Can AutoML really simplify the tedious process of selecting the best ML model for any task? 🤔
Spoiler alert: It can. But what’s more exciting? Building and understanding AutoML systems while testing their capabilities!
AutoML (Automated Machine Learning) is like having a personal assistant for your ML tasks. It handles the boring, repetitive parts of the ML pipeline, so you can focus on the fun stuff like strategy, analysis, and domain-specific problem-solving.
If you’ve ever worked on machine learning projects, you know the pain:
- Trying 50+ model configurations and praying one works. 🙏
- Endless hyperparameter tuning that feels more like luck than science. 🌀
- Losing hours to repetitive tasks when you’d rather be innovating.
AutoML steps in to automate:
- Model training (with model selection and hyperparameter tuning).
- Evaluation and validation (ranking the top models for your data).
It frees you up to focus on other parts of the pipeline, like understanding data, feature engineering, and serving your models in production.
The ML pipeline looks like this:
- Data Cleaning
- Feature Engineering
- Model Selection (AutoML’s playground 🛠️)
- Hyperparameter Tuning (AutoML shines here 🌟)
- Model Evaluation and Validation
- Serving and Monitoring
AutoML tools can automate everything from Step 3 onward, giving you more time to innovate!
We know there are plenty of AutoML tools available, from fancy commercial platforms like H2O Driverless AI and DataRobot to open-source lifesavers like TPOT, AutoKeras, and Scikit-learn’s AutoML. But here’s the catch—these tools automate their way, not necessarily your way.
What if we could:
- Explore existing AutoML tools to understand how they simplify the pipeline.
- Learn their strengths and limitations.
- Build our own AutoML system from scratch tailored to our needs!
TPOT is like your ML co-pilot. It automates pipeline design by using genetic algorithms to evolve the best model.
- Implementation: Use TPOT to train and optimize models for a sample dataset.
- Evaluation: Analyze how well TPOT identifies top-performing models and automates hyperparameter tuning.
- Goal: Just see the features of TPOT and what kind of accuracy we get in first pass using TPOT.
H2O AutoML is a robust framework for automating end-to-end ML workflows. It’s perfect for handling large datasets efficiently.
- Implementation: Explore H2O’s driverless capabilities to run an automated ML pipeline.
- Evaluation: Check how it handles feature engineering and its accuracy in selecting models.
AutoKeras specializes in Neural Architecture Search (NAS), making it ideal for deep learning tasks.
- Implementation: Use AutoKeras for an image classification task.
- Evaluation: See how it automatically tunes complex deep learning models.
Let’s take it to the next level!
We’ll create an AutoML system from scratch to:
- Automate model selection and hyperparameter tuning.
- Rank the top-performing models.
- Generate pipelines for evaluation and validation.
- Search Algorithms: Implement grid search, random search, or Bayesian optimization to find the best models.
- Custom Pipelines: Build modular pipeline blocks for feature engineering, training, and evaluation.
- Performance Metrics: Optimize for accuracy, precision, recall, or multi-objective metrics.
- Meta-Learning: Add meta-learning to predict model performance based on dataset characteristics.
Once we’ve implemented these tools and our own AutoML system, we’ll:
- Compare the results of TPOT, H2O, AutoKeras, and our custom solution.
- Document strengths, weaknesses, and use cases for each.
- Publish the entire journey, including code and insights, for the ML community to learn and improve upon.