Train AI is what? Solution and process training AI basically

To the pattern AI can maximize the efficiency in solving the complex problem, we have a plan trained professional. This process is called train AI (trainer AI), an important step to help AI learn and improve the ability to analyze, predict in the task of bringing out the solution. 

The same Lac Viet Computing learn Training AI and process training professionals out why, as well as the solution supports the most optimal.

1. Train AI is what?

Train AI (or training AI) is the process of training models, artificial intelligence (AI) by providing the data to the model learn and improve the ability to predict, analyze, or the proposed solution for the complex problem. This process includes several steps such as data collection, processing, training, evaluation models to ensure accuracy and efficiency.

Training AI have an important role in the development of smart applications such as chatbot, visual identity, predict trends, or process automation business. It is the key element in the project of transformation of business.

2. Solution train AI professionally

Watch now 4 solutions training AI professional, most common: 

• Training with GPU and TPU
• Using cloud platform
• System dedicated servers
• Software and library support

2.1 Training with GPU and TPU

GPU (Graphics Processing Unit) and TPU (Tensor Processing Unit) is the two types of hardware that is optimized for handling the task parallel as in the process of train AI. GPU with the ability to handle fast graphics help reduce the time training AI, while TPU is designed specifically for machine learning, optimized performance, cost savings.

• GPU (Graphics Processing Unit): GPU capable of parallel processing millions of tasks, helping to significantly speed up the process of training AI. In particular, the GPU is very useful in handling big data and the task as deep learning (deep learning).
• TPU (Tensor Processing Unit): TPU is dedicated hardware developed by Google, optimized for the implementation of algorithms in machine learning. TPU usually have more speed and cost savings than the GPU in the task AI.

2.2 Using cloud platform

Cloud platforms like Google Cloud, AWS or Microsoft Azure to provide resource flexibility with tools AI specialized. Businesses can use the service as GPU/TPU demand, cost savings initial investment and flexibility in the development process.

Advantages of cloud platform:

• Cloud allows businesses to access and manage resources, train AI remote.
• Businesses only pay for the cost of resource use, no need to invest in physical infrastructure.
• Integrated services data analysis and machine learning as AutoML, BigQuery ML.

2.3 System dedicated servers

System dedicated server for AI is designed with high configuration, support multiple GPU or TPU help to ensure performance training, cost optimization.

Highlights of system dedicated server:

• Exclusively designed for the task, AI with powerful hardware.
• Ensuring secure for enterprise data.
• Flexible extension resources as the demand grows.

2.4 software and library support

The framework such as TensorFlow, PyTorch and Keras is the useful tool in the process of train AI. We deliver the items together complete library to build, train and deploy models AI effect.

The software library downloads:

• TensorFlow: Support building machine learning models from basic to the complex.
• PyTorch: Versatile, easy to use, suitable for research and the actual product.
• Keras: Framework, user friendly, help speed up the development process model AI.
• Scikit-learn: Powerful tools for the math machine traditional.

3. Process train AI training model excellent

Process training model AI includes 5 stages, from preparing data to test and deploy models. 

• Step 1: Prepare the data
• Step 2: Select the model Training
• Step 3: Start the training model
• Step 4: Verify training AI
• Step 5: Check the pattern AI

Step 1: Prepare the data

Data preparation is the most important step in the process, train the AI, because of the quality and accuracy of data will directly affect the performance of the model. This phase consists of collecting data, processing, and eventually the annotations to generate data suitable for training.

Collect data

• Data source diversity: Collect data from various sources such as sensors, IoT, customer data base, internal data, or the open source as python questions, UCI Machine Learning Repository. The use of the source data rich help the model has the ability to cover better in fact.
• Quality assurance: Data should be checked and cleaned before use. Remove data or no consistency to avoid interference when training.
• Match formats: Data should be properly formatted standard, such as CSV for data, table PNG/JPG for photos, or JSON for text data.

Data preprocessing

• Remove data noise: Use the cleaning technique to remove null values, excluding outliers, or data not related.
• Standardized data: For example, standardized images of the same size or standardized value of about the same approximately 0.10, 10,1. Through that help model easy to learn and increase performance.
• Enhanced data (Data Augmentation): Perform techniques such as rotate, add noise, Gaussian or make more copies data from the original data to increase diversity, particularly useful with the small data set.

Annotation data

• Label data: For supervised learning, the labeled clear data (such as image classification cat/dog classification, positive/negative, for text) are necessary.
• Tool support: Use tools such as Label Studio or Amazon SageMaker Ground Truth to ensure speed and accuracy when annotated data.

Step 2: Select the model Training

Choose the model train AI is decisive step method that will be used to learn from data. Each problem will require a kind pattern, ranging from basic models to the network in more depth.

