Automated machine learning, called AutoML (Automated Machine Learning) in the field of artificial intelligence and machine learning, describes integrated software platforms for the creation, training and optimization of a machine learning model. These platforms allow users to automatically select and train the best-performing model without having to be experts in data science and machine learning.

The AutoML process usually involves these steps:

1. Data Preparation: Used for data preparation, cleaning, editing and preprocessing of data. AutoML systems also support data preparation.
2. Model Selection: The AutoML system selects the most appropriate model among many different machine learning algorithms. In model selection, factors such as the size of the data set, the data type, and the target variable are also taken into account.
3. Training: The selected model is trained using the data set. The AutoML system offers many different techniques that can be used to optimize the training parameters and hyperparameters of the model.
4. Evaluation: The trained model is evaluated using many different metrics used to assess its performance in the data set.
5. Model Distribution: The improved model is distributed to the production environment. The AutoML system provides all the tools and operations necessary for the distribution, operation and monitoring of the performance of the model.

These steps enable AutoML systems to offer data scientists and machine learning professionals the ability to automate and streamline machine learning model building, training, and deployment processes.

What are the benefits of AutoML in business processes?

1. Time-saving: AutoML can save data scientist and machine learning professionals time in the process of selecting, training and optimizing models. AutoML can save data scientist and machine learning professionals more time by automating these processes.
2. Model performance: AutoML can help data scientist and machine learning professionals optimize model performance by selecting and training the most appropriate model. AutoML can deliver better results than data scientists' manual model selection, training, and optimization processes.
3. Ease of use: AutoML can help data scientist and machine learning professionals understand and use machine learning processes easily. By automating these processes, AutoML can offer easier use to data scientist and machine learning professionals.
4. High accuracy: AutoML can help data scientists and machine learning experts achieve high accuracy rates by analyzing different sizes and properties of data, choosing the best model.
5. Reducing knowledge requirements for model selection and training: AutoML does not require data scientists and machine learning experts to have detailed knowledge of model selection and training. AutoML can reduce the knowledge requirement of data scientist and machine learning professionals by automating these processes.

What Are the Uses of AutoML?

Basically, automatic machine learning is an automated application of model algorithm selection, hyperparameter optimization, modeling with iterations, and model evaluation. This technology is not intended to replace data scientists, but rather saves them from repetitive tasks. AutoML, when combined with MLOps methodologies for the development of machine learning models, ensures high efficiency in business organizations.

MLOps is the application of the concept of DevOps for the continuation and maintenance of artificial intelligence and machine learning projects. This covers all the business processes and tools necessary for the development, testing, deployment and management of machine learning models in a production environment. MLops includes data scientists and machine learning experts as well as the DevOps team, enabling the rapid, secure and scalable deployment of machine learning projects.

What are AutoML Usage Scenarios by Industry?

AutoML is a technology that can be used in many different industries, and different use scenarios can be found in each industry. We can exemplify its use in the financial, health and e-commerce sectors:

1. Financial Sector: Machine learning technologies can be used for applications such as risk management, fraud detection and marketing campaigns. Because data is so big and too much in the financial industry, AutoML can help data scientists and machine learning experts in repetitive tasks. Financial companies can manage portfolios using AutoML. This helps financial companies to best manage their clients' portfolios. Risk management with AutoML enables financial companies to assess and manage market and customer risks.
2. Health Sector: AutoML can use it for applications such as disease prediction, treatment plan and disease tracking in the health sector. It can help data scientists, as care must be taken to the privacy and security of patients' personal data.
3. E-commerce: Can use machine learning technologies in the e-commerce sector for applications such as customer behavior analysis, recommendation engines and customer satisfaction. In this industry, it captures a high accuracy rate while saving data scientists time because so much data is collected and classified from customers. E-commerce companies can recommend their products and services using AutoML to provide their customers with a better e-commerce experience. By creating business analytics with AutoML, it can help companies analyze customer data, product sales, and other critical data. By configuring marketing optimization with AutoML, they can deliver the right message to the right customer at the right time. This helps turn ads into sales.
4. Let us examine in detail the use of AutoML for the tourism sector: Prediction of Travel Destinations: Tourism companies can estimate travel destinations using AutoML. This can help tourism companies understand their clients' travel preferences and provide them with more convenient services.Hotels and Accommodations Rating: AutoML can be used by tourism companies to evaluate hotels and accommodations. This can help tourism companies recommend the most suitable hotels and accommodations to their clients.Travel Packages Recommendation: AutoML can recommend travel packages by using tourism companies. This can help tourism companies recommend the most suitable travel packages to their clients.Tourist Flow Analysis: AutoML can perform tourist flow analysis by using tourism companies. This can help tourism companies understand where tourists are traveling and when they are traveling.

AutoML is very important for companies and has many benefits. Companies can save time and cost in data analytics and machine learning processes by using AutoML. At the same time, AutoML can improve the market competitiveness of companies by increasing the efficiency and accuracy rate in data analytics and machine learning processes.

AutoML saves time by automating the processes of manually selecting, training and optimizing models for data scientists and machine learning professionals. In this way, data analytics and machine learning projects can be completed faster and companies can respond to market needs faster.

AutoML can also increase the accuracy rate in data analytics and machine learning processes. AutoML can help data analysts and machine learning experts achieve high accuracy rates by analyzing different sizes and properties of data by selecting the best model.

AutoML can reduce the knowledge requirement of data scientists and machine learning professionals in data analytics and machine learning processes. By automating these processes, AutoML does not require data analysts and machine learning experts to have detailed knowledge of model selection and training.

AutoML can offer significant benefits for companies in terms of time, cost, efficiency and accuracy ratio in data analytics and machine learning processes. By using AutoML, companies can increase their market competitiveness and respond to the needs of the market faster.

In the transformation required by organizations in the Data and Analytics focus, we ensure the design of the process, the production of the solution and the smooth execution of processes with leading technologies in the field.

Komtaş prefers Dataiku, one of the world's leading technologies in MLops, AutoML solutions. Dataiku is accessible and user-friendly for users with diverse profiles, from non-technical business analysts to top-level data scientists and software developers. Using Dataiku's web interface and drag-and-drop options, the whole team can work together regardless of technical ability.

Contact us to learn about our end-to-end solutions and technologies.