Automated Machine Learning (AutoML)

What is AutoML?

“B2Metric AutoML solution set up an ML pipeline for usage of marketing teams, data scientists and data executives. B2Metric Machine Learning Studio brings end-to-end solution and 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.”

B2Metric AutoML

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:

  1. Data connectivity
  2. Exploratory data analysis (EDA)
  3. Modeling (training, validation, evaluation, comparison, and scoring)
  4. 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.

B2Metric AutoML based Risk Management

Plug&Play B2Metric AutoML Use Cases

  1. Price Elasticity and Optimization
  2. B2Metric creates a continuous business learning infrastructure for marketing budget optimization. Giving the right price for a product or service is an old problem in economic theory. When using a competitive pricing strategy, the prices of your services based on your competitors' pricing. You can also take into account your business targets and how the difference between competitors' prices and your own will appear to customers.

    The B2Metric autoML models can only as good as the data its fed. The process begins with your data scientist team evaluating the data sources, and ensuring that they’re accurately fed into the model correct way. With high-quality data, the price optimization models determine whole price distributions ( for instance, comparing money earned upfront to CLV - customer lifetime value) along with various variables that can help you determine the best price for your business goals. B2Metric can also predict how targeted customers of you will respond to prices they haven’t yet encountered. With plotting responses and predicting patterns, pricing strategies at a basic level without necessarily having to run each one.

  3. Risk Management
  4. Risk management is undoubtedly of great importance in many sectors. Especially insurance and finance industries uses B2Metric Hunter, that predict that claims and non performing loans must be based on features that are paid automatically through deep learning, and you minimize the chance of errors. AI is preparing to change the industries on a large scale with risk analysis and other developing technologies. Using these technologies, insurance agents will be able to obtain customer insights, behaviors, and participation information that they have never seen and are unsure of. As a result, it will increase the value of buyers, attract new buyers to increase revenue as much as possible by moving your product or service beyond the promises.

  5. Customer Journey Analytics
  6. Customer journey cites to the path followed through the points of contact of your customers and potential customers before making a purchase action. Customer journey analysis allows you to identify your customer touchpoints. Today, companies should think like their customers; customer journey analysis makes this easy. Real-time reporting that provided by B2Metric AutoML, allows you to optimize customer loss. With B2Metric AutoML, you can predict churn and retention values with customer interaction data. Journey maps are infographic visualizations to understand how a customer is working towards a goal over time. Each algorithm that B2Metric provides for you can be adjusted to get the most suitable model and provides you the best customer experience.

  7. Sales and Demand Forecasting
  8. Sales forecasts can be used to identify benchmarks and also incremental impacts of new approaches, planing of resources in response to expected demand, and project future budgets. You can calculate the estimated product sales quantity for the next month and optimize the best product sales amount. Then, you can direct the sales activities of business. With the opportunities offered by B2Metric ML Studio, you can get real-time recommendations for the results of models below:

    • -Customer Lifetime Value Prediction
    • -Customer Segmentation
    • -Churn Prediction
    • -Predicting Next Best Action
    • -Uplift modeling
    • -Predicting Sales
  9. Anomaly Detection
  10. Anomaly detection is a method to notice anomalous actions or data. It predominantly focuses on the problem referring to recognize all intrusive attacks based on their anomalous activities that diverge from “normal activity profile” in a system. The power of technology-driven fraud detection solutions stems from their ability to process large data sets. The multiplicity of data increases the accuracy and efficiency of the analysis.

    B2Metric AI uses several internal and external data sources to help its companies identify fraudulent behavior. Anomaly detection affects business decisions across sectors. Sectors such of, insurance, finance, telecom, manufacturing, banking are the main sectors which anomaly detection is of great importance. Technically, the most distinctive criteria between normal and abnormal data point is whether there are similar data points around it in the analytical plane. In this context, the areas where similar points become very clustered are considered normal, and the areas where they become sparse are called abnormal areas.