There’s a rising need for using AI and machine learning in your business. In fact, global artificial intelligence spending is set to reach $434 billion in 2022. If you’re not following suit, you’re being left behind.
After all, machine learning can be used to optimize virtually any business process, saving you time and money. Sales teams can use AI to optimize sales funnels and prevent customer churn, marketers can score leads and classify customer text, HR teams can accurately predict attrition, and more.
Machine learning was once a massively complex task that required extensive technical work. Nowadays, with no-code machine learning solutions, you no longer need AI expertise to get an accurate analysis or prediction
Using cloud-based no-code machine learning, you can effortlessly scale models to optimize your business and gain competitive advantages.
Machine learning is a power-hungry, resource-intensive task, particularly when you’re dealing with big datasets.
For example, suppose you’re building a machine learning model to predict financial fraud. You might use millions of rows of financial data with dozens of columns, and during periods of high activity, you might have to run the model on hundreds or even thousands of transactions per second.
To be able to handle this high volume and speed, you’ll need large, reliable GPU clusters that can scale up or down to service any needs. These remote computing servers are commonly referred to as “the cloud,” and they’re where most machine learning is done.
The cloud is the future of data science. It’s where machine learning will be done, and it’s where a lot of big data analysis will happen. And that means a lot of benefits for companies.
For one thing, the cloud allows you to scale your machine learning projects up and down as needed. You can start with a small set of data points and add more as you get more confident in your predictions.
Variable usage makes it easy for enterprises to experiment with machine learning capabilities and scale up as projects go into production and demand increases.
You can also use machine learning to run experiments on different sets of data to see what works best. This is something that’s difficult or impossible to do on your own server at home or in your office building. And it’s something that requires a lot of time and effort if you want to do it yourself. In short, the cloud drastically speeds up the machine learning lifecycle.
Traditional machine learning isn’t just complex and hard to set up: It’s pricey. If you want to train and deploy large machine learning models, such as deep learning, on your own servers, you’ll need expensive GPU cards. This is particularly true with today’s state-of-the-art models, such as China’s natural language Wu Dao 2.0, a model with nearly 2 trillion parameters. With such models, the cloud is a must-have, not just a nice-to-have.
In order to scale your models to accommodate large-scale needs, you’ll need high-end GPU units, which means that they’ll remain largely unused during periods of low use. In other words, you’ll have expensive servers sitting around collecting dust, while still requiring extensive maintenance.
On the other hand, when using machine learning in the cloud, you’re only paying for your consumption, which works wonders for scalability. Whether you’re just personally experimenting or servicing millions of customers, you can scale to any needs, and only pay for what you use.
Building, managing, and maintaining powerful servers oneself is a complex task. With the cloud, much of the complexity behind these tasks is handled by the cloud provider.
Popular cloud services like AWS, Microsoft Azure, and Google Cloud Platform in fact offer machine learning options that don’t require deep knowledge of AI, machine learning theory, or a large team of data scientists.
With the cloud, AI can be deployed in a matter of minutes. It also scales automatically, so you don’t have to worry about the technical complexity of provisioning resources or managing infrastructure.
Most popular cloud services also provide SDKs (software developer kits) and APIs. This allows you to embed machine learning functionality directly into applications. They also support most programming languages.
With the cloud, you can integrate machine learning into your workflows quickly and easily. In the past, machine learning models were difficult to integrate into existing applications. In today’s cloud-native AI world, this is no longer the case. With Akkio’s API, and even no-code integrations like Zapier, it becomes effortless to integrate machine learning models anywhere, from healthcare to IoT.
The Akkio AI platform provides end-to-end AutoML automation, including model training, real-time inference, high-performance DevOps, and more. Akkio’s no-code web application provides AI toolkits without the complexities of traditional cloud environments, which require technical expertise in tools like Python, TensorFlow, PyTorch, and Kubernetes.
Another important aspect of the cloud is that it reduces the time-to-value. Time-to-value is the amount of time it takes from when you start a project to when you see results from it.
In traditional machine learning deployments, this process can take months or even years. With the cloud, you can start seeing results in hours or days. That’s because you don’t have to provision resources, manage infrastructure, or write code. You can simply upload your data and start building models.
Data is the lifeblood of machine learning. The more data you have, the better your models will be. And the cloud provides access to more data than ever before.
For example, if you’re building a predictive model for customer churn, you can access historical customer data that’s stored in the cloud. This data can be used to train your machine learning model so that it can make better predictions.
When done right, machine learning in the cloud is secure and private. That’s because the data is stored in the cloud provider’s secure data center.
The cloud provider is responsible for the security of the data center and the data that’s stored there. This means that you don’t have to worry about building your own security infrastructure.
