Top 8 Low Code/No Code ML Libraries and Platforms for Data Scientists in 2026

Machine learning continues to grow rapidly, with new breakthroughs and applications emerging all the time. However, developing ML models has traditionally required deep knowledge of algorithms and strong coding skills. This creates a high barrier to entry.

In recent years, a new category of tools has emerged to make machine learning more accessible to a wider audience – low code and no code ML libraries and platforms. These tools allow data scientists, analysts, and even non-technical business users to train and deploy ML models without needing to write all the underlying code themselves.

Low code ML libraries provide pre-built modules and functions that automate much of the coding required, but still allow some customization and coding when needed. No code ML platforms go a step further and provide fully visual drag-and-drop interfaces for training models without writing a single line of code.

These tools can significantly speed up the model development process and make machine learning viable for a wider range of projects and companies that may lack extensive data science resources. Of course, they don‘t eliminate the need for ML knowledge – understanding the fundamentals, cleaning data, and interpreting results is still crucial.

As we enter 2024, low code and no code ML options continue to advance rapidly. Here are 8 of the top libraries and platforms that data scientists should know about:

Low Code ML Libraries

PyCaret

PyCaret has established itself as one of the leading open-source, low-code ML libraries in Python. It automates the entire ML pipeline from data preparation to model deployment, allowing you to train and compare models with just a few lines of code.

Some key features of PyCaret include:

• Support for all data types including text, images, and time-series
• Over 50 ready-to-use algorithms across different model classes
• Built-in data preprocessing, feature engineering, model evaluation, and explainable AI
• Seamless model deployment to cloud platforms
• Full flexibility to tune models when needed

Here‘s an example of just how easy it is to train and deploy a model with PyCaret:

from pycaret.classification import *

# Initialize setup
clf = setup(data = train, target = ‘Survived‘)

# Train and validate model
top3 = compare_models(n_select = 3)

# Finalize model for deployment 
final_model = finalize_model(top3[0])

# Predict on new data
predictions = predict_model(final_model, data=test)

# Deploy model 
deploy_model(final_model, ‘titanic-survival-model‘)

In version 3.0 released in 2023, PyCaret added several powerful new features including advanced time-series forecasting, image classification and detection, and natural language processing (NLP) deep learning models.

H2O AutoML

H2O AutoML is an open-source library that automates the ML pipeline, supporting both Python and R. It‘s hugely popular for Kaggle competitions.

Some key features include:

• Automatic training and stacking of models
• Automatic hyperparameter tuning
• Ability to handle categorical features and missing values
• Exportable models for deployment

Here‘s an example usage:

import h2o
from h2o.automl import H2OAutoML

h2o.init()

# Import data
train = h2o.import_file("train.csv")
test = h2o.import_file("test.csv")

# Run AutoML 
aml = H2OAutoML(max_models=10, seed=1)
aml.train(x=train.columns, y=y, training_frame=train)

# Leaderboard
aml.leaderboard

# Predict on test data
preds = aml.predict(test)

The latest H2O version 4.0 released in 2024 added integration with NVIDIA GPUs for even faster training, a new AnalyticsGUI for no-code usage, and enhanced model explainability.

AutoGluon

Developed by AWS, AutoGluon enables easy-to-use and easy-to-extend AutoML with a focus on deep learning and real-world applications. It automates model selection, hyperparameter tuning, iterative ensemble construction, and model deployment.

Some key features:

• Supports image, text, and tabular data
• State-of-the-art neural architecture search
• Multi-layer model stacking and ensembling
• Automatically tunes hyperparameters of individual and ensemble models
• Trains both on CPU and GPU

from autogluon.tabular import TabularPredictor

train_data = TabularDataset(‘train.csv‘)
test_data = TabularDataset(‘test.csv‘)

predictor = TabularPredictor(label=‘class‘).fit(train_data, hyperparameters = ‘auto‘)

predictions = predictor.predict(test_data)

No Code ML Platforms

Akkio

Akkio is a powerful no-code ML platform that allows anyone to build and deploy models in minutes. Users can connect data from spreadsheets, databases, or cloud storage, then train a model by simply selecting the data to use and the column to predict.

Some key features:

• Automated data prep and feature engineering
• Supports tabular data, images, text, and time-series
• Dozens of pre-built models to choose from
• Automatic model tuning and selection
• Models can be deployed via API, app, or embedded

Google Cloud AutoML

Google Cloud AutoML enables developers with limited ML expertise to train high-quality models. It provides a simple GUI to upload data and train models. Some key features:

• Supports images, text, tabular data, and video
• Transfer learning from Google‘s pre-trained models
• Automatic algorithm selection and hyperparameter tuning
• Deployable model in containers

Lobe

Recently acquired by Microsoft, Lobe is a no code deep learning platform that focuses on making computer vision and deep learning accessible to everyone. Users can simply drag and drop in datasets of images, then use a visual interface to architect the neural network and train the model.

Some key features:

• Simple visual interface
• Real-time model performance feedback
• Transfer learning from pre-trained models
• Export models to various formats
• TensorBoard integration

Conclusion

Low code and no code ML tools have revolutionized the field of data science, making machine learning accessible to a much broader audience. The 8 libraries and platforms we‘ve covered are some of the best options available in 2024 for data scientists looking to speed up model building and deployment.

To summarize, for data scientists that still want to write some code and have full flexibility, the low code libraries PyCaret, H2O AutoML, and AutoGluon are excellent choices. For business users and analysts looking to leverage ML without coding skills, platforms like Akkio, Google Cloud AutoML, and Lobe provide intuitive visual interfaces.

Ultimately, the right choice depends on the specific use case, data types, level of expertise, and deployment needs. But with these powerful tools in your toolkit, the barrier to building effective ML models is lower than ever. Data scientists should explore these low code and no code options to supercharge their productivity and impact in 2024 and beyond.