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.
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.
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
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.
- Automatic prediction of the best pipeline for the labeled 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.
- Non-optimal performance (sometimes very good sometimes bad)
- Not suitable for complex data structure and issues.
- Performance issues if the Dataset is too small.
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