We already touched upon the topic of neural network development, but decided to review it again in detail for those who missed our previous article. Today we will talk about the cost of developing a turnkey neural network, analyze what the price consists of, and why it requires significant resources.
Stages of Neural Network Training: from Strategy to Implementation
Let's take a closer look at the key stages involved in the development and training of a neural network. Each of them plays an important role in creating an effective and accurate model.
1. Developing a Strategy
Before starting to train a neural network, it is necessary to develop a clear strategy. This includes:
- Formulating project goals and objectives.
- Defining the types of data that will be used.
- Choosing a model architecture (e.g., convolutional neural networks, recurrent or transformers).
A well-developed strategy helps avoid confusion and allows you to focus on achieving specific goals.
2. Data Collection for Training
A huge amount of data is required to train a neural network, which must be relevant and high quality. This stage includes:
- Searching for data in open sources.
- Collecting user data if the task is related to a specific audience.
- Creating your own data (e.g., filming for a dataset).
The amount of data depends on the task: the more complex the task, the more data will be required for training.
3. Data Segmentation
The collected data needs to be processed and labeled so that the neural network understands how to work with it. For example:
- For images — indicating objects in photos.
- For text — classification by categories or entity extraction.
- For audio — marking time intervals or transcription.
Segmentation requires significant time and labor resources, but without it, the neural network will not be able to learn correctly.
4. Training the Neural Network
At this stage, data is loaded into the model, which begins the training process. Includes:
- Setting hyperparameters.
- Iterative process: training, evaluation, adjustment.
- Use of powerful computing resources to accelerate training.
This stage can take from several days to weeks or months, depending on the volume of data and the complexity of the model.
5. Testing
After training, the neural network is tested on new data that it has not seen before. This allows:
- Evaluate the accuracy and reliability of the model.
- Find weaknesses that require improvement.
- Ensure that the model works correctly in real conditions.
6. Creating an interface for interaction
To make the neural network useful for end users, an interface is created that simplifies working with the model. This can be:
- Web application.
- API for integration with other services.
- Mobile application.
The interface makes the neural network accessible to businesses and ensures ease of use.
Each of these stages is important for the successful development of a neural network, and their quality directly affects the final result. In the next part, we will talk about what the development cost consists of and how to optimize the budget.
Data Collection and Segmentation: Detailed Breakdown
Data collection is one of the most complex and labor-intensive stages of neural network development. It is impossible to create an effective model without high-quality and large volumes of data. Let's break down what is involved in this process in more detail.
Data Collection: Scale and Approach
Data collection requires not only time but also the involvement of a whole team of specialists. Here are the key aspects of this stage:
-
Searching for data on the Internet
Often, the necessary information can be found in open sources: text databases, images, videos, audio. However, such data may be either insufficient or not relevant to a specific task. -
Creating your own data
When it is impossible to find suitable data, we create it from scratch.
- Text data: Copywriters are involved to write thousands of lines to cover all possible model operation scenarios.
- Visual data: We rent a studio and conduct filming, recording terabytes of photo and video materials that will be used for training.
A large amount of data is required for large projects:
- Text data: tens of thousands of lines are the minimum. Millions or even billions of lines are needed for a high-quality result. For example, the well-known ChatGPT neural network was trained on 275 billion lines.
- Images: Thousands of photographs for computer vision or object recognition training.
Data Segmentation: Preparation for Training
After collecting data, it needs to be segmented so that the neural network can work with it correctly. This stage includes:
- Text annotation: indicating where questions, answers, keywords or other important elements are located.
- Image annotation: highlighting objects in photographs, indicating their location and category.
- Audio and video annotation: creating timecodes and transcripts.
Without segmentation, the neural network will not be able to learn correctly, so this process is just as important as the data collection itself.
Complexity and Costs
This stage requires a huge amount of time, resources and specialists:
- The data collection and segmentation process can take from one month to 4 months.
- The cost starts from 500,000 rubles and depends on the volume of data and the complexity of its preparation.
Data collection and segmentation is the foundation of successful neural network development. The basis for the accuracy, efficiency and performance of the model is laid at this stage. It is not possible to save on this process, as the quality of the data directly affects the final result.
In the following sections, we will discuss how a neural network is trained and why it also takes so much time and resources.
Neural Network Training: A Complex Process Requiring Resources
Training a neural networkmdash; is a stage in which the collected and segmented data is transformed into a working model. This is a complex process that requires powerful computing resources, fine-tuning, and regular monitoring. Let's break down the main steps of this stage.
1. Data preparation for training
Before starting training, the data undergoes final processing. This includes:
- Splitting into training and test data: typically 70–80% of the data are used for training, and the remaining 20–30% – for checking the model's quality.
- Data cleaning: removing noise, errors, and duplicates.
- Data normalization: bringing the data to a format that the neural network understands (e.g., scaling numbers or transforming images).
