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What is Automated Machine Learning

Automated machine learning, also referred to as automated ML or AutoML, is a process of applying Machine Learning (ML) models for real-time problems using automation. Traditionally for each step in the quintessential data science pipeline - machine learning experts manually manage the data pre-processing, feature engineering, and, feature section methods. After that, they perform algorithm selection and maximize the predictive performance of the model to hyperparameter the optimization.

In real-time, analysts and data scientists spend most of their time cleaning, sourcing, and preparing data for the actual work which is model building. Models are developed by hand, and each of these steps in the process must be handled separately and are challenging, resulting in time-consuming and repetitive tasks.

Automated Machine Learning is a software platform that makes machine learning more user-friendly and provides access to machine learning without any specialized data scientists or machine learning experts. AutoML (Automated Machine Learning) is generally a platform or open-source that simplifies each step in the machine learning process, from handling a raw dataset to developing a practical machine learning model. Automated Machine Learning was recommended as an Artificial Intelligence-based solution to the growing challenges in applying machine learning to real-world problems.

Why is Automated Machine Learning Important

Automated Machine Learning is important because it represents a milestone in the fields of machine learning (ML) and Artificial Intelligence (AI). In today’s world technology enables business, therefore, a data scientist or an analyst’s time should be invested in focusing on business problems rather than wasting time on workflow or processes. The AutoML process automates parts of the machine learning that applies the algorithm to real-world scenarios. Performing this task by a human would need an understanding of the algorithm’s internal logic and how it relates to real-time situations. It benefits by providing solutions with explainable and reproducible results for real-time problems, which provides faster, more accurate outputs as it enables expert and non-expert users to automate repetitive and manual machine learning tasks than the traditional ML process which, is tedious, repetitive, and human-dependent while Automated Machine Learning is user-friendly and less time-consuming. Through meta-learning machine learning pipelines or fine-tuning the end-to-end machine learning process has been made possible by Automated Machine Learning. Without a data science team’s attention and by using AutoML, a data analyst or a data scientist now can pre-process data, build a machine learning pipeline, and produce a fully developed model that they can use for validating their own theory.

Features and Benefits of Automated Machine Learning

To build a predictive model there are many steps a data scientist team must go through in a data science pipeline. Even an experienced set of data scientists and machine learning engineers can benefit from the increased speed and transparency with AutoML. A data scientist or machine learning engineer has to start with a hypothesis, gather the correct dataset, try providing some data visualization, engineer more features to harness all the signals available, then train a model with hyperparameters, and for the state-of-the-art deep learning, design a Deep Neural Network on a GPU.

Below are some of the steps of the machine learning process that can automate AutoML.

-Raw data processing

-Feature engineering and feature selection

-Model selection

-Hyperparameter and parameter optimization

-Evaluation metric selection

-Monitoring and problem checking

-Analysis of results

Some popular features of AutoML platforms are:

Google AutoML – Google’s exclusive cloud-based automated machine learning platform.

Azure AutoML – Cloud-based platform.

Auto Keras – Developed by the DATA lab at Texas A&M University, an open-source software library.

Auto-Sklearn – This is replaced by Scikit learn, an open-source, which was commercially used for the collection of samples for machine learning tools in Python.

Auto-sklearn and Azure rely strongly on the whole data set to work. classification and regression techniques are used and are also considered cheaper because they are less resource-intensive.

On the other hand, Google AutoML and Auto Keras, by variance, are very skilled at creating new models. They require the whole data set as they are more resource-intensive and use Recurrent Neural Networks (RNN), Convoluted Neural Networks (CNN), and Long Short-Term Memory (LSTM).

Uses and Benefits of Automation Machine Learning

To harness machine learning and AI technology, Automated machine learning makes it easy for companies in all sectors: health care, financial markets, fintech, public sector, marketing, retail, sports, manufacturing, etc.

Organizations’ primary goal for incorporating AI is to optimize internal business operations, they believe that AI will give them a competitive advantage, the most significant business advantage of the future. It has increased high-level business productivity, firms are investing in Big Data and AI initiatives, and C-level executives are using machine learning for performance analysis and reporting. Launching pilots have proven deceptively easy, but deploying them into production is notoriously challenging. It enables humans to concentrate on more meaningful work. ML in several areas is considered essential to their organization. Sales and marketing is the most profitable area to implement machine learning systems. Industries seeing the most significant increases in budgets for machine learning projects are typically increasing with the banking, manufacturing, and information technology. The adopted AI will be 10 times more efficient in the coming years and have twice the market share of companies. The top three most remarkable challenges companies face when considering the implementation of AI are staff skills, the fear of the unknown, and finding a starting point.

The demand is so high that there is a thrust and demand for data analytic positions in the workplace. Today businesses are turning to no-code and low-code platforms.

Rather than weeks or months business users turn data into actionable insights via predictive analytics in seconds. 10X faster than traditional software development are no-code platforms, an increase in value by using no-code applications, businesses have active development initiatives, and have potentially reduced development time that adds value to their projects.