In addition, most cloud providers offer additional security features, such as encryption, to further protect your data.
Machine learning in the cloud frees up resources so that you can focus on other things. For example, if you’re building a machine learning model to predict demand for a new product, you can use the cloud to train and deploy the model. This frees up your time so that you can focus on other things, such as marketing the product.
When done right, machine learning in the cloud provides a number of benefits that are difficult or impossible to achieve with traditional machine learning. These benefits include reduced time-to-value, easier integration, and increased security and privacy.
While machine learning in the cloud enables incredible things, from generating poems to powering self-driving cars, the technology isn’t perfect. Let’s explore some of its limitations.
Machine learning is a powerful tool, but it can’t make decisions on its own. And machine learning systems need to be monitored and corrected by humans. This is true for virtually any technology, not just machine learning. It’s also true for many of the most exciting uses of machine learning today: from fraud detection in credit card transactions to improving cancer treatments to predicting earthquakes.
These are all great applications that could benefit from machine learning, but they still require human oversight and intervention.
The limitations of machine learning are sometimes overstated, especially in the media. But there are real limits to what artificial intelligence can do without human supervision and intervention. Machine learning can’t yet replace experts at every step of the process, because no algorithm can understand everything about a situation or know how to react in every possible scenario.
The cloud is a great place to run machine learning models, but it also has some limitations. For example, if you want to move your data from the cloud to another cloud provider, you have to do so in a way that doesn’t impact the performance of your model.
This can be tricky because machine learning models are often sensitive to small changes in their input data. If, in order to change the location of your data, you need to make changes to its format or size, for example, then your model might not work as well anymore.
Solving these data mobility solutions with multi-cloud data lakes can seriously add to the pricing, particularly if you’re looking for solutions on-premises.
You will always run the risk of your data center facing problems caused by natural calamities and attacks made by hackers. This includes downtime, data leaks, and loss of data. It's important to invest in a secure platform that has distributed data centers and maintains multiple copies of your data.
Machine learning models can be hacked. If an attacker gains access to your AWS account credentials, for instance, they can use those credentials to modify your model and change its predictions. Such an attack could be undetectable by customers or administrators.
Machine learning models are also vulnerable to denial of service attacks. An attacker could send millions of fake requests for prediction results until your server runs out of space.
Ultimately, the security of your data is in the hands of the cloud service provider. You need to ensure you know the clauses and level of security in place.
If your data is stolen or hacked, you can take legal action against the service provider. However, there is no guarantee that you will be successful in recovering your data. In some cases, it might not be possible to recover all of your data from the cloud provider because they may have deleted or encrypted certain records due to regulatory requirements. It’s important that organizations understand their own risks and then find a service provider that can help minimize them.
Now, let’s take a look at the tools and technologies available to harness the benefits of machine learning in the cloud and minimize the limitations.
Amazon has a wide range of machine learning tools, including the popular open-source library Apache Mahout, which lets users produce free implementations of distributed or otherwise scalable machine learning algorithms.
The library provides a large number of machine learning algorithms and makes it easy to incorporate them into your application. It also includes support for data preprocessing and feature engineering, as well as scalable distributed training and prediction across multiple machines.
In addition to providing access to libraries like these, Amazon offers several cloud services that can be used to build machine learning applications. These include:
Microsoft Azure Cloud services are a great way to rapidly deploy and scale machine learning services, whether you’re building something simpler like speech-to-text or a more complex computer vision solution.
Microsoft Azure offers a variety of machine learning services, including:
Google Cloud Platform has built its reputation on delivering Infrastructure as a Service (IaaS). It is also an excellent choice for deploying machine learning models in the cloud. The Google Cloud ML Engine provides machine learning capabilities with easy access to data stored in BigQuery and other GCP databases.
IBM Cloud isn’t the most popular option, but it has a strong set of tools for Machine Learning. The company offers its own OpenScale ML Platform to monitor payloads.
One thing to keep in mind: There are many other options out there beyond these four vendors; use whatever you feel most comfortable using!
Akkio is a no-code machine learning solution made for all kinds of businesses, which lets you predict the future.
It is built for easy use - even marketing professionals and business developers without a technical background can easily use Akkio to access powerful technology.
Unlike traditional AI software where you’re forced to pay to train AI models (that may never end up being used), Akkio doesn’t have any hourly training rate. With Akkio, any team can build models without hefty up-front costs to test processes.
In this article, we understood what machine learning in the cloud is, the various cloud computing platforms available, and the tools that can help apply machine learning to a business efficiently.
If you’re looking to scale and grow your business with minimal technical know-how and effort, Akkio is the best place to start. Not only can you try it for free, but after signing up, you get access to a number of demos to play with. This lets you build state-of-the-art models in seconds, at no cost.