• Select model type:
  • With Supervised Learningbusinesses should use models such as Decision Tree, SVM or Neural Network to solve classification or regression.
  • Unsupervised Learning matching problem, clustering data, no labels, use K-means or DBSCAN.
  • Reinforcement Learning suitable with the problem related to optimization strategy, as AI game.
• Weigh pattern available: Leverage the model pretrained as BERT (handle natural language), YOLO (object detection) to save time and cost.
• Determine the complexity: Choose the model must balance between performance and resources. For example: network Neural Network deep requires more computing resources than Logistic Regression.

Step 3: Start the training model

The process of training patterns is where you learn to make predictions from data. This stage needs to be done correctly, with the adjustable hyperparameter and performance monitoring.

• Integration of data and models: Data processed to be put into the pipeline, training, ensure every batch data are trained exactly the design pattern.
• Configuration hyperparameter: Parameters such as learning rate, number of epoch, the size batch should be configured accordingly. For example: learning rate too high can cause instability in the learning process.
• Track performance: Use tools like TensorBoard or WandB to track metrics like loss and accuracy, from which adjust the model if necessary.
• Handle overfitting: Use Dropout layers, or increase the amount of data to avoid the model just memorize the data training without general is.

Step 4: Verify training AI

After the train AI step authentication help reviews models are really effective when applied to new data or not. This is the guarantee period model stable operation before deployment.

• Separate data test: Retain 20%-30% of initial data to data, test, help reviews models in an objective way.
• Cross-validation: Technical use K-fold Cross Validation to test the model on several different datasets, ensuring high reliability.
• Reviews performance metrics: Popular indicators such as F1-score, Precision, Recall, or MAE allows to measure the accuracy and efficiency of the model.

Step 5: Check the pattern AI

Step check is the process of putting the model on real data or test to evaluate the applicability of AI in the real conditions.

• Test on real data: Testing the model with the new data or the fact that hasn't been used in the training process.
• Comparison with expectations: Assess whether the results of the model can meet the business goals, not original.
• Optimize last: If the results are unsatisfactory, tune hyperparameter or improve data quality to increase performance before the official implementation.

4. How to process training AI best effect?

To build process train the AI effect, the following steps should be taken to improve the quality of the model and optimization results. Here are the specific methods that help the process of training AI optimal performance:

4.1 Add new data frequently

To machine learning models operate efficiently, the need to ensure the input data is always updating and rich. Adding new data regularly will help the model learn new traits, to avoid being “outdated” is no longer accurate.

Data can include information from new sources, the situation has not been simulated before that, or the analysis of the market and customer behavior latest. Thanks to that, the model AI can learn from the past, including trends, changes in current.

4.2 enhanced data

Enhance data is a technique that helps improve the quality of data without the need to collect new data. This method can include creating different versions of the original data by applying variations such as rotate, flip, change brightness, or add noise to the image data. 

For text data, can apply techniques such as paraphrasing (reinterpret verses), translate the language (back translation) or extract the characteristic semantic. Enhanced data helps model train AI learned patterns of various information and increase the likelihood of generalization, from which minimize overfitting (the joints), improved accuracy when deployed in real environment.

4.3 Apply the method of active learning

Methods of active learning (active learning) is a powerful strategy to optimize the process, train the AI. In this method, the model AI will choose data difficult to learn and requires a user or system authorized additional label (label) or additional data. 

This method helps the model to focus on the data most important, improving the quality of learning without the need to use the entire data set. Active learning helps to save time and cost when just focus on the form, important information, at the same time improve the performance of the model.

4.4 Upgrade algorithm training

Machine learning algorithms have been increasingly improved to handle the complex issues and the larger set of data. The update algorithm to train the AI, experimenting with new models such as deep learning (deep learning), machine learning unattended (unsupervised learning) or machine learning transfer (transfer learning) can help improve performance significantly, at the same time the ability general of AI. 

In addition, techniques such as adjustable parameters (hyperparameter tuning), optimize the model (model optimization) and apply the learning methods such as learning with limited monitoring (semi-supervised learning) can also help improve the efficiency of training.

5. Solution Server AI Lac Viet – active learning in natural language

In the era of digitization – conversion of the strong, the integration of technology artificial intelligence (AI) into operational processes business become key elements to optimize performance and enhance work productivity. 

Lac Viet Server AI allowed custom (Fine-tune) specifically for the task: OCR, extract data, translation, chatbot,... Business fully complete control over the data put into AI, easy trainer AI fits your specific needs, do not depend on the services of the Tuesday. Add to that infrastructure, advanced technology, processing nearly realtime. AI learning fast the language of natural English, understanding user through the conversation.

Train AI is a critical process to ensure that the model artificial intelligence can meet the business requirements or technical. The selection of training methods right, combined with technology, hardware and cloud platform strong will help businesses optimize effective use of AI, from which promotes the conversion project of the success. To achieve the best results, businesses need to invest in solutions AI integrated features automatic training automatic learning to save the maximum time.

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