When it comes to managing and troubleshooting Windows systems, having the right tools can make a world of difference. This is especially true for those who work with AI, machine learning, or data science projects on a Windows platform. AI applications can be resource-intensive, and dealing with performance bottlenecks, crashes, or compatibility issues can be a real challenge without the right set of tools.

In this blog post, we’ll cover the four must-have troubleshooting tools that every Windows user — especially AI enthusiasts and tech professionals — should have in their toolkit. These tools will help you identify issues, diagnose problems, and get your system back to peak performance, allowing you to focus on your AI projects rather than dealing with tech headaches.

1. Windows Performance Monitor (PerfMon)

Why It’s Essential:

The Windows Performance Monitor is one of the most powerful tools for monitoring system performance. It’s a built-in utility that provides real-time data on system health, resource usage, and performance metrics. Whether you’re dealing with slow AI model training, lagging applications, or unexpected system behavior, PerfMon is an indispensable resource.

Key Features:

• Real-Time Monitoring: View live system performance, such as CPU usage, memory utilization, disk activity, and network traffic.
• Customizable Data Collection: Set up custom counters to track specific metrics like GPU utilization (via third-party plugins or integrated with NVIDIA’s nvidia-smi tool).
• Detailed Logs: Log performance data over time to identify trends, potential bottlenecks, or irregular spikes in resource consumption.
• Alerts: Set alerts to notify you when specific system metrics exceed predefined thresholds, helping prevent issues before they escalate.

How to Use It for AI Troubleshooting:

• Memory Issues: AI and machine learning processes tend to be memory-intensive. If you’re running large datasets or complex models and experiencing slowdowns or crashes, PerfMon can help you track memory usage and identify whether your system is running out of RAM or virtual memory.
• CPU/GPU Usage: AI workflows can put a heavy load on both CPUs and GPUs. You can track the performance of these components in real-time to determine if they are being overworked, under-utilized, or overheating, and take appropriate action.

2. Event Viewer

Why It’s Essential:

Event Viewer is another built-in Windows tool that provides detailed logs of system, application, and security events. While it’s often overlooked by casual users, it’s a must-have tool for troubleshooting deep system issues, especially those related to hardware failures, software crashes, or driver conflicts — problems that often plague AI-related tasks.

Key Features:

• Comprehensive Event Logs: View logs that cover everything from hardware issues to software failures and security breaches.
• Filterable Entries: You can filter logs by date, type, or specific event sources to quickly pinpoint the cause of the issue.
• Detailed Error Messages: Many AI-related issues stem from specific hardware or software incompatibilities, and Event Viewer provides error codes and descriptions that can help identify the root cause of a problem.
• Error Resolution: Based on error codes or warnings, you can search for fixes on Microsoft’s support site or other tech forums.

How to Use It for AI Troubleshooting:

• Application Crashes: If an AI application (like TensorFlow, PyTorch, or a custom model script) crashes or behaves unexpectedly, Event Viewer logs can give you insight into whether the issue is due to software bugs, memory problems, or even failed driver updates.
• Driver Issues: Event Viewer is particularly useful for identifying faulty drivers, especially GPU drivers, which are crucial for AI tasks that require GPU acceleration. The logs will tell you if a driver failed to load or if there are compatibility issues between software and hardware.

3. Process Explorer

Why It’s Essential:

Developed by Sysinternals (now owned by Microsoft), Process Explorer is a powerful tool that lets you drill down into the details of running processes on your Windows machine. It provides much more in-depth information than the standard Task Manager, making it a critical tool for diagnosing system-level issues that could affect AI applications.

Key Features:

• Detailed Process Information: View the processes that are running, how much CPU, memory, and disk they are consuming, and which services or threads they are associated with.
• Process Hierarchy: See parent-child relationships between processes, which is useful for identifying processes spawned by your AI application or even rogue processes.
• GPU/CPU Utilization: For AI workloads, you can monitor GPU and CPU utilization in real-time, enabling you to spot inefficiencies or over-usage.
• Handles and DLLs: View the handles and DLL files being used by processes. This is especially useful when diagnosing issues related to software incompatibilities or locked files.

How to Use It for AI Troubleshooting:

• Identifying Resource Hogs: If your AI workflow is experiencing poor performance, Process Explorer helps you pinpoint which processes are consuming excessive CPU or memory. For example, if your model training process is unexpectedly slow, you may discover that other applications are competing for resources.
• Tracking GPU Usage: AI applications, particularly deep learning models, rely heavily on GPUs. Process Explorer, when used alongside tools like GPU-Z or NVIDIA’s nvidia-smi, lets you track GPU usage to ensure your AI workloads are using the GPU efficiently.
• Unusual Processes: If something is running on your system that shouldn’t be (e.g., an unknown background process consuming excessive resources), Process Explorer helps you identify it. This is particularly useful for diagnosing malware or unauthorized programs that could be interfering with your AI work.

4. LatencyMon

Why It’s Essential:

LatencyMon is an advanced Windows troubleshooting tool specifically designed to monitor system latency and identify real-time performance issues. While this tool is typically used by audio professionals to diagnose issues related to audio latency, it’s also invaluable for diagnosing system latency problems that can interfere with high-performance AI tasks, such as real-time inference, video processing, or large-scale simulations.

