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How does machine learning modeling work

Machine learning technology is relatively new and is in great demand. It’s worth knowing how ML modeling works and recognize the key difficulties with training the algorithms.

The accuracy of modeling will directly depend on the correctness and completeness of the collected data set. Check this detailed post to know, how to prepare the raw data for ML analysis. In addition, errors at the stage of data preparation can critically impact the quality of ML model training.

Now, let’s go deeper into the process of model training. If you do not intend to go into details, but looking for a free consultation from advanced IT experts, check this page.


After collecting the necessary information, you can proceed to the training stage of the model. During the training, various modifications are tried, hyperparameters are sorted out, and metrics are compared, during which the best combination of parameters is selected. ML modeling includes several stages, namely:

– Algorithm selection

The choice directly depends on what tasks need to be solved and, accordingly, on the complexity and applied features. At the same time, certain factors must be taken into account, such as:

1) Completeness of information (complex models require more data),

2) Processing of omissions (certain models are not able to eliminate them),

3) Data format (some algorithms require information conversion).


– Preparation of a test plan

Traditionally, there is a division into three parts, namely:

– validation,

– training,

– testing.

At this stage, the main thing is to determine the data set with the information the model will be trained. In addition, it is worth determining exactly how the optimization of hyper-parameters of modifications will be carried out, how many integrations will be required for any method, and whether grid-search or random-search will be used.

– Directly training the model

At this step, the direct training of the model begins. With the results of the training, you should pay attention to the following factors:

– Unusual patterns,

– Training time,

– Difficulties with data processing.

– Analysis of the results

At the end of the work, it is necessary to choose the most successful models that meet all the requirements and learn at maximum speed.

Necessary to conduct a qualitative technical analysis of the quality of the final model to assess whether the current model is ready to be implemented directly into the data warehouse, whether all the specified quality criteria have been achieved. If the success criterion (the metrics selected as the goal) is not reached, then the current models should be improved or changed.

Evaluation of the solution

At this stage, the evaluation of the current model should take place. The difference from the previous stage is the direct evaluation of the result and its benefit to the business (the effectiveness of the model in the business sphere). The main question to be answered in the analysis is how well and quickly a trained model can solve business problems.

It is necessary to estimate all the advantages and disadvantages of the ready model. All results will depend on the work of this model. To evaluate if it’s good enough, answers to questions, namely:

– Are there any stages that can work more efficiently? If so, how do I improve the model?

– Are there any errors in the current model? How can they be avoided?

– Were there any hypotheses that didn’t work? If they were, can they be avoided in the future?

– Were there any specific problems (surprises) while implementing the envisaged steps? Is it possible to revise and eliminate them?

After answering these questions, it is necessary to evaluate the finished model, and if there are options for improving it, then it is essential to rework the current model to make it more effective.


The implementation process is one of the most complicated. At this stage, the first errors that resulted from the incorrect collection of information will appear, and the suitability of the model for this area will be visible.

The most essential task for ML model integration is to control the consistency of model work with all systems that will supply it with data and receive ready-made forecasts.

To deploy a model, you need to implement the model using an open API.

At this stage, it is necessary to conclude how the company will use the current model, namely the Platform as a Service (PaaS) or Infrastructure as a Service (IaaS). Based on this fact, further development of the model will take place.

Testing and monitoring

We came to the final stage. Here we’ll see the model’s suitability to the particular business requirements and disclose its weaknesses in operation with real data. This part can be divided into several components:

– Differential tests

During this test, the old and new models are compared. During the comparison, a standard data set is used. These tests are necessary to detect if a new model is working better and if the results achieved, overcome in speed, accuracy, and veracity the results of the previous model.

– Control tests

These tests compare the effectiveness of the models. The model’s effectiveness is quite challenging to determine using standard mechanisms (mathematical computations allow you to estimate and compare the accuracy of results).

– Stress tests

The stress test is quite specific, but it must be passed since the model may experience overloading. It’s one of the standard procedures to ensure the model will be working even with the increased quantity of requests from users and the increased volume of data.

– A/B testing

A fairly popular test, during which it is possible to evaluate the quantitative performance characteristics of several variants of the model. To improve the result, we need to exclude the influence of models on each other.

Business analysis

Some ML models may be universal, but most of them are suitable for a particular industry. Below is the list where the ML models achieved excellent results and sped up business development progress.

– healthcare: analysis of medical data, improving the accuracy of diagnosis of various diseases;

– cybersecurity: using deep learning algorithms to detect anomalies in network behavior;

agriculture: management of agrobots, careful harvesting;

transport: automatic control systems for freight trains, excluding the human factor, driverless vehicles;

– e-commerce: “smart” recommendation systems for buyers;

– retail: supply chain planning, consumer behavior monitoring, warehouse automation;

– marketing: automation of targeted advertising, development of personal offers for the consumer;

– finance: algorithmic trading, processing of bank data, formation of credit ratings;

– sports: collecting and analyzing player actions, virtual assistants for coaches and referees.

These are just a few of the many examples of how ML modeling work to increase business efficiency and provide your company with great tooling for future progress.

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