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Please explain the following from UiPath AI Center-

  1. Data Labelling
  2. Pipelines
  3. ML Skills
  4. ML logs
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@Ritaman_Baral

  1. Data Labelling: Data labelling is the process of annotating and tagging data to make it usable for training machine learning models. In the context of AI Center, data labelling refers to the capability of collecting and preparing labeled data for machine learning tasks. You can use AI Center’s data labelling capabilities to define the data that your models need to learn from and create datasets with annotated data to be used for training.
  2. Pipelines: Pipelines in UiPath AI Center are workflows designed to automate the end-to-end process of building, training, and deploying machine learning models. They provide a visual representation of the entire machine learning workflow, including data preparation, model training, evaluation, and deployment. Pipelines help you automate and streamline the machine learning lifecycle, making it easier to manage and maintain AI models.
  3. ML Skills: ML Skills are pre-built, reusable components in UiPath AI Center that encapsulate machine learning capabilities. They act as building blocks for creating more complex AI models. ML Skills cover various tasks such as image recognition, sentiment analysis, named entity recognition (NER), and more. With ML Skills, you can leverage pre-built machine learning models and integrate them into your automation workflows easily.
  4. ML Logs: ML Logs are logs generated during the training and evaluation of machine learning models in UiPath AI Center. They provide detailed information about the model training process, including metrics, performance results, and any potential errors or warnings. ML Logs are essential for monitoring the performance of your models, identifying issues, and improving the accuracy of the trained models.

Hope it helps!!

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Hi @Ritaman_Baral

ML Skills: ML Skills are pre-built machine learning components in UiPath AI Center that encapsulate machine learning models or algorithms to perform specific tasks. ML Skills act as reusable building blocks that can be integrated into UiPath workflows and pipelines to add AI capabilities without the need for extensive data science knowledge.

UiPath provides a collection of pre-built ML Skills for various use cases, such as text classification, object detection, sentiment analysis, and more

Pipelines:
In UiPath AI Center, pipelines are workflows that define the end-to-end process for deploying and managing machine learning models in production. A pipeline typically consists of multiple stages or steps, each representing different tasks or activities involved in the machine learning workflow, such as data preparation, model training, evaluation, deployment, and monitoring.

Check the below docs might help you

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@Ritaman_Baral

  • Data Labelling: Data labelling is the process of manually labeling data so that it can be used to train machine learning models. This involves identifying the features of the data and assigning them labels.
  • Pipelines: A pipeline is a sequence of steps that are used to process data. In UiPath AI Center, pipelines are used to train and deploy machine learning models.
  • ML Skills: An ML skill is a machine learning model that is packaged and ready to use. ML skills can be used in UiPath workflows to automate tasks.
  • ML Logs: ML logs are a record of the activity of machine learning models. They can be used to troubleshoot problems with machine learning models and to track the performance of machine learning models over time.

Here are some additional details about each of these terms:

  • Data Labelling: Data labelling is a critical step in the machine learning process. It is important to label the data carefully so that the machine learning model can learn from it correctly. There are a number of different methods for data labelling, including manual labelling, crowdsourcing, and active learning.
  • Pipelines: Pipelines are a powerful way to automate the machine learning process. They can be used to train and deploy machine learning models, as well as to monitor the performance of machine learning models. Pipelines can be made up of a variety of different activities, including data labelling activities, machine learning activities, and deployment activities.
  • ML Skills: ML skills are a convenient way to use machine learning models in UiPath workflows. They are pre-trained machine learning models that have been packaged and made available for use. ML skills can be used to automate a variety of tasks, such as classifying text, predicting outcomes, and identifying objects.
  • ML Logs: ML logs are a valuable resource for troubleshooting problems with machine learning models and for tracking the performance of machine learning models over time. ML logs can be used to see how the machine learning model is making decisions, as well as to see how the machine learning model is performing on different datasets.

https://docs.uipath.com/ai-center/automation-cloud/latest/user-guide/using-ai-center

Refer this url so that you learn more about Data labling, Pipelines.,

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Hi @Ritaman_Baral

  1. Data Labelling:
    Data labelling in UiPath AI Center means tagging or marking data to create a labeled dataset. It’s like putting labels on your data so that the computer can understand and learn from it. For example, if you have pictures of dogs and cats, data labelling involves telling the computer which pictures have dogs and which have cats.

  2. Pipelines:
    In UiPath AI Center, pipelines are like step-by-step recipes that tell the computer how to use AI to solve a problem. It’s like a workflow that combines different AI tasks, such as preparing data, training the AI model, and making predictions, in a clear order. Pipelines make it easier to automate AI processes and get results faster.

  3. ML Skills:
    ML Skills are like ready-to-use AI superpowers in UiPath AI Center. They are pre-built tools that you can use in your automation projects without needing to create everything from scratch. Just like Lego blocks, you can add ML Skills to your workflows to make your automation smarter. For example, you can add a text classification ML Skill to understand and categorize text automatically.

  4. ML Logs in UiPath:
    ML Logs are like notes or records of what happens when the computer uses AI models. They keep track of how the AI model is doing, what decisions it’s making, and if there are any issues. ML Logs help developers and data scientists understand how well the AI model is performing and what changes might be needed to make it better.

UiPath AI Center simplifies the use of AI and machine learning in your automation projects, making it easier to build intelligent and efficient automation solutions.

Hope you understand!!
Regards,

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Hi @Ritaman_Baral

=> Data Labelling - Data Labeling is a tab where you can deploy labeling sessions to prepare the datasets for training and evaluation. Within the current version, you can deploy Document Manager sessions to build Document Understanding models

=>PipeLines - A pipeline is a description of an ML workflow, including all of the functions in the workflow and the order of execution of these functions. The Pipeline includes the definition of the inputs required to run the pipeline and outputs to get from this pipeline.

A Pipeline Run is an execution of a pipeline based on code provided by the user. Once completed a Pipeline Run will have associated outputs and logs

=> ML Skills - An ML Skill is a live deployment of an ML Package. It can be used in an RPA workflow simply by dragging and dropping an ML Skill Activity in UiPath Studio.

=> ML Logs - In the context of UiPath and Machine Learning (ML), logging plays a crucial role in tracking and understanding the behavior of ML models during training and deployment. Logging in ML usually involves recording various metrics, outputs, and events generated during the model training and prediction process.

When using ML models in UiPath workflows, developers may implement custom logging mechanisms to capture important information during the ML model’s execution. This logging can include data such as input features, model predictions, confidence scores, and any other relevant information to monitor and analyze the model’s performance.

Hope it helps!!

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