Machine learning is a subset of Artificial Intelligence. It is the process of training a machine with specific data to make inferences. In this post, we are going to cover everything about Automated Machine Learning in Azure. This topic is covered in [AI-900] Microsoft Certified Azure AI Fundamentals Course.
Overview Of Automated Machine Learning
Automated machine learning, also called Automated ML or AutoML is the process of creating a Machine Learning model. It automates the time consuming and iterative tasks of creating a model.
Traditional machine learning model development requires a good knowledge of various machine learning algorithms and it takes time to build an efficient model for predictions. Using Azure Automated ML we can build an efficient model without spending much time.
Read More: About machine learning models. Click here
Where To Use Automated Machine Learning
We use Azure Automated ML where we want to train and deploy a model based on the target metric we specify. This is used in various scenarios like:
- Implement ML solutions without extensive programming knowledge
- Save time and resources
- Leverage data science best practices
- Provide agile problem-solving
Also, Read Our previous blog post on Microsoft Azure Object Detection. Click here
Why Automated Machine Learning Important
Manually constructing a machine learning model is a multistep process and it requires expertise in various domains like statistics, calculus, Coding platform like python & R, and computer science skills. This will also increase the chances of errors and bugs which will directly affect the accuracy of the model.
Azure Automated ML enables organizations to deploy ML models with a baked-in knowledge of Data Science. Using Automated ML a non-technical background person can also implement models with a little knowledge of Data Science. This approach of deploying models will decrease efforts, risk, and time.
Azure Automated ML makes it possible for a business in every industry like healthcare, financial market, banking, etc to leverage ML & AI technologies.
To Know More About Azure Cognitive Services click here
Pros & Cons Of Automated Machine Learning
Benefits:
- Automatic prediction of the best pipeline for the labelled data.
- Automates various iterative ML related tasks (like model selection, featurization)
- Doesn’t require expertise in Data Science or technical background.
- Low development cost, less time-consuming.
Drawbacks:
- Non-optimal performance (sometimes very good sometimes bad)
- Not suitable for complex data structure and issues.
- Performance issues if the Dataset is too small.
Also Read: Our Previous Post on Microsoft Azure AI
How Automated ML Works In Azure
During the training process, Azure Machine Learning creates a number of pipelines simultaneously to predict which ML algorithm is best to suit the underlying data. It also does the feature selection and all the pre-processing required.
Note: Do Check Our Blog Post On DP 100 Exam for an overview.
Note: Azure ML pipeline is like a flowchart that specifies and performs the data flow from one phase to another in building a model.
Steps to design & run automated ml in the azure workspace:
- Identify which algorithm best suits the underlying problem.
- Choose what you want to use for deploying a model between Python SDK & Azure ML studio.
- Specify the source and format of the training data (Numpy or pandas)
- Configure Compute Targets for model training such as local compute, azure ml computes, remote VMs, or azure data bricks.
- Configure Auto ML parameters. It involves all the pre-processing, featurization, number of iterations over different models.
- Submit the trained model
- Review and analyze the score.
To Know More About Azure Machine Learning Studio click here
Cloud Vendors Providing Automated ML
- Google Cloud
- Microsoft Azure
- Amazon AWS
- IBS
- Salesforce Cloud Service
- SAP Cloud Platform
Read more: MLOps is based on DevOps principles and practices that increase the efficiency of workflows and improve the quality and consistency of the machine learning solutions.
Related/References:
- [DP-100] Microsoft Certified Azure Data Scientist Associate: Everything you must know
- [AI-900] Microsoft Certified Azure AI Fundamentals Course: Everything you must know
- Exam DP-100: Designing and Implementing a Data Science Solution on Azure
- Microsoft Certified Azure Data Scientist Associate | DP 100 | Step By Step Activity Guides (Hands-On Labs)
- Certified Kubernetes Administrator (CKA) Certification Exam: Everything You Must Know
- [DP-100] Designing and Implementing a Data Science Solution on Azure
- Microsoft Azure Data Scientist DP-100 FAQ
- Datastores And Datasets In Azure
- Overview of Hyperparameter Tuning In Azure
Next Task For You
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