GET https://rct.dev.bbntimes.com/science/creating-ai-models-from-data-to-deployment

ArticleController :: show

Request

GET Parameters

None

POST Parameters

None

Uploaded Files

None

Request Attributes

Key Value
_controller
"App\Controller\ArticleController::show"
_firewall_context
"security.firewall.map.context.main"
_links
Symfony\Component\WebLink\GenericLinkProvider {#3589
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    3710 => Symfony\Component\WebLink\Link {#3710
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      -attributes: [
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    3708 => Symfony\Component\WebLink\Link {#3708
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      -attributes: [
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    3707 => Symfony\Component\WebLink\Link {#3707
      -href: "/build/502.js"
      -rel: [
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      -attributes: [
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    3706 => Symfony\Component\WebLink\Link {#3706
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      -attributes: [
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    3705 => Symfony\Component\WebLink\Link {#3705
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      -attributes: [
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    3704 => Symfony\Component\WebLink\Link {#3704
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      -attributes: [
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      ]
    }
    3703 => Symfony\Component\WebLink\Link {#3703
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      -rel: [
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      ]
      -attributes: [
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      ]
    }
    3702 => Symfony\Component\WebLink\Link {#3702
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      -rel: [
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      -attributes: [
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      ]
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    3701 => Symfony\Component\WebLink\Link {#3701
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      -rel: [
        "preload" => "preload"
      ]
      -attributes: [
        "as" => "style"
      ]
    }
    3700 => Symfony\Component\WebLink\Link {#3700
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      -rel: [
        "preload" => "preload"
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      -attributes: [
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      ]
    }
    3699 => Symfony\Component\WebLink\Link {#3699
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      -rel: [
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      -attributes: [
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    3698 => Symfony\Component\WebLink\Link {#3698
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      -rel: [
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      -attributes: [
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      ]
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    3697 => Symfony\Component\WebLink\Link {#3697
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      -rel: [
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}
_route
"article_show"
_route_params
[
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]
_security_firewall_run
"_security_main"
_stopwatch_token
"ab8393"
category
"science"
slug
"creating-ai-models-from-data-to-deployment"

Request Headers

Header Value
accept
"*/*"
accept-encoding
"gzip, br, zstd, deflate"
connection
"close"
host
"rct.dev.bbntimes.com"
user-agent
"Mozilla/5.0 AppleWebKit/537.36 (KHTML, like Gecko; compatible; ClaudeBot/1.0; +claudebot@anthropic.com)"
x-php-ob-level
"1"

Request Content

Request content not available (it was retrieved as a resource).

Response

Response Headers

Header Value
cache-control
"no-cache, private"
content-type
"text/html; charset=UTF-8"
date
"Thu, 17 Apr 2025 04:28:26 GMT"
link
"</build/runtime.js>; rel="preload"; as="script",</build/644.js>; rel="preload"; as="script",</build/502.js>; rel="preload"; as="script",</build/app.js>; rel="preload"; as="script",</build/view-more.js>; rel="preload"; as="script",</build/term-condition.js>; rel="preload"; as="script",</build/contact.js>; rel="preload"; as="script",</build/scroll-infinite-article.js>; rel="preload"; as="script",</build/app.css>; rel="preload"; as="style",</build/cookie-style.css>; rel="preload"; as="style",</build/term-condition-css.css>; rel="preload"; as="style",</build/contact-css.css>; rel="preload"; as="style",</build/comment-css.css>; rel="preload"; as="style""
x-debug-token
"ca6c09"

Cookies

Request Cookies

No request cookies

Response Cookies

No response cookies

Session 11

Session Metadata

Key Value
Created
"Thu, 17 Apr 25 04:28:25 +0000"
Last used
"Thu, 17 Apr 25 04:28:25 +0000"
Lifetime
0

Session Attributes

Attribute Value
_csrf/https-comment
"KXj4s80FEA0dzNYva8gfFHLegDgU7F7UTgJMNkUsncI"
_csrf/https-cookie_accept
"ZAVpoEWqLSEGI6dwla68w5-dO8yC7R2uf-LL-ZcB9gw"

