B2Metric AutoML solution set up an ML pipeline for the usage of marketing teams, data scientists, and the executive team's self service machine learning modeling solution. B2Metric Machine Learning Studio solution meets these main data science situations: data preparation, data wrangling, feature engineering, selection of algorithms, training and parameter tuning, then understandable insights with reporting at clean dashboards.

AutoML is a machine learning that aims to manage the most appropriate solution that can be used for a specific problem in an end-to-end machine learning process, with minimum effort and maximum success, by automatically combining the techniques used in machine learning stages. The developments in recent years show that the algorithms prepared with AutoML techniques pass the success of the algorithms prepared with the expertise, intensive time, labor and technical knowledge in minimum time. Data science is not about data wrangling, nor about building complex models. The golden key to this success is its ability to quickly and effectively search different combinations in the space of autonomous features and the combination of different techniques that can be used at any stage. The following steps in the autoML data pipeline:

• Data connectivity

• Exploratory data analysis (EDA)

• Modeling (training, validation, evaluation, comparison, and scoring)

• Deployment

Why Do You Need AutoML?

In recent years, when digital transformation and data accumulation has been accelerating, the increasing need for qualified data scientists, the time to establish the right team that can solve the pending problems of large companies, the budget and time constraints of small and medium-sized enterprises stand against the further advancement. In this context, it is an obstacle for those who want to fill the skill gap and bring the transformation of machine learning to their businesses. So, There is a inevitable request for a machine learning method that can grow rapidly and can be used independently of expert knowledge. AutoML offers a solution to these problems with its high automation skills.

“With B2Metric, you can use AutoML in every field of data science and artificial intelligence studies. AutoML approach automates the ML modeling and feature engineering steps with highest increase on accuracy of prediction scores.”

AutoML attracts the attention of the machine learning ecosystem and those who are not familiar with machine learning or who are not capable of specialization in machine learning methodologies because of its autonomous features and pipeline managing capabilities. While the first of these groups aim to develop its creative technical skills in areas where AutoML is not successful and to use AutoML as an auxiliary tool, the dependence of the second group on the AutoML tools is more obvious. The ecosystem of the second group is more remarkable because this group lacks the time, expertise, and a sufficiently large data management team to plan the machine learning process so that they can see the possible contribution of machine learning to the way they do business. Here, the devastating effect of AutoML is used to automate creating predictive modeling processes, to get feedback in a short time, and to manage transformation without the expert team.

To conclude AutoML offers the best data science applications, saves time, effort, and resources, and allows you to use ML applications without expertise.

Implementation of AutoML

“B2Metric offers ML Pipeline solution for customer journey analysis and dynamic pricing calculation which can be run on the cloud or on-prem set up. It allows you to integrate Auto ML into your in-house DWH systems. With the automated production of advanced ML pipelines, it automates the hassle of getting your data ready for Machine Learning.”

This makes your customers' behavior much more understandable by your marketing and data science teams.

• Classification is the process of predicting the class of given data. It is one of the widely used controlled machine learning algorithms. Also, there are 2 types of learners in classification as lazy learners and eager learners.

• Clustering is one of the basic unsupervised machine learning algorithms. Clustering is the process of dividing data into groups. It is basically a collection of objects on the basis of similarity and dissimilarity between them. Mainly there are 4 types of clustering methods; Density-Based Methods, Hierarchical Based Methods, Partitioning Methods, Grid-based Methods

• Regression analysis is a subdomain of supervised machine learning. It purposes to model the relationship between a number of features and a continuous target variable.

There are hundreds of models working with the above algorithms. With machine learning, these models are run and results that will mean data are obtained. The main goal is to run as many models as possible and choose the best model as champion model. On the other hand, AutoML controls many models at a speed that cannot be reached manually and shows the model that gives the best results as a champion model. In short, AutoML is the best ML model search automation for available data.