As big data gets more complex, companies are struggling to accommodate the storage and computing needs of average organizations, much less massive enterprises. This is where cloud-native ML and AI comes into play.
What Does Cloud Native Mean?
Your computing power is limited. No matter what kind of hardware and software you buy, you’ll always be tiptoeing towards obsolescence. This is normal – and has been for years – but with the advent of big data and AI, we’re tiptoeing there just a little bit faster.
Enter cloud-native applications. The introduction of the cloud has democratized computing capability. Companies can deploy applications at scale using massive cloud computing and storage capabilities. Cloud native apps never settle into in-house systems. Instead, they run in the elastic computing environment, delivering reusable features through things like containers that can operate using agile.
Short story? You can deploy programs faster, progressively bigger, and with fewer taxes on your limited computing resources. Plus, the cloud can package your application into a container, allowing you to replicate the results across multiple platforms.
What Is Cloud Native Machine Learning?
Machine Learning requires datasets for training models to perform tasks. The machine brain learns to identify a hotdog versus “not a hotdog,” for example, by consuming raw training in the form of pictures of hotdogs versus not hotdogs. Accessing the type of data you need for training more complex tasks can be frustrating when you don’t have the human or processing power you need.
Your sole data scientist can’t go through thousands of piece of data to train the machine to recognize the hot dog. Instead, by deploying those tools in the cloud, a company could automate machine learning and operate at scale.
The cloud allows organizations to use automated or managed machine learning to remove the tax on limited human resources. It removes limitations and access to data and machine learning, allowing all stakeholders to access the program and insights. In short, everyone gets to know which is a hotdog and which isn’t.
It also opens the door for citizen data scientists to deploy programs without having experience in code. The cloud uses automation to train and serve out a model. The user can evaluate the model, debug, and replicate results from the cloud directly.
What Is Cloud Native AI?
AI takes the same approach on the cloud as machine learning, but the focus is a bit broader. In fact, AI is becoming the driver of cloud computing. Companies can deploy AI models and deep learning to the elastic and scalable environment of the cloud.
Cloud Native AI is less a new technology and more a paradigm shift. Businesses can use the cloud to deploy AI micro-services, for example, or find data lakes for better, more in-depth training required for deep learning. It’s beyond hotdog/not a hotdog, and more like learning how to make the hotdog itself.
Cloud-based AI can be added to the three pillar services so desperately needed by business: sight, language, and conversation. Cloud AI gives businesses the ability to deploy these services without having to have native AI apps. Organizations can leverage cloud AI for their existing applications, access data lakes, and perform big data pulls.
Advantages of Cloud-Based AI and ML
The cloud environment is elastic. This is the most significant advantage for most businesses because the ability to customize how much data and where it’s stored without later implementing costly upgrades and system changes is an absolute game changer. The environment grows or pivots with your development. This leads to three critical things:
Reliable scale: Your growth isn’t hampered by your dependence on an established in-house repository. You can work with your existing solutions and keep one eye on expansion without the expensive and time-consuming software switch.
Microservice capability: Microservices are modular and easily follow an agile development timeline. Your organization can deploy microservices to automate a product or service for your customer base and relieve the burden on your human team, or you can make use of microservices for targeted development.
Data Lakes: Data lakes housed in the cloud give your organization access to bigger, better data for training without straining your in-house resources. As you deploy newer models and race towards the continuous innovation ideal, building on the resources available outside of your organization gives you a strategic advantage.
The Downsides – It’s Not Magic
Kevin Wang of braze.com recently said, “The experience of effectively using AI is more like riding a horse, and less like having a super-intelligent horse steal your job.” For many people, this idea is comforting because no, in fact, AI is not coming for your job. There’s a flipside to this idea, however, because AI cannot do your job for you. Not really.
Deploying AI models or ML in the cloud doesn’t create a magic bullet for your product, service, or workflow. It enhances what you’ve already established. If you’ve got something great, those things make it better. If you’ve got something half put together, those things only amplify that confusion. If you don’t know where you’re going, your “horse” doesn’t either.
Other possible disadvantages center around the issue of control. Although you largely control your applications, services, and data, your back-end may reside with someone else. This possibility could be useful if you don’t have the infrastructure to handle true security and maintenance, but you’re more open to:
Downtime: Downtime is inevitable, and the good news is that your in-house IT team doesn’t have to work around the clock to figure out what’s going on. The bad news is that you have no control over when and where. If you don’t have a system in place to deal with downtime (expected and otherwise), you could lose out on your bottom line.
Service obstacles: If you can’t control your backend, you need to consider how your cloud provider will support your organization. Make sure you understand the service level agreements and that the SLA covers what you need.
Scaling With Cloud Native
Building a culture of continuous innovation can be difficult without proper infrastructure. For the first time, organizations have access to software and services that go far beyond their in-house capabilities. If you’ve got a scrappy startup, you could compete with giants in your industry quickly, as in right now. Consider the benefits and the downsides to see if you’re ready to make the switch to a cloud-native system.