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Automated Machine Learning GitHub / Python

With very little user intervention AutoML provides tools to automatically discover good machine learning model pipelines for a dataset and get good results quickly for a predictive modeling task. For using AutoML methods there are open-source libraries that are available with popular machine learning libraries in Python, for eg: - the scikit-learn machine learning library. Hyperopt-Sklearn, Auto-Sklearn, and TPOT are the three most popular AutoML libraries for Scikit-Learn.

Auto-Sklearn: Efficient and Robust automated machine learning system based on scikit-learn is an open-source Python library for Automated Machine Learning models.

Tree-based Pipeline Optimization Tool (TPOT): Is a Python library for automated machine learning. A transformative algorithm that automatically designs and optimizes machine learning pipelines is called the Tree-based Pipeline Optimization Tool (TPOT).

Hyperopt-Sklearn: To users of Python and scikit-learn a plan that brings the benefits of automatic algorithm configuration. Designed for large-scale optimization for models with hundreds of parameters and that allows the optimization procedure to be scaled across multiple cores and multiple machines. The automatic search of data preparation methods are machine learning algorithms, and model hyperparameters for classification and regression tasks.

Automated Machine Learning methods systems challenges

Auto-ML Methods:

Automated Feature Engineering: Feature engineering is one of the key value-adding processes in an ML workflow, a feature engineering refers to the transformative process where a data scientist derives new information from existing data. The raw data are read into the model and serve as the heart of the machine learning process. In the modern-day manual feature, engineering comes at a great cost in terms of time, building a single feature can often take hours, while AutoML reduces the time in this phase from days to minutes. It not only reduces the manual interventions but also generates features that are more often interpretable, they make high-quality models more compelling and actionable. Once feature engineering is completed will then have to optimize their models with strategic feature selection.

Automated Hyperparameter Optimization: Hyperparameters are a part of machine learning algorithms, which are best known by comparing as levers for fine-tuning model performance. In a small-scale data scientist model, it can easily be set by hand and optimized by trial and error. The number grows exponentially which puts their optimization beyond the abilities of a data science team to accomplish in a manual and timely fashion for deep learning applications. It relieves teams to explore and optimize instead allows them to iterate and experiment over features and models. an analytics team can pay attention to which aspects of the model they should optimize with a large amount of data available which is another strength of automating the machine learning process.

Neural Architecture Search (NAS): The creation of Neural architecture is the most complex and time-consuming process in deep learning. The data science team spends long hours selecting the appropriate language models. Neural Architecture search is one of the most prominent areas of machine learning to benefit from automation. NAS begins to search with a choice of architectures to try, the outcome is determined by the metric against which each architecture is judged. It uses several common algorithms. If the prospective number of architectures is small, random choices of testing can be made. Neural Architecture search is one of the main elements of AutoML that promises to democratize AI. However, this comes with a very high carbon footprint. The optimization for ecological cost in an ongoing search area for NAS approaches for the examination of these trade-offs has not been done yet.

AutoML Systems:

Hyperopt-Sklearn: data preparation methods are machine learning algorithms, and model hyperparameters for classification and regression tasks.

Auto-Net: feature preprocessing, data preprocessing and ensemble construction - Tuned Neural Networks.

TPOT: automatically designs and optimizes machine learning pipelines including data preparation and modeling algorithms, and model hyperparameters.

The Automatic Statistician: data pre-processing and transformation are all done at the same time.

AutoML Challenges:

-Supervised learning problems.

-Feature vector representations.

-Homogeneous datasets

-Medium size datasets

-Limited computer resources

-Different data distributions

How FutureAnalytica can help you in your AutoML journey

We help enterprises from every industry leverage the benefits of AI and decision-makers take profitable business decisions by unlocking the value from data, without any hassle, through our comprehensive no-code automated machine learning platform.

FutureAnalytica enables automating the time-consuming iterative tasks of machine learning model development. Our platform allows data scientists, analysts, and developers to build ML models with high scale efficiency, and productivity while maintaining model quality.

Conclusion:

Machine learning and Artificial Intelligence are no longer far away, they are revolutionizing entire aspects of life. AutoML helps data scientists to increase their efficiency and realize their true potential by automating machine learning tasks such as pipeline development and hyperparameter tuning, and by automating repetitive ML tasks, AutoML can exponentially reduce the time and effort.

FutureAnalytica is the only holistic automated machine-learning, no-code AI platform providing end-to-end seamless data-science functionality with a data-lake house, Propriety AI app-store & world-class data science support, thus reducing time and effort in your data-science and artificial intelligence journey.

We hope this article was insightful and helped you understand automated machine learning and its importance. Thank you for showing interest in our blog and if you have any questions related to Data Analytics, Machine Learning, or AI-based platforms, please send us an email at info@futureanalytica.com.

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