AI News, BOOK REVIEW: Automated ML algorithm selection & tuning artificial intelligence

What is automated machine learning?

Automated machine learning is the process of taking training data with a defined target feature, and iterating through combinations of algorithms and feature selections to automatically select the best model for your data based on the training scores.

The traditional machine learning model development process is highly resource-intensive, and requires significant domain knowledge and time investment to run and compare the results of dozens of models.

There are two key aspects to transparency in machine learning: You can enable global feature importance on-demand post training for the pipeline of your choice, or enable it for all pipelines as part of automated ML training.

New automated machine learning capabilities in Azure Machine Learning service

As part of Azure Machine Learning service general availability, we are excited to announce the new automated machine learning (automated ML) capabilities.

Automated ML allows you to automate model selection and hyperparameter tuning, reducing the time it takes to build machine learning models from weeks or months to days, freeing up more time for them to focus on business problems.The making of automated ML was driven by our commitment to improve the productivity of data scientists and democratize AI.

When you are ready to scale, automated ML enables you to run your training jobs in the Azure Cloud by scaling up as well as running multiple training jobs in parallel using Azure Machine Learning compute or Azure Databricks clusters.

With automated ML, you can run your training jobs entirely on your local machine without data ever leaving your computer, complying with data security and protection needs.

Automated ML simplifies many of these data pre-processing tasks by automatically transforming categorical features into one hot encoding, imputing missing values and rows, generating new date time features, and more.

When using the forecasting capability, automated machine learning optimizes our pre-processing, algorithm selection and hyperparameter tuning to recognize the nuances of time series datasets.

Our service expands our support for feature engineering with greater focus on things like grain index featurization and grouping and missing row imputation to provide greater model performance and accuracy.

In the feature engineering part of the modeling stage, you need to transform the input data via techniques such as removing nulls, rescaling, selecting for features, and/or generating new features.

Source: scikit-learn machine learning library In short, data scientists and developers face a series of sequential and interconnected decisions along the way to achieving "magical"

When machine learning solutions need to be updated as data evolves, data scientists would need to repeat the same feature engineering, model training and evaluation process.

Automated ML empowers users, with or without data science expertise, to identify an end-to-end machine learning pipeline for any problem, achieving a high quality machine learning model while spending far less of their time.

By making automated ML available through the Azure Machine Learning service, we're empowering data scientists and organizations to build, deploy and manage the machine learning life cycle from end to end.

This will enable more people in your organization to leverage machine learning and most importantly allow domain experts to rapidly prototype ML solutions and validate their hypothesis before involving data scientists.

If you are an experienced data scientist, automated ML will let you improve productivity and save time by eliminating the need to manually perform the tedious and repetitive tasks of feature engineering, algorithm selection and hyperparameter tuning.

refinery enterprise that’s using automated ML to forecast reservoir production and a medical devices company that’s using automated ML for predictive maintenance.

The approach combines ideas from collaborative filtering and Bayesian optimization to search an enormous space of possible machine learning pipelines intelligently and efficiently.

Automated ML (numbers represent accuracy of pipelines evaluated on datasets) As indicated by the distributions shown on the right side of the figures above, automated ML also takes uncertainty into account, incorporating a probabilistic model to determine the best pipeline to try next.

This approach allows automated ML to explore the most promising possibilities without exhaustive search, and to converge on the best pipelines for the user’s data faster than competing “brute force”

Python is one of the most popular languages for building machine learning solutions due to the availability of numerous libraries such as numpy, matplotlib and machine learning frameworks such as scikit-learn, PyTorch, and TensorFlow.

When building machine learning solutions, data scientists must inspect many attributes of machine learning models to and carefully weigh the trade-offs of each before choosing an optimal for a given business problem.

We want to provide complete control and transparency into all the models that automated ML generates so you can choose the best one for your scenario by making the tradeoffs that make sense for your business problem.

Tutorial: Use automated machine learning to build your regression model

The automated machine learning technique iterates over many combinations of algorithms and hyperparameters until it finds the best model based on your criterion.

Open and run the data flow and review the results: You prepare the data for the experiment by adding columns to dflow_x to be features for our model creation.

You define dflow_y to be our prediction value, cost: Now you split the data into training and test sets by using the train_test_split function in the sklearn library.

The random_state parameter sets a seed to the random generator, so that your train-test splits are always deterministic: You now have the necessary packages and data ready for autotraining your model.

By using the overloads on get_output, you can retrieve the best run and fitted model for any logged metric or a particular iteration: Register the model in your Azure Machine Learning service workspace: Use the best model to run predictions on the test dataset.

Print the first 10 predicted cost values from y_predict: Create a scatter plot to visualize the predicted cost values compared to the actual cost values.

To compare the variance of predicted cost at each trip distance value, the first 100 predicted and actual cost values are created as separate series.

It indicates roughly how far your predictions are from the actual value: Run the following code to calculate mean absolute percent error (MAPE) by using the full y_actual and y_predict datasets.

Then it expresses that sum as a percent of the total of the actual values: [!INCLUDE aml-delete-resource-group] In this automated machine learning tutorial, you did the following tasks: [!div class='checklist'] Deploy your model with Azure Machine Learning.

Analytics and security for machine learning

AWS has the broadest and deepest set of machine learning and AI services for your business.

Our capabilities are built on the most comprehensive cloud platform, optimized for machine learning with high-performance compute, and no compromises on security and analytics.

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