The value of machine learning (ML) can hardly be overstated, given that it enables companies to optimize their business, by way of cost savings and increased productivity, and gives rise to new business models.
In manufacturing, there are extensive ML opportunities, especially when combined with IoT and Edge computing, comprising the very foundation of what is referred to as “Industry 4.0.” ML solutions include optical quality control (machine vision) and the continuous monitoring of production equipment using predictive maintenance. However, although there is a lot of focus on ML itself, It is smart to pivot discussions around “data,” since almost all ML solutions feed off data.
Ideally, the data required by ML is built on top of an organization’s data strategy. This strategy should not only be implemented in tech or IT departments but rather, must be implemented across the entire organization. Collecting and providing low-barrier access to data is not only a prerequisite for most ML (and AI) solutions but also provides an all-inclusive view of the business and drives optimal data-based decision-making.
Machine Learning without the cloud? Think Again.
ML is a powerful tool that companies can use to develop and market completely new, highly innovative solutions. However, there is a certain level of maturity needed in an organization’s overall digital transformation journey which precedes or includes a data strategy – that is embracing a cloud-native mindset. In terms of innovation, being cloud-native decreases the time to value and embraces a notion of agility and speed.
By using Cloud services, such as AWS, an organization will have sufficient computing and storage capacity available to it, as well as the necessary high-level ML services and solutions at a low, pay-as-you-go price. It’s no wonder that according to IDC Research, manufacturing is in the top three sectors that plan to spend the most on cloud computing services at $19.7 billion, with professional services ($18.1 billion) and banking ($16.7 billion) not far behind.
Beyond ML innovation, migrating to the cloud brings your organization more flexibility and scalability; low costs with the pay-as-you-use options; competitive advantages, and frees up your IT resources to focus on growing your business while moving your organization away from a siloed data approach to a collaborative and accessible one. Your bottom-line costs for IT should be on par with the increase in business value delivered by IT. It’s all about doing more with less.
The Cloud Obstacles
The journey to becoming cloud-native will have its challenges and obstacles, as most innovative projects do. However, if companies don’t find a way to navigate around those challenges, it can cause a delay to their target goals even if they have completed their cloud migration. The top two challenges we see customers experience are: little or no cost optimization and IT resources with insufficient training. But, there is a way to overcome these obstacles with a bit of planning.
Obstacle 1: Little to no cost optimization
Once your organization is in the cloud, one of the critical steps is monitoring and optimizing operations. The challenge for most organizations is that the practices and processes that need to be put in place for success are quite different from ones developed for an on-prem solution. Without these processes in place, organizations tend to leave their applications running at a high rate and reduce their operational levels only when the company has downtime or neglect to touch or adjust their application perimeters at all. According to Gartner, companies that do little or no cost optimization in the cloud pay up to 70% too much, meaning that those resources are not available for innovation and business growth.
Begin your cloud journey with a plan in place and with an in-depth understanding of your current monthly charges and your corresponding resource consumption. Your plan should also include visibility to your organizational practices that maximize your ability to see where your money is going and the purpose of allocation. Simple admin cleanups such as ensuring your naming conventions clearly identify workloads and using a tagging system.
There are also numerous AWS services that are tremendously helpful in optimizing your costs such as the AWS Trusted Advisor Service that helps your organization eliminate any unused resources and monitors service limits. AllCloud’s AWS Cost Optimization Guide has many tips and best practices to get you started.
Obstacle 2: Lack of resources with the proper expertise
Even after having migrated to the cloud, when it comes to ML, going cloud-native requires you to have the right human resources on hand to develop, deploy and scale ML. These resources need to have the right expertise and leverage the right blueprints to establish the necessary cloud-native data foundation such as a data lake. This means that many companies are facing an impossible challenge if they only rely on their internal resources, a challenge that can be seen by the countless abandoned ML and data projects and PoCs.
The solution: a strategy with an associated roadmap
Depending on the digital maturity level, crucial steps may be required before ML solutions can be rolled out. This is especially true for companies that have a greater need to catch up and address the topic at the C-level to develop a strategy with a suitable roadmap.
Not sure where to start? Perhaps a talented partner who has already helped numerous companies on this path and will provide the right expertise and manpower. That way, your in-house resources can focus on the growth of the business and aren’t wasted spending time on figuring out how everything works and using a trial and error method for your cloud journey, highlighted in our Top 3 Tips for Approaching Cloud Migration on AWS blog.
ML offers massive advantages, such as data-based decision-making and forms a foundation on which manufacturing companies can get their innovations ready for market – today and in the future. In order to get the most out of ML solutions, companies must have already migrated to the cloud in a sustainable way and in accordance with best practices. Also, continuous cloud cost management is the only way to have the financial resources required for sophisticated use cases with ML functionality. If organizations don’t have the manpower and expertise in-house for this complex part of the digital transformation journey, they should consider the support of a cloud enabler such as AWS experts with the background and skills to develop the precise strategy that is right for an organization.
Looking to see how you can optimize and get the most out of your current cloud strategy or machine learning capabilities? Contact our AWS experts today.