Unified AutoML Platform

What is AutoML?

Automated machine learning makes machine learning more accessible to non-experts, increasing machine learning efficiency, and accelerating machine learning research. In recent years, machine learning (ML) has been used more and more in various fields, leading to remarkable successes. The new technology known as Automated Machine Learning, also referred to as Automated ML or AutoML, enables data scientists to concentrate on tasks with higher value, accelerates the process of building models, automates processes associated with machine learning tasks, and improves the accuracy of ML models.

What benefits do customers get when using FutureAnalytica’s unique codeless AI platform?

FutureAnalytica’s services help automate the laborious and iterative process of building machine learning models for customers. It lets developers, analysts, and data scientists build ML models at scale, with efficiency, accuracy, and productivity while keeping the quality of the models. An artificial intelligence platform generates all the information for your models automatically and quickly. The data contained in this information may be used by business executives, data engineers, data scientists, and others to take the necessary actions required. The platform also tells you which model is best for deployment.

Application of AutoML

AutoML incorporates artificial intelligence best practices to make data science more accessible and reduce time to value creation. Machine learning is significantly ahead of humans in many tasks. To get the most out of this cutting-edge technology, many industries are using machine learning in different ways.

One of the most fundamental applications of ML is fraud detection. Online shopping is essential for the future of retail. Credit card fraud is becoming the most common form of identity theft due to the increase in the number of people using credit cards as a payment method and the expansion of the e-commerce industry. Another use of AutoML is for interpretation. Google’s GNMT (Google Neural Machine Translation) is the most famous example of ML in machine translation. The use of neuro-linguistic processing (POS tagging, named entity recognition, and segmentation) improves fluency and accuracy. The healthcare industry, especially medical diagnostic management, benefits greatly from AI. ML holds the key to efficiently automating all routine, manual, and tedious workloads, whether its reviewing key medical parameters, predicting disease progression based on information extract, planning treatment or providing support.

Benefits of automatic machine learning

1. Use numbers to make predictions — As an entrepreneur, it’s natural to want to know what you’re aiming for. Time series anticipation is utilized by ML architects and information researchers to foresee future occasions. They do this by looking at data and observing how certain values ​​change over time. It is a complicated procedure that often requires a lot of effort and time. It is especially difficult to decipher the appropriate signals and determine how past events will affect the future.

2. Automate ML models: ML models often need to be manually rebuilt and updated on a regular basis. On the other hand, the forecasting process is completely automated by AutoML. The discovery of future signals, values ​​and forecasts is also included. In other words, the model is always modified by AutoML to adapt to the new situation. This means you have a lot less stress. Choose algorithms for classification, model testing, and model refinement: AutoML can do it all.

3. Word-based estimates — When looking at information, are you quick to look at numbers? This does not surprise me: many individuals and certainly many entrepreneurs focus on numbers. But our public is ultimately built on words. Almost all correspondence is in language. Moreover, tone and choice of words, in addition to content, also contains valuable information. However, large-scale languages ​​are even harder to classify for people than large datasets containing numbers.

4. Natural Language Processing — Machines can read and understand language, just like ML can decode large data sets of numbers. This is how you build models that sift through multiple documents for important or even sentimental information. Just like individuals, but on a much larger scale and without getting lost in their own emotional implications. Imagine the opportunities this presents to better understand the impact of a particular news event or product launch on your company’s reputation.

Steps involved in AutoML

Data preprocessing: Almost all AutoML systems provide basic data preprocessing options, such as scaling, transformation of categorical or qualitative variables (such as gender, age group and other attributes) into numeric values ​​and so on. Separating your data into training and validation sets is also part of it.

Feature selection and engineering — In machine learning, features are important attributes or attributes that represent the problem you want to solve. For example, if you run an online travel agency and want to create a machine learning-based flight price predictor to attract more customers, the feature set would include the operating airline, time departure and arrival times, flight distances, seasonality and more. The attributes used as inputs have a significant impact on the accuracy of the model. When useful attributes are extracted from the raw data and transformed into the desired shape, feature engineering, also known as feature extraction and feature selection, where the attributes are not related.

Obviously, you need to be an expert in your field to determine which option is best for your situation. However, beyond specific knowledge, the feature creation process includes many common tasks that can be simplified without compromising on quality. Generic features can also be extracted fully automatically in some cases to make predictions with sufficient accuracy.

Algorithm Selection — With AutoML, choosing the right algorithm for your problem is a thing of the past. The software will make this decision for you, choosing the option that best suits your task from the available selection.

Model selection and hyperparameter optimization — Prediction accuracy is affected not only by features but also by hyperparameters — internal parameters determine how accurate your algorithm is will learn on a particular data set. The goal of hyperparameter optimization or tuning (HPO) is to find the configuration that will produce the best predictive model.

HPO instruction is very time consuming because you have to train iteratively for each new set of hyperparameters and go through all the options in turn. AutoML tools, unlike humans, can quickly choose the best performing model from thousands of candidate models during testing. This is why this step is considered the central point of AutoML. Neural Architecture Search, also known as NAS, is a subset of deep learning-related hyperparameter tuning based on neural networks. Similar to HPO, it aims to: choose an ideal configuration for a given task.

Conclusion

AutoML can automate and resolve relationships by allowing teams to run a variety of ML models by

continuously assessing their exposure until an ideal limit is reached. Finding the unknown is the most rigorous aspect of model selection. This is why AutoML annoys testers. By using less code and avoiding custom hyperparameter tuning, it is considered to make ML tasks easier. AutoML’s main invention is hyperparameter search and intelligent fit search.

FutureAnalytica’s coming- generation technology is a no- code AI solution that lets anyone make advanced AI/ ML results without knowing how to code. An AI solution that doesn’t demand any coding and makes it simple for anyone to produce slice- edge analytics results with just countable clicks. However, please contact us at [email protected] for any queries. Don’t forget to visit our website www.futureanalytica.com.