What Is No Code Machine Learning?
No code machine learning (ML) platforms use visual drag-and-drop platforms to automatically build machine learning models and generate predictions without writing a single line of code. These platforms automate the process of data collection, data cleansing, model selection, model training, and model deployment.
No code ML democratizes machine learning. It allows business analysts without ML knowledge or programming experience to build machine learning models and generate predictions to solve immediate problems, such as predicting when customers might churn or when orders will be delivered.
No Code ML Versus Traditional ML
With traditional ML, a skilled data scientist uses a programming language like Python to build a ML model. Data scientists must import datasets and prepare the data for ML using manual and automated data cleansing and feature engineering techniques. They must select a portion of the data to use to train and tune their model before deploying it into production.
Conversely, a no code platform combines the capabilities of cutting-edge ML programming with easy-to-use tools that allow business users to build ML models.
No code ML modeling is different from AutoML. AutoML is a technique used to streamline conventional ML processes. AutoML typically automates data preparation and uses automated processes to identify appropriate algorithms. The primary difference between AutoML and no code ML is that AutoML requires the skills and knowledge of the data scientist, while no code ML does not.
Why No Code ML Is Important
While tools like Amazon SageMaker are designed for data scientists and ML engineers to build, train, and deploy ML models for any use case with fully managed infrastructure, tools, and workflows, business analysts also need to innovate with ML.
No Code ML bridges that gap and brings automated machine learning to business analysts so they can generate predictions.
How Do No Code Machine Learning Tools Work?
Most no code ML tools have a simple graphical or drag-and-drop interface. These allow you to connect to data sources by simply dragging the data icon into the interface or clicking on the file. Once data is imported, no code platforms clean and transform the data, so it's ready for ML.
No code ML platforms simplify algorithm selection. While in some instances, you will select algorithms from drop-down lists, in others, the platform runs automated selection algorithms to find the best algorithm for your data. The platform automatically trains the model and provides statistics regarding prediction accuracy and features that most influence the outcome. Once trained, you can use no code ML models for generate predictions.
How Can You Leverage No Code ML Tools?
You can leverage no-code ML to answer urgent questions. For example, marketing analysts can use no code ML to evaluate sales leads and predict which have the highest conversion potential. Finance analysts use no code ML to evaluate the credit risk of new customers or to predict revenue growth. In manufacturing, production analysts can use no code ML to predict capacity constraints, while logistics analysts may prepare ML models to determine optimal shipping routes.
No Code ML with Amazon SageMaker
Amazon SageMaker Canvas expands access to ML by providing business analysts with a visual point-and-click interface that allows them to generate accurate ML predictions on their own — without requiring any machine learning experience or having to write a single line of code.
You can quickly connect, access, and combine data from cloud and on-premise data sources, automatically detect, cleanse, and analyze data, create ML models with the click of a button, and generate single or bulk predictions. You can also collaborate and send models to data scientists using SageMaker Studio for review and feedback.
To get started with SageMaker Canvas, explore the tutorial.
