as an example, Broadcasters would invest hugely to fulfill streaming demands globally when the game took place. However, the resources became obsolete after the season. If the broadcaster leverages cloud computing, the budget allocates more efficiently as the machine can switch on and off on an hourly basis. The broadcaster won’t need to pay when the machine is not in use. This strategy also applies to shopping festivals for e-commerce or surge demand for manufacturers.

Time to market

TTM, or Time to market, are synonyms describing the period of time it takes from initial idea to finished product. When expanding business overseas, companies demand the fast provisioning of applications to speed up time to market by leveraging the global footprints of cloud service providers, saving time on purchasing property, equipment, and construction.

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Multi-cloud Spending: 8 Tips To Lower Cost

A multi-cloud approach is nothing but leveraging two or more cloud platforms for meeting the various business requirements of an enterprise. The multi-cloud IT environment incorporates different clouds from multiple vendors and negates the dependence on a single public cloud service provider. Thus enterprises can choose specific services from multiple public clouds and reap the benefits of each.

Given its affordability and agility, most enterprises opt for a multi-cloud approach in cloud computing now. A 2018 survey on the public cloud services market points out that 81% of the respondents use services from two or more providers. Subsequently, the cloud computing services market has reported incredible growth in recent times. The worldwide public cloud services market is all set to reach $500 billion in the next four years, according to IDC.

By choosing multi-cloud solutions strategically, enterprises can optimize the benefits of cloud computing and aim for some key competitive advantages. They can avoid the lengthy and cumbersome processes involved in buying, installing and testing high-priced systems. The IaaS and PaaS solutions have become a windfall for the enterprise’s budget as it does not incur huge up-front capital expenditure.

However, cost optimization is still a challenge while facilitating a multi-cloud environment and a large number of enterprises end up overpaying with or without realizing it. The below-mentioned tips would help you ensure the money is spent wisely on cloud computing services.

• Deactivate underused or unattached resources

Most organizations tend to get wrong with simple things which turn out to be the root cause for needless spending and resource wastage. The first step to cost optimization in your cloud strategy is to identify underutilized resources that you have been paying for.

Enterprises often continue to pay for resources that have been purchased earlier but are no longer useful. Identifying such unused and unattached resources and deactivating it on a regular basis brings you one step closer to cost optimization. If needed, you can deploy automated cloud management tools that are largely helpful in providing the analytics needed to optimize the cloud spending and cut costs on an ongoing basis.

• Figure out idle instances

Another key cost optimization strategy is to identify the idle computing instances and consolidate them into fewer instances. An idle computing instance may require a CPU utilization level of 1-5%, but you may be billed by the service provider for 100% for the same instance.

Every enterprise will have such non-production instances that constitute unnecessary storage space and lead to overpaying. Re-evaluating your resource allocations regularly and removing unnecessary storage may help you save money significantly. Resource allocation is not only a matter of CPU and memory but also it is linked to the storage, network, and various other factors.

• Deploy monitoring mechanisms

The key to efficient cost reduction in cloud computing technology lies in proactive monitoring. A comprehensive view of the cloud usage helps enterprises to monitor and minimize unnecessary spending. You can make use of various mechanisms for monitoring computing demand.

For instance, you can use a heatmap to understand the highs and lows in computing visually. This heat map indicates the start and stop times which in turn lead to reduced costs. You can also deploy automated tools that help organizations to schedule instances to start and stop. By following a heatmap, you can understand whether it is safe to shut down servers on holidays or weekends.

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Applications of machine learning in different industry domains

Machine learning applications are a staple of modern business in this digital age as they allow them to perform tasks on a scale and scope previously impossible to accomplish.Businesses from different domains realize the importance of incorporating machine learning in business processes.Today this trending technology transforming almost every single industry ,business from different industry domains hire dedicated machine learning developers for skyrocket the business growth.Following are the applications of machine learning in different industry domains.

Transportation industry

Machine learning is one of the technologies that have already begun their promising marks in the transportation industry.Autonomous Vehicles,Smartphone Apps,Traffic Management Solutions,Law Enforcement,Passenger Transportation etc are the applications of AI and ML in the transportation industry.Following challenges in the transportation industry can be solved by machine learning and Artificial Intelligence.

• ML and AI can offer high security in the transportation industry.
• It offers high reliability of their services or vehicles.
• The adoption of this technology in the transportation industry can increase the efficiency of the service.
• In the transportation industry ML helps scientists and engineers come up with far more environmentally sustainable methods for powering and operating vehicles and machinery for travel and transport.

Healthcare industry

Technology-enabled smart healthcare is the latest trend in the healthcare industry. Different areas of healthcare, such as patient care, medical records, billing, alternative models of staffing, IP capitalization, smart healthcare, and administrative and supply cost reduction. Hire dedicated machine learning developers for any of the following applications.