Session Usage

11 Usages
Stateless check enabled
Usage
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Symfony\Component\Security\Csrf\TokenStorage\SessionTokenStorage:79
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App\Entity\Article {#1094
  -id: 17778
  -title: "Creating AI Models: From Data to Deployment"
  -slug: "creating-ai-models-from-data-to-deployment"
  -introtext: "<p>The ability to transform raw data into intelligent and actionable AI models is a crucial skill.</p>\r\n"
  -content: """
    \r\n
    <p>This multi-step process involves a combination of hardware resources, software tools, and mathematical techniques. Let's explore the journey from data to deployed AI applications.</p>\r\n
    <p><img src="/images/The_journey_from_data_to_deployed_AI_applications.png" alt="The_journey_from_data_to_deployed_AI_applications.png" width="901" height="586" /></p>\r\n
    <p id="ember46" class="ember-view reader-content-blocks__paragraph"><strong>Step 1</strong>:<span class="white-space-pre"> </span><strong>Data Collection and Preprocessing</strong>: The first step in creating an AI model is to gather and prepare the data that will be used for training. This involves:</p>\r\n
    <p id="ember47" class="ember-view reader-content-blocks__paragraph">a. Data Acquisition: Obtaining relevant data from various sources, such as databases, APIs, sensors, or manual data collection processes.</p>\r\n
    <p id="ember48" class="ember-view reader-content-blocks__paragraph">b. Data Cleaning: Handling missing values, removing duplicates, and addressing inconsistencies or errors in the data.</p>\r\n
    <p id="ember49" class="ember-view reader-content-blocks__paragraph">c. Data Transformation: Converting the data into a suitable format for model training, such as numerical encoding for categorical variables or normalization/scaling for numerical features.</p>\r\n
    <p id="ember50" class="ember-view reader-content-blocks__paragraph">d. Data Splitting: Dividing the dataset into training, validation, and testing subsets for model training, evaluation, and testing purposes.</p>\r\n
    <p id="ember51" class="ember-view reader-content-blocks__paragraph"><strong>Hardware</strong>: High-capacity storage systems (e.g., cloud storage, network-attached storage) and computing resources for data processing.</p>\r\n
    <p id="ember52" class="ember-view reader-content-blocks__paragraph"><strong>Software</strong>: Data wrangling tools (e.g., Python libraries like Pandas, Numpy), database management systems, data cleaning scripts.</p>\r\n
    <p id="ember53" class="ember-view reader-content-blocks__paragraph"><strong>Step 2</strong>:<span class="white-space-pre"> </span><strong>Model Selection and Architecture Design</strong>: Next, you need to choose an appropriate model architecture based on the problem domain and the characteristics of the data. Some common model types include:</p>\r\n
    <p id="ember54" class="ember-view reader-content-blocks__paragraph">·&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Feedforward Neural Networks</p>\r\n
    <p id="ember55" class="ember-view reader-content-blocks__paragraph">·&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Convolutional Neural Networks (CNNs) for image data</p>\r\n
    <p id="ember56" class="ember-view reader-content-blocks__paragraph">·&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Recurrent Neural Networks (RNNs) or Transformers for sequence data</p>\r\n
    <p id="ember57" class="ember-view reader-content-blocks__paragraph">·&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Generative Adversarial Networks (GANs) for generative modeling</p>\r\n
    <p id="ember58" class="ember-view reader-content-blocks__paragraph">The model architecture involves determining the number of layers, types of layers (e.g., convolutional, pooling, dense), activation functions, and other hyperparameters.</p>\r\n
    <p id="ember59" class="ember-view reader-content-blocks__paragraph"><strong>Hardware</strong>: Powerful CPU/GPU systems for training and experimenting with different model architectures.</p>\r\n
    <p id="ember60" class="ember-view reader-content-blocks__paragraph"><strong>Software</strong>: Deep learning frameworks (e.g., TensorFlow, PyTorch, Keras), model visualization tools.</p>\r\n
    <p id="ember61" class="ember-view reader-content-blocks__paragraph"><strong>Step 3</strong>:<span class="white-space-pre"> </span><strong>Model Training</strong>: Once the model architecture is defined, the next step is to train the model using the prepared training data. This typically involves:</p>\r\n
    <p id="ember62" class="ember-view reader-content-blocks__paragraph">a. Defining the Loss Function: Selecting an appropriate loss function (e.g., mean squared error for regression, cross-entropy for classification) to measure the model's performance during training.</p>\r\n
