Why AutoML is Key to Entrepreneurs Innovating with ML

It is not unusual to see people having a conversation with Siri on the street or for self-driving trucks to be diligently delivering freight across the country. Machines have been woven into the daily fabric of our lives for many a year, and lately, with machines, comes machine learning.

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, who look to solve problems and build disruptive innovations.

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, 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?

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

  • DataRobot
  • H20
  • Google Cloud AutoML

If you want a more in-depth comparison, we have an article on the AI & Analytics Engine vs. AWS Glue DataBrew.

Hold up. What is AutoML?

AutoML workflow vs. Traditional ML workflow

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. Additionally, AutoML makes the processes cost-effective as 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.

Sure, AutoML sounds great. But what if I can’t code? How will this work?

6 Steps with the AI & Analytics Engine to take you from raw data to model deployment

Steps 1–3 of 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.

Steps 4–6 on the AI & Analytics Engine

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.

With an AutoML solution like the AI & Analytics Engine, you’ve got Machine Learning at your fingertips. Need more information? We have an article on how you can get started with machine learning without any coding!

Ready to start turning your data into predictions? Sign up for a 2-week free trial of the Engine!

So, what exactly can entrepreneurs use AutoML for?

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.

So why is AutoML key to entrepreneurs innovating with ML?

Accessibility in cost and low knowledge barrier

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

“For a small, young start-up, the return on investment in the AI & Analytics Engine has been almost immeasurable. The Engine gives us the equivalent of a data science and development squad at a fraction of the cost.” — Chris Hall, Founder of Wine Grounds

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

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

Originally published at https://www.pi.exchange.



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