2. Model architecture setup
The choice and configuration of the neural network architecture depend on the task. For example:
- Convolutional neural networks (CNNs) are used for image processing.
- Recurrent neural networks (RNNs) or transformers (like in GPT) are used for text.
- Combinations of various architectures can be used for audio or video.
At this stage, model hyperparameters are also set, such as the number of layers, the number of neurons, the learning rate, and the batch size.
3. Model training
The training process itself involves the model processing data and learning patterns. This includes:
- Iterations: the model repeatedly goes through the data, improving its predictions with each step.
- Optimization: an algorithm is used that minimizes the model's error (e.g., Adam or SGD).
- Backpropagation: adjusting the weights of neurons to increase accuracy.
Training can take from several days to several weeks or months, depending on the complexity of the model and the amount of data.
4. Results verification
After each training cycle (epoch), the model is tested on data that it has not seen before. This allows:
- To evaluate the accuracy of predictions.
- Find errors that need to be fixed.
- Avoid overfitting (when the model memorizes data instead of learning to analyze it).
5. Use of computing resources
Training neural networks requires powerful hardware:
- GPU and TPU: graphics processors and tensor processors accelerate the processing of large volumes of data.
- Server clusters: when training large models, such as language transformers, distributed training on multiple servers is used.
- Cost: renting or using such resources can cost hundreds of thousands of rubles, especially for large projects.
6. Final model
After the training is complete, the model undergoes final verification, and its weight parameters are saved. These parameters determine how the neural network will work on new data.
Difficulties and cost
Training a neural network is one of the most expensive stages of development:
- Time costs: from several weeks to several months.
- Cost: starting from 300,000 rubles and higher, depending on the complexity of the task and the volume of data.
Training a neural network is not just a technical process, but an entire science that requires experience, patience and the right approach. At this stage, the model's "intelligence" is formed, which will determine its success in solving tasks.
In the next section, we will discuss how the trained neural network is tested and deployed.
Testing and deployment of a neural network: key stages
After the training of the neural network is completed, there is no less important stage - testing and deployment. It is here that it is checked how well the model copes with real tasks and is integrated into workflows.
1. Neural network testing
Testing is a stage where the quality of the model's work on new data, which it has not seen before, is checked.
Main steps:
- Testing on reserved data: The model is tested on a pre-allocated dataset to determine its accuracy, completeness, F1-score and other metrics.
- Real-world scenarios: The neural network is tested on tasks close to real usage conditions.
- For example, for a text processing model — a test with texts containing complex structures and errors.
- For image recognition models — testing on images of different quality and lighting.
- Error analysis: Cases where the model performs incorrectly are identified. This helps to understand what needs to be improved.
Goal: To ensure that the neural network operates stably and performs the task with sufficient accuracy.
2. Model Optimization
Defects detected during testing may require optimization:
- Elimination of biases, due to which the model overfits to one set of data and performs poorly with others.
- Reducing the size of the model so that it processes data faster (e.g., through pruning).
- Tuning hyperparameters to improve performance.
3. Neural Network Deployment
When the model has been successfully tested, it is ready for deployment in a real environment. This process involves several stages:
3.1 Integration into infrastructure
The neural network is connected to the company's systems:
- API integration: An interface is created through which the model interacts with other systems.
- Integration with application: For example, embedding the model in a web application, chat bot or mobile application.
- Integration with databases: So that the neural network can work with real company data.
3.2 Configuration of computing resources
Dedicated servers or cloud solutions may be required for the operation of the neural network:
- Cloud services: AWS, Google Cloud or Microsoft Azure for scalability and reliability.
- Local servers: For companies where data confidentiality is important.
4. Real-world Testing
After integration, the model goes through a validation stage in a production environment:
- Its performance on real data is checked.
- Processing speed and stability are evaluated.
- Possible bugs or failures in operation are identified.
5. User Training and Technical Support
To successfully implement the neural network, it is important to train company employees:
- Explain how to use the model and interpret its results.
- Ensure access to documentation and instructions.
- Organize technical support in the initial stages of use.
6. Monitoring and Updates
Even after implementation, the model requires constant monitoring:
- Performance Monitoring: collecting data on how the neural network handles tasks in a real environment.
- Model Update: regular retraining or reworking to account for new data and changed conditions.
- Bug Fixes: prompt elimination of problems that may arise during use.
Difficulties and Costs
Testing and implementing a neural network require significant resources:
- Time: from several weeks to months.
- Cost: from 200,000 rubles for simple integration to millions of rubles for complex corporate solutions.
Testing and implementation are the final, but no less important stage of neural network development. It is at this stage that the model becomes a ready-made tool that can solve your business problems. Successful implementation requires a professional approach, thorough testing, and constant monitoring so that the neural network brings maximum benefit.
If you need implement a neural network or adapt it to your business processes, EasyByte specialists are ready to take over this process!