Artificial Intelligence

Democratizing Machine Learning With No-Code AI

by
Jon Reilly
,
June 3, 2021

The traditional road to building and deploying artificial intelligence (AI) is long and expensive, with leading organizations spending an average of around $40 million a year on AI, while relatively few projects make it into production - and then primarily at Tier-1 technology companies like Google, Netflix, Microsoft and Apple.

From forecasting and automation to computer vision, Automated Machine Learning tools (AutoML) like Microsoft Azure can be a powerful way for technical AI professionals to speed up their workflows. That said, they have their shortcomings. Here are just a few:

  • They’re inaccessible to non-technical folk (data science and algorithms required). 
  • They’re expensive and time-consuming. 
  • AutoML models require a lot of expertise to deploy and maintain.

No-code AI platforms like Akkio are democratizing machine learning by making AI:

  • Easy to use: Akkio is built for non-engineers, so business users can build models straight from their datasets for many use cases without writing code or using complex tools. If you can use excel, you can build and deploy predictive models. 
  • Flexible: Akkio allows you to combine and predict data from multiple sources, including Salesforce, Snowflake, Google Sheets, and more.
  • Affordable: Akkio is priced based on the number of actions you take per month, so you pay only for what you use. 
  • Deployable: Akkio models can be deployed in any setting in just a few clicks, and used for real-time data-driven decision making.

Let’s explore these points (and more) in-depth to see why traditional AI tools, including AutoML, are ultimately too hard to use in practice, and why no-code AI platforms are the solution.

No-Code AI Doesn’t Need Experts

Traditional AutoML tools are not simple, intuitive, no-code solutions, like what the average Internet user is used to. They’re complex interfaces with a non-trivial amount of software development required to build and deploy models - at best they are “low-code”. And they still require data scientists or data science teams to take full advantage of their capabilities.

For example, take a look at Google’s Professional ML Engineer Certificate, which is designed for those who are proficient with Google Cloud technologies like Google AutoML. The prerequisites include over 1 year of experience with GCP, which is another way of saying that it takes over a year to become an expert in Google AutoML. That’s the equivalent of learning a new programming language. 

On the other hand, no-code AI tools like Akkio can be used to build and deploy models in minutes, and you’ll be well-versed in the functionality in hours, as opposed to years.

While no-code tools are like self-driving cars, that are “get in and go,” code-based tools are like aircraft cockpits, filled with complex dials that don’t make any sense unless you’re an expert.

No-Code AI Platforms Are Affordable

Traditional AutoML tools are not like your average software. They can cost hundreds of thousands, or even millions of dollars a year. That puts them out of reach for small to mid-sized businesses, never mind your average startup. 

For instance, Fool.com reports that C3 AI’s average contract price is over $10 million. Many traditional AutoML tools also charge hourly, so you’ll be spending money whether or not you end up deploying or even building a valuable model.

On the AWS Marketplace, we can see that H2O Driverless AI charges $10 an hour for the software and another $12 an hour for infrastructure costs. The hourly costs are almost like having another employee on board, except that this software is so complex that you’ll need an actual employee to manage it, quickly pumping up the cost of your AI projects.

With Akkio, there’s never a model training cost, so you can try out the model building without any risk. In every plan, there’s no limit on users, there’s no limit on data uploads, you can use the Akkio API to serve predictions in any setting, and more. Now businesses of all sizes can have access to machine learning tools and unlock AI applications.

No-Code AI Platforms Are Fast

Traditional AutoML isn’t just time-consuming because it’s complex, it’s time-consuming because the actual model training process is incredibly slow.

Akkio’s proprietary model training process is around 100x faster than traditional AutoML tools, allowing you to build an AI model for your use case in as little as 10 seconds. Slow model training quickly adds up, and even half an hour of training will add up to several hours as you iterate your model. As traditional AI tools require technical employees to use them, these costs add up double time.

With Akkio, non-technical professionals can iterate and deploy high-quality models in minutes.

No-Code AI is Easy to Deploy and Maintain

Deploying an AI model is its own beast with traditional AutoML tools.

