Automated Machine Learning
For Data Scientists and Machine Learning Engineers
Automates data preprocessing, model building, and hyperparameter tuning to efficiently identify the optimal model.
Accessible For Beginners, Advanced For Experts.
Automated to save time, but flexible enough to keep you in control. Beginners get a smooth start, and experts stay in control where it matters.
Stuck in Setup?
Forget environment errors and tricky installations - get straight to building models, no coding required.
Frustrated by Time-Consuming Processes?
Automate the grunt work and focus on creative problem-solving, whether you're learning or refining advanced models.
Need to Deliver Faster?
Experiment, iterate, and deploy quickly-with automation that speeds you up, not holds you back.
For Anyone Who Knows the Struggle Is Real
We've all been there:
ValueError: Input contains NaN, infinity, or a value too large
When you realize you forgot to clean your data.
NotFittedError: This <estimator> instance is not fitted yet
Because you called .predict() before .fit()—again.
ValueError: X has 5 features, but StandardScaler is expecting 6
When your training and test datasets don't play nicely.
ConvergenceWarning: Stochastic Optimizer reached max iterations
Your model refuses to converge no matter how many iterations you throw at it.
MemoryError: Unable to allocate X GiB for an array
Trying to load just one more dataset, and your machine taps out.
ModuleNotFoundError: No module named 'sklearn'
When your virtual environment ghosts you.
We've been through the same frustrations - so you don't have to.
Curious How It Works?
See ApX Automate Your ML Pipeline in Action.
From our blog
Nov 5, 2024
Feature engineering is one of the most critical steps in the machine learning pipeline, often determining the success or failure of a model. This guide dives into what feature engineering is, why it matters, and shares practical tips and tricks for data scientists to create impactful features for high-performing models.
Oct 18, 2024
Mastering tabular data on Kaggle requires knowing which machine learning models deliver the best performance. Explore 7 popular models like XGBoost, LightGBM, CatBoost, and more. Understand their strengths, key features, and when to use each to improve your Kaggle competition results.
Oct 15, 2024
Discover what AutoML (Automated Machine Learning) is, how it simplifies machine learning model creation, and why it's transforming the way businesses approach AI. Learn the benefits, challenges, and popular tools in the AutoML landscape.