• Identifying Diseases and Diagnosis
• Drug Discovery and Manufacturing
• Medical Imaging Diagnosis
• Personalized Medicine
• Machine Learning-based Behavioral Modification
• Smart Health Records
• Clinical Trial and Research
• Better Radiotherapy
• Crowdsourced Data Collection
• Outbreak Prediction

**
Finance industry**

In financial industries organizations like banks, fintech, regulators and insurance are Adopting machine learning to improve their facilities.Following are the use cases of machine learning in finance.

• Fraud prevention
• Risk management
• Investment predictions
• Customer service
• Digital assistants
• Marketing
• Network security
• Loan underwriting
• Algorithmic trading
• Process automation
• Document interpretation
• Content creation
• Trade settlements
• Money-laundering prevention
• Custom machine learning solutions

Education industry

Education industry is one of the industries which is investing in machine learning as it offers more efficient and easierlearning.AdaptiveLearning,IncreasingEfficiency,Learning Analytics,Predictive Analytics,Personalized Learning,Evaluating Assessments etc are the applications of machine learning in the education industry.

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Future of machine learning
Continuous technological advances are bound to hit the field of machine learning, which will shape the future of machine learning as an intensively evolving language.

• Improved Unsupervised Algorithms
• Increased Adoption of Quantum Computing
• Enhanced Personalization
• Improved Cognitive Services
• Rise of Robots

**Conclusion
**
Today most of the business from different industries are hire machine learning developers in India and achieve their business goals. This technology may have multiple applications, and, interestingly, it hasn’t even started yet but having taken such a massive leap, it also opens up so many possibilities in the existing business models in such a short period of time. There is no question that the increase of machine learning also brings the demand for mobile apps, so most companies and agencies employ Android developers and hire iOS developers to incorporate machine learning features into them.

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Hire Machine Learning Developer | Hire ML Experts in India

Machine learning applications are a staple of modern business in this digital age as they allow them to perform tasks on a scale and scope previously impossible to accomplish.Businesses from different domains realize the importance of incorporating machine learning in business processes.Today this trending technology transforming almost every single industry ,business from different industry domains hire dedicated machine learning developers for skyrocket the business growth.Following are the applications of machine learning in different industry domains.

Transportation industry

Machine learning is one of the technologies that have already begun their promising marks in the transportation industry.Autonomous Vehicles,Smartphone Apps,Traffic Management Solutions,Law Enforcement,Passenger Transportation etc are the applications of AI and ML in the transportation industry.Following challenges in the transportation industry can be solved by machine learning and Artificial Intelligence.

• ML and AI can offer high security in the transportation industry.
• It offers high reliability of their services or vehicles.
• The adoption of this technology in the transportation industry can increase the efficiency of the service.
• In the transportation industry ML helps scientists and engineers come up with far more environmentally sustainable methods for powering and operating vehicles and machinery for travel and transport.

Healthcare industry

Technology-enabled smart healthcare is the latest trend in the healthcare industry. Different areas of healthcare, such as patient care, medical records, billing, alternative models of staffing, IP capitalization, smart healthcare, and administrative and supply cost reduction. Hire dedicated machine learning developers for any of the following applications.

• Identifying Diseases and Diagnosis
• Drug Discovery and Manufacturing
• Medical Imaging Diagnosis
• Personalized Medicine
• Machine Learning-based Behavioral Modification
• Smart Health Records
• Clinical Trial and Research
• Better Radiotherapy
• Crowdsourced Data Collection
• Outbreak Prediction

**
Finance industry**

In financial industries organizations like banks, fintech, regulators and insurance are Adopting machine learning to improve their facilities.Following are the use cases of machine learning in finance.

• Fraud prevention
• Risk management
• Investment predictions
• Customer service
• Digital assistants
• Marketing
• Network security
• Loan underwriting
• Algorithmic trading
• Process automation
• Document interpretation
• Content creation
• Trade settlements
• Money-laundering prevention
• Custom machine learning solutions

Education industry

Education industry is one of the industries which is investing in machine learning as it offers more efficient and easierlearning.AdaptiveLearning,IncreasingEfficiency,Learning Analytics,Predictive Analytics,Personalized Learning,Evaluating Assessments etc are the applications of machine learning in the education industry.

Outsource your machine learning solution to India,India is the best outsourcing destination offering best in class high performing tasks at an affordable price.Business** hire dedicated machine learning developers in India for making your machine learning app idea into reality.
**
Future of machine learning
Continuous technological advances are bound to hit the field of machine learning, which will shape the future of machine learning as an intensively evolving language.

• Improved Unsupervised Algorithms
• Increased Adoption of Quantum Computing
• Enhanced Personalization
• Improved Cognitive Services
• Rise of Robots

**Conclusion
**
Today most of the business from different industries are hire machine learning developers in India and achieve their business goals. This technology may have multiple applications, and, interestingly, it hasn’t even started yet but having taken such a massive leap, it also opens up so many possibilities in the existing business models in such a short period of time. There is no question that the increase of machine learning also brings the demand for mobile apps, so most companies and agencies employ Android developers and hire iOS developers to incorporate machine learning features into them.

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