First off, with tools like Google AutoML, you can’t easily deploy models with no-code tools like Zapier. In other words, there are only code-based deployment options. Secondly, deployment incurs separate charges. Thirdly, you need to be careful to “undeploy” and delete models to avoid unnecessary charges when you’re not intending to use a model.

In contrast, deployment is a one-click solution with Akkio, and it’s extremely easy to integrate Akkio models into any workflow automation with Zapier. The API is also highly straightforward, and there are direct integration options with tools like Salesforce.

Given that non-technical professionals can easily deploy and maintain models, it becomes much easier to bring artificial intelligence projects to production.

No-Code AI Has High Adoption

Imagine that you’ve signed a million-dollar contract to use a traditional AutoML tool for AI solutions and application development at your enterprise firm. If your employees don’t actually use the tool, it’s a complete loss.

There’s no guarantee that your employees will use the tool, so it’s critical to consider employee resistance to new tools. They may be asking: Why bother using this complex tool? What are the actual benefits? Will this save me time, or just take up more of my time?

With complex and slow AutoML tools, it can be nearly impossible to get employee uptake. Using tools like Akkio it's fast and easy for business users to create ML models. You only need to ask employees to spend a minute or two on the product in order to see the benefits. This removes the risk of signing up for software, only to have employees ignore it. In addition, Akkio comes with a free trial, so you can get started immediately and without risk.

No-Code AI Is Scalable

Managing a small sample of datapoints and predictions is easy enough on any infrastructure, but what about when you need to manage thousands or millions of datapoints in real-time? The scalability of your AI solution will be crucial here.

Setting up an on-premises solution is one way to get around the scalability problem, but it’s not the most efficient. Not only do you need to manage your own servers, but you also need to ensure that they have the power and capacity to handle your predicted traffic volume.  In addition, on-premises solutions come with high up-front costs and can be inflexible when you need to make changes.

The traditional alternative of setting up a cloud solution is much more flexible, but it comes with its own set of challenges. For one, you need to ensure that your cloud provider can handle the traffic load and has the capacity to scale up when needed. This can be a complex and time-consuming task, particularly if you’re not using a tool like Akkio that’s built for the cloud.

Akkio is a serverless platform, so you don’t need to worry about provisioning or managing servers. The Akkio platform will automatically scale to meet your traffic needs and provides high availability so that you don’t have to worry about service disruptions.

No-Code AI Enables Rapid Innovation

Innovation requires experimentation, and experimentation requires trying out new ideas quickly without incurring huge development costs. That’s why no-code solutions are critical for businesses that want to move quickly and experiment with different AI use cases.

With traditional AutoML solutions, it can take weeks or months to get a prototype up and running. This iterative process is incredibly important for developers who want to experiment with different models and parameters, but it’s not feasible if you have to wait weeks for each iteration.

No-Code AI Reduces IT Burden

The IT department is often responsible for managing and deploying new software, which can quickly become a bottleneck. With traditional AutoML solutions, the IT department is usually responsible for training models, deploying them, and maintaining them.

This can quickly become a burden for the IT department, particularly if they don’t have expertise in AI or data science. In contrast, no-code solutions like Akkio can be used by non-technical professionals, which reduces the burden on the IT department.

No-Code AI Increases Productivity

The goal of any business is to increase productivity and efficiency, and no-code solutions are a critical part of that equation. By enabling rapid innovation and easy deployment of AI models, no-code solutions like Akkio help businesses achieve their goals.

In addition, no-code solutions free up the time of your technical employees so that they can focus on other tasks. And because no-code solutions are easy to use, you’ll find that employees are more likely to actually use the tool and get value from it.

Summary

At Akkio, we’re focused on making machine learning effortless and affordable for everyone. Our no-code AI platform allows anyone to start using the power of artificial intelligence to grow their business.

But there are other power-user solutions in the market promising to bring machine learning capabilities to businesses of any size. We’ve found that these offerings fall short in a few key ways, namely in their need for technical experts, their slowness, and their high costs. No-code platforms are democratizing machine learning by solving these challenges.

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