Artificial Intelligence (AI) and machine learning (ML) are propelling business transformation and innovation, ushering in a new era of technology-driven operations, processes, and business models.
As machine learning development becomes commonplace for organizations across industries, new advancements are being made that help improve ML development processes. One of these advancements is Automated Machine learning (AutoML).
The Benefits of AutoML
Many AutoML solutions use popular features that are common with modernized technology deployments, such as using a single unified interface for building and deploying AI models.
AutoML helps organizations better manage model deployment, helping data science professionals and developers experiment with new models and access the tools that they need to deploy models quickly. AutoML solutions also support the use of large datasets, adding customizable features that assist in data preparation, cloud and edge deployment capabilities, video analysis, and enhance model scalability.
Organizations across industries need AI solution opportunities, and AutoML brings new AI access to many different people with varying AI and ML-related skill sets and educational levels within the business.
A Deeper Look at AutoML
ML requires coding experience, and AutoML simplifies development by improving code generation. ML pipelines can be complex to navigate, from coding, data collection and cleaning, model training, and model deployment. AutoML helps people search algorithms and find the best solutions for each stage of machine learning development.
Feature engineering is the process of choosing integral elements out of raw data sets to be used in ML model development.
Automated feature engineering helps to reduce the time it takes for feature engineering, which is a lengthy process that can take days as data scientists try out different feature combinations.
AutoML helps overcome challenges in feature engineering, such as maintaining different feature model versions, simplifying how features are defined, and keeping up with tracking varying features and models.
Some parts of ML models need to be tuned, and this is called hyperparameter optimization. It can be done manually for more basic ML models, but this can exponentially grow in difficulty for deep learning.
Automating this process helps developers narrow in on their data and focus on the reasons behind creating the models instead of how they are creating them. This improves analytics because developers and data scientists can pay attention to improving specific aspects of their models, which helps with different industry cases such as fraud detection in financial services, for example.
Neural Architecture Search (NAS)
Creating a neural architecture for deep learning applications is very challenging and computationally heavy. The purpose of AutoML in this area is to democratize deep learning to improve model efficiency and reduce errors in model development.
AutoML functions help evaluate architectures quickly so that developers can take advantage of NAS adaptability and enhance optimization and performance. NAS brings much agility to different industries and can benefit from automation.
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An Automated Machine Learning Future
Organizations across industries can benefit from AutoML capabilities and support their development and data science teams with automation. AutoML will help increase model development and deployment speed and efficiency, helping developers with less experience take advantage of machine learning while giving seasoned data science professionals more tools at their disposal. Enroll in Simplilearn's Post Graduate Program in AI and Machine Learning and step into the exciting world of AI and machine learning.
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