Master AI Model Management in IBM Cloud Pack

Table of Contents:

1. Introduction
2. Overview of AI Model Management in IBM Cloud pack
3. Training AI models for automation and problem-solving
4. AI Model Management tool in Cloud pack UI
5. Training AI algorithms
   • 5.1 Trainable AI algorithms
   • 5.2 Pre-trained AI algorithms
6. Setting up training configurations
   • 6.1 Configuring log anomaly detection training
   • 6.2 Selecting training data
   • 6.3 Enabling Incremental training feature
   • 6.4 Filtering data for training
   • 6.5 Setting up automatic training schedule
   • 6.6 Choosing manual or automatic deployment of trained models
7. Running pre-check, training, and deployment for log anomaly detection
8. Data assets and connections in AI Model Management
9. Conclusion

AI Model Management in IBM Cloud pack: Automating Research with AI Algorithms

Artificial Intelligence (AI) has revolutionized the way we solve problems and streamline processes. With the AI Model Management feature in IBM Cloud pack, organizations can leverage powerful AI algorithms to analyze diverse datasets and train models for automated research. This not only reduces manual efforts but also enhances efficiency and accuracy in problem-solving.

Overview of AI Model Management in IBM Cloud pack

IBM Cloud pack provides a comprehensive platform for managing AI algorithms. The AI Model Management feature offers a user-friendly interface to set up training configurations and deploy AI models. By utilizing this tool, organizations can harness the power of AI to automate research tasks and accelerate decision-making processes.

Training AI Models for Automation and Problem-Solving

The primary objective of AI Model Management is to train AI models capable of solving complex problems. Cloud pack for Watson AI Ops supports a wide range of AI algorithms, ensuring compatibility with various data types. From alert seasonality and change risk detection to natural language processing and metric anomaly detection, AI models can be tailored to address a diverse set of challenges.

AI Model Management tool in Cloud pack UI

When accessing the AI Model Management page in the Cloud pack UI, users are presented with a comprehensive interface for managing AI training. The Training tab provides two sections: trainable AI algorithms and pre-trained AI algorithms. Each section offers tiles representing different algorithms, enabling users to create training configurations or enable/disable algorithms as required.

Trainable AI algorithms

Trainable AI algorithms need to be trained on specific data types to create deployable models. Some examples of trainable AI algorithms include log anomaly detection, golden signals, and natural language processing. By clicking on the respective tiles, users can set up training configurations for individual algorithms, customizing the training process based on their requirements.

Configuring log anomaly detection training

Let's take a closer look at setting up a training configuration for log anomaly detection algorithm. By clicking on the "Log Anomaly Detection" tile, users can access the configuration settings. This algorithm detects unusual Patterns in logs and reports them as alerts in the chat Ops interface. To train log anomaly models, a sufficient amount of normal log data is required.

Selecting training data

On the next page, users can select the data they want to use for training. The date picker allows them to choose the start and end ranges of training data. Additionally, the incremental training feature can be enabled to improve the performance of subsequent trainings. The Filter Data Page provides options to exclude specific date ranges, such as anomalous data, which may skew the training process.

Enabling incremental training feature

The incremental training feature enhances the effectiveness of AI models over time. By considering the incremental changes in data, models can adapt and improve their accuracy. Users can choose to enable this feature to ensure their AI models continuously learn and evolve.

Setting up automatic training schedule

To streamline the training process, users can choose to set up an automatic training schedule. By enabling this option, they can specify the start and end dates for training launches, along with the desired frequency of the training runs. This automation ensures that AI models are regularly updated and trained on the latest data.

Choosing manual or automatic deployment of trained models

Once the training configuration is complete, users can choose whether they want the trained models to be deployed manually or automatically. Manual deployment allows users to review and assess the models before deploying them, while automatic deployment ensures a seamless integration of trained models into the existing workflow.

Running pre-check, training, and deployment for log anomaly detection

After setting up the training configuration, users can access the Log Anomaly Detection training definition page. Here, they can run pre-checks to validate the training settings and launch the training process. Once the training completes, the trained models can be deployed for real-time log analysis and anomaly detection.

Data Assets and Connections in AI Model Management

AI Model Management also provides a Data Assets tab, which displays the data connections feeding into AI training. These connections can be set up in the Data and Tools Connection page and are listed with details such as connection name, data flow status, and connection type. This tab offers an overview of the data sources utilized in the training process.

Conclusion

In conclusion, the AI Model Management feature in IBM Cloud pack plays a crucial role in automating research and problem-solving through AI algorithms. By leveraging trainable and pre-trained algorithms, organizations can streamline processes, reduce manual work, and enhance decision-making capabilities. With the ability to configure training, schedule automated updates, and deploy trained models, Cloud pack empowers organizations to harness the full potential of AI in their operations.

🌐 Resources:

• IBM Cloud pack: [insert URL here]

Highlights:

• IBM Cloud pack offers AI Model Management for automating research and problem-solving.
• Trainable AI algorithms allow customization and training for specific datasets.
• Pre-trained AI algorithms are ready to use with default configurations.
• Users can configure training settings, select datasets, and schedule automatic updates.
• Manual or automatic deployment options are available for trained models.
• Data Assets tab provides insights into data connections used in AI training.

FAQ:

Q: What is AI Model Management in IBM Cloud pack? A: AI Model Management is a feature in IBM Cloud pack that enables the training and deployment of AI models for automation and problem-solving.

Q: What are trainable AI algorithms? A: Trainable AI algorithms are AI models that can be trained on specific data types to create deployable models. They can be customized and configured based on the organization's needs.

Q: What are pre-trained AI algorithms? A: Pre-trained AI algorithms are ready-to-use algorithms that come with default configurations. They do not require additional training and can be directly deployed for analysis and problem-solving.

Q: How can I configure the training settings for AI algorithms? A: By accessing the AI Model Management tool in Cloud pack UI, users can select the desired algorithm and customize the training settings, including the selection of training data, enabling incremental training, and setting up automatic training schedules.

Q: What data assets and connections are available in AI Model Management? A: The Data Assets tab in AI Model Management displays the data connections that feed into AI training. These connections can be set up in the Data and Tools Connection page and provide insights into the data sources used for training.