No-Code AutoML solutions: How can they help you innovate with ML?

Everywhere we look, AI/ML is powering most of the tools that we interact with daily, from Siri to personalized movie recommendations on your favorite streaming site, all the way to the Roomba you have running in the background while you work. Let’s face it, AI/ML plays a significant role in the innovations that we love and rely on.

Machine learning (ML) enables computers to tackle tasks that have, until now, been performed by people. Tackling the monotonous to the highly complex, and doing so faster and with fewer mistakes than their human counterparts.

It's no wonder then, that ML is often powering the imagination of entrepreneurs and innovators, who look to solve problems and build disruptive innovations to stay ahead of the competition.

Entrepreneurs are time-poor and, often, resource-poor, so getting from A to B with speed is not a nice-to-have, for entrepreneurs, but a must-have. Enter AutoML solutions, capable of speeding up the traditional process of developing and deploying ML models, meaning faster time-to-value for you and your idea.

Where do I sign up, you might say?

Not so fast! A quick search for AutoML platforms will uncover an ocean of options to wade through. With all these different AutoML solutions, some boasting no-code capabilities or end-to-end functionalities, how can you know where to start, which to choose, and what they can do for you?

To help out, here are 3 of the top ML providers:

• DataRobot

• H20

• Google Cloud AutoML

If you want in-depth comparisons, we have articles comparing the AI & Analytics Engine with:

• AWS Glue DataBrew,
• AWS SageMaker Canvas,
• Trifacta, 
• H2O.AI's Driverless AI
• Dataiku

What is AutoML?

First off, what is AutoML? Simply put, an AutoML solution automates a portion of or all the steps of the machine learning process to take you to model deployment. AutoML also ticks the affordability box, requiring fewer resources to uphold performance - making it power-efficient and cost-effective. Unlike traditional machine learning processes, with AutoMLs, skilled professionals are not necessarily required to build and operate the models. We go further into AutoML in this article on the Democratization of Data Science. 

Some AutoML solutions work well for users with minimal or basic ML knowledge, enabling them to leverage full ML capabilities without the need to be proficient in coding. Too good to be true? Say you’re an entrepreneur who wishes to incorporate ML into your business to make predictions, recommendations, or gather insights. Looking to an AutoML solution can help you get to your goals faster, even without a designated Data Science team. We’ll show you how.

What if I can’t code?

With ML’s popularity growing, and companies jumping on board the AI/ML bandwagon, the number of data scientists has jumped 76% between 2020 and 2021¹. Data Scientists aid companies implementing AI/ML, as specialists with ML expertise. But what about the rest of us who can’t code nor afford a team of data scientists? That’s where AutoML platforms, specifically no-code ones can help.

The AI & Analytics Engine is a no-code AutoML platform that simplifies and speeds up the entire ML process from raw data to model deployment. Let's take you through the seamless process:

6 Steps with the AI & Analytics Engine

Step 1: Upload your data onto the Engine.

Step 2: Unlike traditional ML methods, there is no need to clean or prepare your data manually. The Engine comes equipped with a Smart Data Preparation feature that provides AI-powered recommendations to get your data ML-ready. Use this feature to create a recipe of actions to clean and wrangle your dataset.

Step 3: Once your data is ML-ready, select the important features that you have identified and want to serve as the input for predicting your target column.

Step 4: From your configurations in Step 3, the Engine’s Model Recommender recommends ML models for you to predict your target column. The best part? The Engine ranks these models according to their predicted performance on the dataset. So all you have to do is choose the models with the best-predicted performance for your business goal and train them.

Step 5: With a straightforward and user-friendly UI, compare and evaluate the models' performances to identify the ones that performed the best.

Step 6: Choose your desired model, and it is ready for deployment with just a click. Literally. We have a one-click deployment process!

You’re done!

The biggest effort on your part is to ensure that you’ve gathered good quality data to train your model on.  

What exactly can you use no-code AutoML solutions for?

With AutoML solutions, entrepreneurs have the ability to create machine learning models to make predictions. At their fingertips. All these in minutes, as opposed to days and weeks. Bonus: There’s no need for any data science expertise or machine learning specialists if you take this no-code route.

You can power all types of products and ideas with machine learning. Take, for example, Grape Base, a food and wine pairing app. Using machine learning, this app predicts the quality of a bottle of wine, based on historical data, and the meal it pairs best with. Combining wine and machine learning - genius!

In the realm of content and language, startups have emerged utilizing language. For instance, Textio Hire uses AI/ML to analyze the language used in job postings. The company’s algorithm makes predictions on the right use of language and formatting in job postings, enabling companies to reach the right candidates and encourage more applicants.

HealthTech firms are starting to realize the hidden value in the vast data captured by wearable fitness devices. Using machine learning, predictions on patients’ general health or predisposition to diseases can be made with such data. Period tracking apps are one common HealthTech use case. Take, for example, Flo. With the data that users input into the app, Flo uses AI to make calculated predictions on a user’s period cycle and expected symptoms.

All you need is the right data. With the right data in hand, anyone with a great idea can build a business, based on the fundamentals of predicting a value or an outcome. The possibilities are endless. Read this article to make sure you're asking all the right questions before starting on your AI/ML project! 

For a longer list of use cases, check out our article on the top AI use cases for leading industries.

Why is AutoML key to innovating with ML?

Accessibility in cost and low knowledge barrier

AutoML solutions are making ML accessible to everyone, regardless of their technical knowledge. Whether you’re a bootstrapped entrepreneur looking for a low-cost solution or a creative who lacks the right technical knowledge, no longer are you hindered by your lack of coding proficiency or light resources. AutoMLs make it possible for you to innovate your product with ML at a fraction of the cost it would take to fund a team of specialists.

Entrepreneur, Chris Hall, is a testament to that fact:

Have a great product idea or feature that you think ML can power?

Innovating your idea with ML-based products or features can help you stay competitive. With differentiated features, you can serve the growing expectation of a digital-first and time-sensitive customer base. There is no end to the number of problems ML-based startups are solving. So, why not join other innovators using AutoML solutions like the AI & Analytics Engine to do that?

Not sure where to start with machine learning? Reach out to us with your business problem, and we’ll get in touch with how the Engine can help you specifically.