    <p id="ember63" class="ember-view reader-content-blocks__paragraph">b. Optimization Algorithm: Choosing an optimization algorithm (e.g., Stochastic Gradient Descent, Adam) to update the model's parameters and minimize the loss function.</p>\r\n
    <p id="ember64" class="ember-view reader-content-blocks__paragraph">c. Training Loop: Implementing a training loop that iteratively feeds the training data to the model, computes the loss, and updates the model's parameters using the optimization algorithm.</p>\r\n
    <p id="ember65" class="ember-view reader-content-blocks__paragraph">d. Regularization: Applying techniques like dropout, L1/L2 regularization, or early stopping to prevent overfitting and improve model generalization.</p>\r\n
    <p id="ember66" class="ember-view reader-content-blocks__paragraph">e. Validation: Periodically evaluating the model's performance on the validation set to monitor its progress and tune hyperparameters if necessary.</p>\r\n
    <p id="ember67" class="ember-view reader-content-blocks__paragraph"><strong>Hardware</strong>: High-performance computing clusters or cloud instances with multiple GPUs for parallel training.</p>\r\n
    <p id="ember68" class="ember-view reader-content-blocks__paragraph"><strong>Software</strong>: Deep learning frameworks, optimization libraries (e.g., TensorFlow Probability, PyTorch Lightning), data parallelization tools.</p>\r\n
    <p id="ember69" class="ember-view reader-content-blocks__paragraph"><strong>Mathematical Concepts</strong>:</p>\r\n
    <p id="ember70" class="ember-view reader-content-blocks__paragraph">- Loss Functions (e.g., mean squared error, cross-entropy)</p>\r\n
    <p id="ember71" class="ember-view reader-content-blocks__paragraph">- Optimization Algorithms (e.g., Stochastic Gradient Descent, Adam)</p>\r\n
    <p id="ember72" class="ember-view reader-content-blocks__paragraph">- Regularization Techniques (e.g., dropout, L1/L2 regularization)</p>\r\n
    <p id="ember73" class="ember-view reader-content-blocks__paragraph"><strong>Step 4</strong>:<span class="white-space-pre"> </span><strong>Model Evaluation and Tuning</strong>: After training, the model's performance is evaluated on the held-out test set to assess its generalization capability. This step involves:</p>\r\n
    <p id="ember74" class="ember-view reader-content-blocks__paragraph">a. Performance Metrics: Selecting appropriate evaluation metrics (e.g., accuracy, precision, recall, F1-score, mean squared error) based on the problem domain.</p>\r\n
    <p id="ember75" class="ember-view reader-content-blocks__paragraph">b. Evaluation on Test Set: Computing the chosen performance metrics on the test set to obtain an unbiased estimate of the model's performance.</p>\r\n
    <p id="ember76" class="ember-view reader-content-blocks__paragraph">c. Hyperparameter Tuning: If the model's performance is unsatisfactory, adjusting the hyperparameters (e.g., learning rate, batch size, number of layers) and retraining the model to improve its performance.</p>\r\n
    <p id="ember77" class="ember-view reader-content-blocks__paragraph">d. Model Comparison: Comparing the performance of different model architectures or techniques to select the best-performing model for the given problem.</p>\r\n
    <p id="ember78" class="ember-view reader-content-blocks__paragraph"><strong>Hardware</strong>: Dedicated testing/validation systems or cloud instances.</p>\r\n
    <p id="ember79" class="ember-view reader-content-blocks__paragraph"><strong>Software</strong>: Evaluation libraries (e.g., Scikit-learn, TensorFlow Evaluation), visualization tools (e.g., TensorBoard), hyperparameter tuning frameworks (e.g., Keras Tuner, Ray Tune).</p>\r\n
    <p id="ember80" class="ember-view reader-content-blocks__paragraph"><strong>Mathematical Concepts</strong>:</p>\r\n
    <p id="ember81" class="ember-view reader-content-blocks__paragraph">- Performance Metrics (e.g., accuracy, precision, recall, F1-score)</p>\r\n
    <p id="ember82" class="ember-view reader-content-blocks__paragraph">- Statistical Techniques for Model Comparison and Selection</p>\r\n
    <p id="ember83" class="ember-view reader-content-blocks__paragraph"><strong>Step 5</strong>:<span class="white-space-pre"> </span><strong>Model Deployment and Integration</strong>: Once a satisfactory model is obtained, it can be deployed and integrated into applications or production systems. This step may involve:</p>\r\n
    <p id="ember84" class="ember-view reader-content-blocks__paragraph">a. Model Serialization: Saving the trained model's architecture and weights to a file or database for later use or deployment.</p>\r\n
