If you Google “learn machine learning,” you’ll find a bunch of guides, online courses, and such that walk you through the coding languages of ML and the processes it takes to solve data predictions. You even get this futuristic robot with the list of steps. Basically, you conclude very quickly it takes a lot of time to learn technical machine learning.
And it does. Mastering ML is super hard and requires a time investment if you want to apply it to solving problems for your business.
However, something that doesn’t come up that often in Google, is “no-code machine learning.” Compared to the keyword “learn machine learning,” which has the volume of around 2K searches/month (according to Ahrefs), “non-technical machine learning” Ahrefs doesn’t even have enough data to get an accurate search volume—meaning the search volume is very low.
This is crazy to us, being a no-code machine learning platform because ML doesn’t just have to be reserved for technical programmers anymore. Analysts now have the power of data predictions and the ability to move quicker, cheaper, and more creatively with no-code machine learning.
Traditional ML vs the No-Code ML Process
The Traditional Machine Learning Process
When searching for how to approach the machine learning process you may come across this post (or a post like it) and get close to ten steps on how to collect data, build a model, train it, improve it, etc.
Typically the traditional model looks like this:
The No-Code Machine Learning Process
No-code ML simplifies this process into this:
This is a much simpler route for someone looking to make data predictions without taking the time to master technical machine learning skills.
With drag and drop data predictions, you can simply edit your queries by replacing identifier and prediction columns and setting aside columns you don't want to use.
This allows you to quickly predict metrics like churn, LTV, the tenure of a contract etc.
Use Cases With No-Code Data Predictions
Going into the 2nd year of this blog, we've published some crazy use cases.
Usually, we find inspiration finding Kaggle notebooks, which use traditional machine learning tactics and build models surrounding current topics.
We do the same, except take a no-code ML approach.
We've even compared Google ML's "No-Code" process to our own and avoided wasting time with technical documentation and training algorithms.
As we produce more and more use cases, the point we want to make is whatever you want to predict, you can as long as you have the data.
How No-Code Algorithms Work
We have dedicated a post to this, but to reiterate, our no-code algorithms aren't just "out of the box" algorithms, they are custom built and trained based on your prediction.
Here's a rundown on how no-code algorithms speed up the traditional ML process from the moment you start predicting in Obviously AI.
1. Preprocessing/Feature Engineering/Normalization
Once you press “Go” to make a prediction, Obviously AI begins the preprocessing phase where it essentially turns raw data into inputs the machine learning algorithm can understand. It removes rows or columns with empty/null values, feature columns with too many unique non-numeric values, upsamples and downsamples the data, and finally runs several other processes to make your data machine learning ready. This is also called Feature Engineering and is a popular ML term to improve ML model accuracy.
Obviously AI also performs normalization where it changes the values of the numerical columns to get more accurate ranges. Not every dataset requires normalization, but it is mainly used to improve accuracy when there are two very different ranges. Say for example, there’s a column of Age and a column of Salary. These columns will have two very different ranges. Age will primarily be number 0 to 100 and Salary could be anywhere between $40K to $1M. We don’t want the column with the larger range to influence the smaller range and make the prediction inaccurate, so we normalize the data and put it on a similar scale to Age.
2. Training Models
This is where machine learning gets technical.
Think of building an algorithm the same way you would trying to make music on a synthesizer. When you take a synthesizer out of the box, there’s pre-loaded settings inside of it. The same is true for algorithms. There are basic algorithms that are essentially a blank canvas. Each algorithm has different settings. Think of these settings as knobs or buttons on a synth—the same way you would Attack, Release, Decay, etc. A professional musician could take these pre-set sounds and find the most fitting one they want for a track pretty quickly, compared to a beginner. Think of Obviously AI as this professional musician that takes a pre-set algorithm and tries out thousands of permutations based on the dataset’s properties and finds the right combinations on the fly for optimized accuracy. This is music to a non-technical business user’s ears because they might be a ML beginner and it would take a while to build the most accurate algorithm.
All the user has to do is enter a query and press “Go.”
3. Testing for Accuracy
On top of the previously mentioned processes Obviously AI performs for accuracy, we also take an extra step to improve the accuracy of your Prediction Report.
While Obviously AI is testing your dataset, it sets aside a section of rows to test separately for consistency. For example, out of a 1,000 row dataset, it separates 100 rows and tests them for the same accuracy as the rest of your dataset. This ensures that the algorithm is accurate for all of your data—even out of context of the 1,000 rows.
AND the crazy thing about these 3 steps is this all happens in 30 seconds or less.
As of now, we use classification and regression algorithms and our adding two more algorithms very soon to make our platform more powerful.
We’re Making Data Predictions More Accessible to SMBs
With a tool like Obviously AI, you can become a data-driven company without having a data science team or scaling one. We’re challenging the traditional approach of learning technical machine learning and introducing more accessible non-technical machine learning.
With no-code machine learning you:
Become data-driven without a data science team - Most companies that want to be data-driven don’t often have a data science team or cannot scale one. This means finding data science talent and shifting around a budget to offer salaries to data scientists or analysts become common roadblocks in their day-to-day work. In such scenarios Obviously AI, a no-code ML tool, can offer a great alternative and provide results in seconds rather than days/weeks.
Can create ML-driven products and scale them - Customers now want personalization, efficiency, and content and product curation. To do that, products need data input and output that appeals to the user’s needs. Data predictions can help your product improve UX and allow you to make more informed business decisions about your product.
Eliminate costs while improving profit - Referencing our post on dynamic pricing, this is just one example of many of how to apply ML to increase profit opportunities. You can also use historical data to make predictions on where to cut costs and improve customer retention.
No-Code Machine Learning is Fast, Simple and Economic
As you can see, the no-code machine learning process is much shorter and simpler than the traditional process which also requires the learning of technical skills. With a no-code machine learning platform, you can do all of the same things—except without the expensive team of data scientists and improve business and product decisions quicker.
Most importantly, no-code machine learning allows you to be more creative with your data. If you’re interested in learning more about creativity and machine learning, keep reading our blog as that’s our main mission of creating these terrific posts for you.
OR get started with no-code machine learning here to make predictions from your data.