How Low Code Platforms Can Bolster Machine Learning Projects

For developers, data scientists, and business analysts looking to improve the outcome of their machine learning projects, let’s look at how low code platforms are playing a big role in meeting their needs and speeding up ML projects.

Is your data scientist team looking to speed up your machine learning projects? Or spend more time experimenting with different ML algorithms and less time maintaining or debugging code? Or, are you considering integrating ML into your business processes yet don’t have the resources to hire a slew of data scientists and engineers? Then, you may be ready to consider low code machine learning platforms for your next project. 

How Low Code Platforms Can Speed ML Model Deployment

Traditional machine learning model development and deployments are complex, time-consuming and expensive, requiring hard-to-find skilled ML professionals. Low-code platforms can help you build ML models faster and at a lower cost by reducing the time it takes to learn how to use the tools effectively. They also feature pre-built components to help speed up development. The infrastructure is already set up and managed. Not having to manually code algorithms can provide results in seconds instead of days or weeks. Some platforms even come with pre-built, pre-trained deep learning models that can be used to classify images. Low code allows developers to integrate their custom models into the platform while spending less time on repetitive coding tasks. Because they spend less time writing code, they can focus on optimizing their app’s functionality. 

Low code platforms allow developers to access their pre-built components to customize the code or add additional functionality. While low code platforms will never completely replace manually-coded algorithms, low code’s pre-built components can relieve even the most skilled data science team of debugging and maintaining thousands of lines of code inherent in manually produced ML algorithms. 

See More: 5 Trends That Will Take Low Code Platforms to the Next Level in 2021

Low Code vs. No Code

Businesses across all industries are coming to realize the value of no code ML platforms that can help them incorporate ML into their business processes quickly, efficiently, and without having a data science team. No code ML platforms are for non-programmers looking to make data predictions without acquiring ML technical skills. With no-code platforms, they can build ML models using a drag and drop user interface, restricting them from accessing and modifying the platform’s pre-built components or algorithms. Business analysts can now create ML models to make data predictions on their own.

Both low code and no code machine learning platforms can help build AI applications from pre-built components. Both reduce the amount of coding for building ML applications by dragging and dropping ML elements from a library. However, low code platforms do not prevent you from customizing their pre-built algorithms by writing your code. As the name suggests, no code platforms require no programming, so they can be used by non-programmers who need ML functionality in their work, such as artists, managers, business analysts, or scientists. So while they allow you to build ML apps quickly, no-code platforms do not let you modify the platform’s code or add your own. 

How Do No Code/Low Code Platforms Work

With traditional ML model training platforms, your goal is to find the algorithm which gives the best results for your particular problem or dataset. You do this by using different models against an appropriate dataset and specific analysis parameters to determine the model that gives you the most accurate results. Model experimentation takes a lot of time and effort when done manually. Low code/no-code platforms automate this process. Your datasets are automatically run through different machine learning models until the best model with the most accurate result is found. 

There is no need to preprocess the dataset; these platforms automatically clean your data, classify the variables, and perform feature reduction on any number of different types of variables within a single model. Preprocessing includes removing rows or columns with null values, upsampling and downsampling the data, and normalizing columns to improve accuracy, such as where different ranges exist between columns. 

Top No Code Platforms for Business Analysts

There are dozens of no-code platforms available, and more are being developed every day. Here are four popular platforms in use today:

Obviously AI

Obviously AI provides:

• Classification and regression algorithms for predictive analysis.
• Forecasting revenue
• Optimizing supply chains
• Developing personalized marketing campaigns

Users upload the corresponding dataset, mark the column for prediction, and enter the model’s question to solve in natural language. Obviously AI chooses from among its pre-built algorithms to train the model for you.  

MonkeyLearn

MonkeyLearn provides pre-built ML models trained for text analysis that can be deployed in applications that conduct sentiment analysis, perform topic classification, or detect specific elements in unstructured text. 

Google Cloud AutoML

Google Cloud AutoML is a cloud-based no-code technology that lets developers with little or no machine learning experience build and incorporate machine learning algorithms that are tailored to their company requirements into their websites and applications. Object recognition and picture categorization in the cloud or at the edge, natural language processing and translation, and organized data analysis are only some of the applications.

DataRobot

Designed for business analysts, DataRobot uses automated ML to generate predictive models. Its user-friendly interface lets nonprogrammers create their ML models in a few steps. 

See More: Adoption of Low Code Development Tech to Boom in 2021: Gartner

Top Low Code Platforms for Developers/Data Scientists

For developers and data scientists looking to enhance their ML projects, here are four low code platforms to consider: 

Pycaret

Pycaret is described as “an open source, low-code machine learning library in Python that allows you to go from preparing your data to deploying your model within minutes in your choice of notebook environment.” It is “essentially a Python wrapper around several machine learning libraries and frameworks such as scikit-learn, XGBoost, Microsoft LightGBM, [and] spaCy. ” 

Pycaret lets you train, test and deploys ML models, performing end-to-end ML experiments with fewer lines of code. It automates many preprocessing features, including data preparation, scale and transform, feature engineering, feature selection, and cluster creation. 

H2O AutoML

H2O AutoML is an open-source machine learning platform that lets you train a model with only a few lines of code in R or Python. In-depth knowledge of ML algorithms is not required. It supports some of the most widely used ML algorithms, including gradient descent, linear regression, and deep artificial neural networks. AutoML automates data preprocessing and model training and fine-tuning and can build and test multiple models simultaneously. 

AutoViML

AutoViML is a low-code library for creating interpretable models with high performance. It executes many models with different characteristics at the same time to identify the best solution for your dataset. It performs data cleaning and category feature transformation, along with feature selection and feature engineering. Different types of variables are supported, including textual, numeral and visual data.

Microsoft Power Automate AI Builder

AI Builder provides a number of pre-built models for such applications as sentiment analysis, text recognition and translation, category classification, and keyphrase extraction. It also includes custom prediction, entity extraction, category classification, form processing and object detection models that you can build and train for your specific business requirements. 

When to Choose Low Code Over No Code

All low/no-code platforms are not alike. They run the gamut from no-code platforms confined to working with simple applications for machine learning with a limited set of pre-built algorithms to fully functional, automated platforms for end-to-end ML model development, training and deployment.

While no-code platform vendors often promise their platforms can do anything traditional machine learning processes can do without an extensive team of data scientists, and the time it takes to come up with predictions with manual coding, most no code platforms are limited to a specific set of algorithms. No code platforms are typically designed for start-ups, medium-sized companies or individuals that need to work with the insights gained from machine learning models but do not have a data science team to build the models for them. No code platforms are not intended for use in custom ML model development for data-intensive projects for which low-code are better suited. 

Do you think low code platforms have matured enough to merit adoption by enterprises? Let us know on LinkedInOpens a new window , TwitterOpens a new window , or FacebookOpens a new window . We would love to hear from you!