    <p id="ember85" class="ember-view reader-content-blocks__paragraph">b. Application Integration: Integrating the trained model into the target application or system, typically by exposing an API or embedding the model within the application's codebase.</p>\r\n
    <p id="ember86" class="ember-view reader-content-blocks__paragraph">c. Model Monitoring: Implementing monitoring mechanisms to track the model's performance in the deployed environment and detect any potential issues or performance degradation over time.</p>\r\n
    <p id="ember87" class="ember-view reader-content-blocks__paragraph">d. Model Updating: Establishing a process for retraining or updating the model with new data as it becomes available, ensuring the model remains up-to-date and accurate.</p>\r\n
    <p id="ember88" class="ember-view reader-content-blocks__paragraph">Throughout these steps, various mathematical concepts and techniques may be employed, such as:</p>\r\n
    <p id="ember89" class="ember-view reader-content-blocks__paragraph">·&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Linear algebra operations for matrix computations and neural network calculations</p>\r\n
    <p id="ember90" class="ember-view reader-content-blocks__paragraph">·&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Probability theory and statistics for loss functions and evaluation metrics</p>\r\n
    <p id="ember91" class="ember-view reader-content-blocks__paragraph">·&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Calculus and optimization techniques for gradient-based optimization algorithms</p>\r\n
    <p id="ember92" class="ember-view reader-content-blocks__paragraph">·&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Algorithms for data preprocessing, feature engineering, and dimensionality reduction</p>\r\n
    <p id="ember93" class="ember-view reader-content-blocks__paragraph"><strong>Ethical Considerations</strong>:</p>\r\n
    <p id="ember94" class="ember-view reader-content-blocks__paragraph">- Data Privacy and Security</p>\r\n
    <p id="ember95" class="ember-view reader-content-blocks__paragraph">- Bias Mitigation and Fairness</p>\r\n
    <p id="ember96" class="ember-view reader-content-blocks__paragraph">- Model Interpretability and Transparency</p>\r\n
    <p id="ember97" class="ember-view reader-content-blocks__paragraph">- Responsible AI Practices</p>\r\n
    <p id="ember98" class="ember-view reader-content-blocks__paragraph">While the specific details and implementations may vary depending on the problem domain, data characteristics, and chosen model architecture, the general process outlined above serves as a framework for creating AI models from data to applications.</p>\r\n
    <p id="ember99" class="ember-view reader-content-blocks__paragraph">It's important to note that the process of creating an AI model is iterative, and multiple iterations of model selection, training, evaluation, and tuning may be required to achieve satisfactory performance. Additionally, careful consideration must be given to ethical and responsible AI practices, such as ensuring data privacy, mitigating biases, and maintaining transparency and interpretability of the models.</p>\r\n
    <p id="ember100" class="ember-view reader-content-blocks__paragraph">The creation of AI models is an iterative process, often requiring multiple cycles of model selection, training, evaluation, and tuning to achieve satisfactory performance. Additionally, careful consideration must be given to ethical and responsible AI practices, such as ensuring data privacy, mitigating biases, and maintaining transparency and interpretability of the models.</p>\r\n
    <p id="ember101" class="ember-view reader-content-blocks__paragraph">By leveraging the appropriate hardware resources, software tools, and mathematical foundations, data scientists and machine learning engineers can effectively transform data into powerful AI models that can be integrated into a wide range of applications, driving innovation and solving complex problems across various domains.</p>\r\n
    <p id="ember102" class="ember-view reader-content-blocks__paragraph"><strong>Putting It All Together: An End-to-End Example</strong></p>\r\n
    <p id="ember103" class="ember-view reader-content-blocks__paragraph">To illustrate the end-to-end process, let's consider a real-world example:<span class="white-space-pre"> </span><em>building an AI model for image classification to automatically identify different types of vehicles</em>.</p>\r\n
    <p id="ember104" class="ember-view reader-content-blocks__paragraph"><strong>Step 1: Data Collection and Preprocessing</strong></p>\r\n
    <p id="ember105" class="ember-view reader-content-blocks__paragraph">- Acquire a dataset of labeled vehicle images from various sources (e.g., online repositories, crowdsourcing)</p>\r\n
