Introduction to Machine Learning on the Cloud

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This workshop will be both a gentle introduction to Machine Learning, and a practical exercise of using the cloud to train simple and not-so-simple machine learning models. We will start with using Automatic ML to train the model to predict survival on Titanic, and then move to more complex machine learning tasks such as hyperparameter optimization and scheduling series of experiments on the compute cluster. Finally, I will show how Azure Machine Learning can be used to generate artificial paintings using Generative Adversarial Networks, and how to train language question-answering model on COVID papers to answer COVID-related questions.

This workshop has been presented at ML conf EU 2020, check out the latest edition of this Tech Conference.

FAQ

Azure Machine Learning is a cloud-based service from Microsoft that provides various tools and services specifically designed for machine learning tasks. It allows users to create, train, and deploy machine learning models using a variety of tools, including virtual machines, clusters, and pre-trained models.

To start using Azure Machine Learning, you need to create an Azure Machine Learning workspace. This can be done through the Azure portal by selecting 'Create a resource' and searching for 'Machine Learning Workspace'. Once created, you can launch the Azure Machine Learning Studio to access various tools and services.

Azure Machine Learning can be used in multiple ways: 1. Using pre-built services such as AutoML and Designer for no-code and low-code solutions. 2. Running custom scripts and experiments from Visual Studio Code or Jupyter Notebooks. 3. Training and deploying models using clusters and virtual machines. 4. Utilizing cognitive services for tasks like computer vision and speech-to-text.

AutoML, or Automated Machine Learning, is a feature in Azure Machine Learning that automatically tries different machine learning models and hyperparameters to find the best model for a given dataset. It simplifies the process of model selection and tuning without requiring extensive knowledge of machine learning.

To use AutoML in Azure Machine Learning, follow these steps: 1. Upload your dataset to the Azure Machine Learning workspace. 2. Navigate to the 'Automated ML' section in the Azure Machine Learning Studio. 3. Create a new AutoML run by selecting your dataset, specifying the target column, and choosing the type of machine learning task (e.g., classification, regression). 4. Choose the compute cluster for running the experiments and start the AutoML run.

Azure Databricks is a big data analytics service that provides a fast, easy, and collaborative Apache Spark-based analytics platform. It is designed for big data processing and analytics, allowing users to process large datasets using clusters. Azure Machine Learning, on the other hand, is focused on building, training, and deploying machine learning models. While Azure Databricks is more suited for big data tasks, Azure Machine Learning is tailored for machine learning workflows.

Yes, you can use Jupyter Notebooks with Azure Machine Learning. You can create or upload Jupyter Notebooks in the Azure Machine Learning Studio and run them using compute instances. This allows you to write and execute Python code for data analysis, model training, and experimentation directly within the Azure Machine Learning environment.

The Azure Machine Learning workspace is a central place that contains everything needed for machine learning tasks. It includes data storage, datasets, compute resources, experiments, and models. The workspace allows users to organize and manage all aspects of their machine learning projects in one place.

To deploy a trained model in Azure Machine Learning, follow these steps: 1. Register the trained model in the Azure Machine Learning workspace. 2. Create a scoring script that defines how the model should be used for predictions. 3. Define the environment and dependencies required for the model. 4. Deploy the model to a compute target such as an Azure Kubernetes Service (AKS) cluster or an Azure Container Instance (ACI).

Dmitry Soshnikov
Dmitry Soshnikov
146 min
22 Jul, 2021

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Video Summary and Transcription

The video discusses the use of Azure Machine Learning for training models in the cloud. It highlights the importance of using low priority VMs in Azure to save costs and the need to structure training scripts to be resumable. The video explains how to create a dataset from web files and use the Titanic dataset as an example, emphasizing the importance of selecting relevant features like gender, age, and class for model training. Azure Machine Learning provides tools like AutoML and Designer for no-code model training, making it accessible for beginners. The video also covers the use of Jupyter Notebooks for connecting to the data store and running models, as well as the process of scheduling experiments on clusters for collaborative projects. Advanced topics such as hyperparameter optimization, distributed training, and the use of GANs for generating paintings are also discussed. The speaker provides practical tips on using Azure ML efficiently, such as enabling SSH access, choosing the right virtual machine type, and avoiding unnecessary costs by deleting resources after use.
Video transcription and chapters available for users with access.

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