How to Launch an Automated Machine Learning Model

Learn how to launch an automated machine learning model without a data scientist. This step-by-step guide makes AutoML tools easy for marketers and analysts.

Shreya Banker

Data Scientist

Data Analyst enthusiast. More than 7 years of exposure in Data Analysis and Software programming. I am a highly motivated, versatile IT professional with experience in Data Analysis, Visualization and Database Management. I look for the hardest problem to solve and where I can learn and develop the most. I love a challenge and never run from a difficult task. I'm determined to succeed, and I look forward to what life has to offer.

Not every business has a data scientist on the team. And that’s okay.

The good news? You don’t need to be a machine learning expert to use the power of AI anymore. Thanks to modern AutoML tools, almost anyone with a laptop and a business problem can build a working ML model without writing a single line of code.

In this blog, I’ll walk you through how you (yes, even without a tech background) can launch an automated machine learning model using no-code tools like Google Vertex AI, from setup to deployment, without writing a single line of code.

First, What is AutoML?

AutoML (Automated Machine Learning) uses artificial intelligence to handle the heavy lifting of model creation.
It automatically:

Prepares your data
Chooses the best algorithms
Tests everything in the background
And gives you a ready-to-use model
Provides easy-to-understand results

Think of it as hiring a data scientist on autopilot - minus the complexity and cost.

What You Need to Get Started

Before you jump in, here’s what you need to get started:

A problem you want to solve (e.g., predict who will buy your product)
A dataset (like an Excel sheet or a Google Sheet with relevant historical data)
A bit of common sense to interpret the results

That’s it. No Python. No math PhD. Just curiosity and a clear goal.

Step-by-Step: How to Launch a Machine Learning Model Without Writing Code

1. Pick a Tool That Does the Heavy Lifting

There are plenty of user-friendly AutoML platforms out there. Here are a few popular ones:

Google Vertex AI (AutoML)
Microsoft Azure AutoML
Amazon SageMaker Autopilot
DataRobot
Akkio (very beginner-friendly)

These platforms are designed for non-technical users. Just upload your file, click a few buttons, and let the system handle the rest.

2. Prepare Your Data

Before uploading, make sure your data:

Is in CSV or Excel format
Has clear column names (e.g., “Customer Age,” “Location,” “Converted”)
Includes a column you want to predict (like "Will Buy = Yes/No")

No need for fancy formatting. Just make sure your data makes sense and isn’t full of blanks or weird symbols.

3. Choose Your Prediction Target

Once your data is uploaded, the system will ask what you want to predict.
For example:

“Will the customer churn?”
“How many products will be sold?”

Simply select the column that represents your prediction goal.

4. Let AutoML Handle the Modeling

Now, sit back. The tool will:

Clean your data
Test multiple models behind the scenes
Compare performance and accuracy
Choose the best one for your problem

Depending on dataset size, this process can take from a few minutes to an hour.

5. Review the Results

Once the model is ready, you’ll get:

An accuracy score (How well it performs)
A list of the most important factors (e.g., age, country, browser)
Charts and explanations (in simple terms)

Even if you’re not technical, you’ll understand what’s working and what’s not.

6. Deploy and Use Your Model

This is where the magic happens.

Most AutoML tools give you an API or a simple way to plug the model into:

Your website or app
Your Google Sheets or BigQuery
Your CRM system
Or even just another Excel file

Basically, you can start using predictions in your daily workflow. No engineers needed.

Want to go further? Unlock Powerful Marketing Insights with Google Cloud

Example: AutoML in Marketing

Let’s say you run a small marketing agency. You have a list of 1,000 past leads. Some converted, some didn’t.

You upload this list to Google Vertex AI, select “Converted” as the thing you want to predict, and hit go. Within an hour, you get a working model that can now predict which new leads are most likely to convert. You can focus your effort on the hot ones and save time and money.

Pretty awesome, right?

A Few Things to Keep in Mind

Bad data = bad results. For example, if half of your customer ages are missing or some sales dates are in the wrong format, your model will get confused and give unreliable predictions. Many AutoML tools (like Google Vertex AI or DataRobot) also help “code” your data — cleaning blanks, standardizing formats, and even flagging suspicious entries before training.
Don’t rely 100% on the model. Always add human judgment.
Check results every few months. Models can go out of date as trends change.

Final Thoughts

Launching a machine learning model used to be a job for data scientists only. But not anymore. With AutoML, you can start predicting outcomes, improving decisions, and saving time, with just your data and a goal.

Whether you’re in marketing, sales, HR, or product, you can now tap into AI without needing to code.

If you’re ready to turn your business data into predictive intelligence, Napkyn’s experts can help you set up, train, and deploy AutoML models in Google Cloud that scale with your goals. Get in touch with us today.

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