    <p id="ember106" class="ember-view reader-content-blocks__paragraph">- Use data cleaning scripts (e.g., Python's OpenCV library) to remove corrupted or low-quality images</p>\r\n
    <p id="ember107" class="ember-view reader-content-blocks__paragraph">- Preprocess images (e.g., resizing, normalization) using libraries like TensorFlow's<span class="white-space-pre"> </span><a class="app-aware-link " href="http://tf.data/" target="_self" data-test-app-aware-link>tf.data</a><span class="white-space-pre"> </span>pipeline</p>\r\n
    <p id="ember108" class="ember-view reader-content-blocks__paragraph"><strong>Step 2: Model Selection and Architecture Design</strong></p>\r\n
    <p id="ember109" class="ember-view reader-content-blocks__paragraph">- Choose a Convolutional Neural Network (CNN) architecture (e.g., ResNet, VGGNet) suitable for image classification tasks</p>\r\n
    <p id="ember110" class="ember-view reader-content-blocks__paragraph">- Design the model architecture, specifying the number of convolutional and dense layers, filter sizes, and activation functions</p>\r\n
    <p id="ember111" class="ember-view reader-content-blocks__paragraph">- Visualize and experiment with different architectures using tools like TensorBoard or Keras' model visualization utilities</p>\r\n
    <p id="ember112" class="ember-view reader-content-blocks__paragraph"><strong>Step 3: Model Training</strong></p>\r\n
    <p id="ember113" class="ember-view reader-content-blocks__paragraph">- Split the preprocessed dataset into training, validation, and test sets</p>\r\n
    <p id="ember114" class="ember-view reader-content-blocks__paragraph">- Define an appropriate loss function (e.g., categorical cross-entropy) and optimization algorithm (e.g., Adam)</p>\r\n
    <p id="ember115" class="ember-view reader-content-blocks__paragraph">- Train the model on the training set using a GPU-accelerated deep learning framework like TensorFlow or PyTorch</p>\r\n
    <p id="ember116" class="ember-view reader-content-blocks__paragraph">- Apply regularization techniques (e.g., dropout, data augmentation) to improve generalization</p>\r\n
    <p id="ember117" class="ember-view reader-content-blocks__paragraph">- Monitor training progress and periodically evaluate on the validation set</p>\r\n
    <p id="ember118" class="ember-view reader-content-blocks__paragraph"><strong>Step 4: Model Evaluation and Tuning</strong></p>\r\n
    <p id="ember119" class="ember-view reader-content-blocks__paragraph">- Evaluate the trained model's performance on the held-out test set using metrics like accuracy, precision, recall, and F1-score</p>\r\n
    <p id="ember120" class="ember-view reader-content-blocks__paragraph">- Utilize visualization tools (e.g., confusion matrices, saliency maps) to analyze the model's predictions and identify potential issues</p>\r\n
    <p id="ember121" class="ember-view reader-content-blocks__paragraph">- Experiment with different hyperparameters (e.g., learning rate, batch size) and model architectures using tuning frameworks like Keras Tuner or Ray Tune</p>\r\n
    <p id="ember122" class="ember-view reader-content-blocks__paragraph">- Select the best-performing model based on the evaluation results</p>\r\n
    <p id="ember123" class="ember-view reader-content-blocks__paragraph"><strong>Step 5: Model Deployment and Integration</strong></p>\r\n
    <p id="ember124" class="ember-view reader-content-blocks__paragraph">- Serialize the trained model using TensorFlow Serving or ONNX for deployment</p>\r\n
    <p id="ember125" class="ember-view reader-content-blocks__paragraph">- Deploy the model to a cloud platform (e.g., AWS SageMaker, Google AI Platform) or edge device using containerization tools like Docker</p>\r\n
    <p id="ember126" class="ember-view reader-content-blocks__paragraph">- Integrate the deployed model into the target application or system (e.g., a vehicle recognition app) by exposing an API or embedding the model</p>\r\n
    <p id="ember127" class="ember-view reader-content-blocks__paragraph">- Implement monitoring and logging mechanisms to track the model's performance in production and detect potential issues or performance degradation over time</p>\r\n
    <p id="ember128" class="ember-view reader-content-blocks__paragraph">By following this end-to-end workflow and leveraging the appropriate hardware, software, and mathematical foundations, data scientists and machine learning engineers can create powerful AI models that can be successfully integrated into real-world applications, driving innovation and solving complex problems in domains like transportation, manufacturing, and beyond.</p>
    """
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