Key Features:

• Real-Time Latency Monitoring: It monitors the system for latency issues in real-time and identifies which drivers or processes are responsible for system delays.
• Detailed Statistics: LatencyMon provides detailed statistics on DPC (Deferred Procedure Call) and ISR (Interrupt Service Routine) times, which are crucial for diagnosing performance bottlenecks.
• Driver and Hardware Analysis: The tool gives you insights into which device drivers are introducing latency, which is particularly important when using GPUs or specialized hardware like TPUs or FPGA devices for AI workloads.
• System Optimization Recommendations: Based on the analysis, LatencyMon can suggest optimizations to improve system responsiveness and reduce latency.

How to Use It for AI Troubleshooting:

• GPU Latency: AI workloads that rely on real-time decision-making (e.g., autonomous driving systems, robotics, etc.) are highly sensitive to latency. LatencyMon can help diagnose if any driver or hardware components are introducing latency that could affect your AI application’s real-time performance.
• System Optimization: By identifying which drivers or system processes are causing delays, you can make targeted optimizations to improve system efficiency — ensuring that your AI workflows run smoothly without lag or unnecessary delays.

What is Event Viewer?

Event Viewer is a built-in Windows utility that logs a wide variety of system, application, security, and hardware events. These logs can help you monitor the health of your system, pinpoint issues, and track application or service failures—information that is not always immediately visible through other diagnostic tools.

For AI developers and tech professionals, Event Viewer can provide detailed insights into performance bottlenecks, hardware errors, software crashes, and even issues related to virtual environments, cloud connectivity, or system security—all of which can affect the performance of data-intensive tasks such as AI model training, data processing, or multi-system orchestration.

Why Event Viewer is Essential for AI Enthusiasts and Tech Professionals

1. Deep Diagnostics Beyond the Surface
   • While Task Manager and Resource Monitor provide real-time stats on CPU, memory, and disk usage, Event Viewer digs deeper, offering a log-based approach that traces the root causes of issues that might be overlooked at the surface level. For example, if an AI model training session fails without a clear error message, Event Viewer can show you exactly when and why the failure occurred—whether it’s due to memory issues, file access errors, or application crashes.
2. Detect and Resolve Software Conflicts
   • Running AI frameworks like TensorFlow, PyTorch, or scikit-learn often requires a mix of third-party libraries, virtual environments, and other system tools. Event Viewer can log conflicts between these components—helping you identify and fix issues that might prevent software from running correctly. You can quickly spot dependency issues, library conflicts, or even faulty drivers that might impact the performance or reliability of your AI workloads.
3. Monitor AI Model Performance and System Resources
   • For those working with resource-hungry AI tasks, keeping track of system errors and warnings is vital. Event Viewer records detailed logs that help you monitor CPU usage spikes, memory leaks, disk bottlenecks, or even network failures that can impact AI model performance. For example, if you’re training a large neural network and notice that it’s underperforming, Event Viewer logs might reveal a system failure related to GPU memory, disk space, or network latency.
4. Track Hardware Failures
   • AI systems are often built on specialized hardware (e.g., GPUs, high-speed SSDs) that can be subject to wear and tear, especially when running intensive tasks for prolonged periods. Event Viewer logs any hardware malfunctions, providing insights into driver errors, hardware failures, or even overheating issues, all of which can affect AI model training, inference, or system stability.
5. Security and Access Logs for Multi-User Systems
   • Many AI professionals work in environments where multiple users have access to the system or when systems are part of a larger network. Event Viewer helps monitor system access, user logins, permission errors, and potential security breaches. This is crucial for both private workstations and cloud-based AI environments, where unauthorized access could compromise sensitive data or intellectual property.

PowerShell + Windows Terminal, GitHub, and Copilot: Supercharging Your Workflow for AI Enthusiasts and Tech Professionals

PowerShell: The Command-Line Powerhouse

PowerShell is a powerful, flexible command-line interface (CLI) and scripting language that is built into Windows, but it’s also cross-platform (Windows, macOS, Linux). For AI professionals and developers, PowerShell is not just a terminal; it’s a full-fledged scripting environment capable of automating tasks, managing files, configuring system settings, and interacting with APIs—all with a few lines of code.

Why PowerShell is Essential:

1. Automating Repetitive Tasks: PowerShell scripts can automate mundane tasks like downloading datasets, setting up virtual environments, or clearing temporary files, saving you time when you’re working on multiple AI projects.
2. Managing Dependencies: AI development often involves setting up complex dependencies (e.g., Python, TensorFlow, Jupyter, Docker). PowerShell can help automate the installation of these dependencies, ensuring a consistent environment across different machines and projects.
3. System Management: PowerShell is great for managing Windows-based systems, especially when performing routine tasks like file manipulation, process management, network diagnostics, or checking for system resource availability. This is crucial when working with resource-intensive AI workloads.
4. Interacting with APIs: With PowerShell, you can quickly send HTTP requests to cloud services, download data from APIs, or interact with various web services—perfect for when you’re pulling data from external sources for your AI models.

Example PowerShell Commands for AI:

• Install a Python virtual environment:
  powershell

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  python -m venv myenv

• Install dependencies:
  powershell

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  pip install -r requirements.txt

• Download a dataset from a URL:
  powershell

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  Invoke-WebRequest -Uri "http://example.com/dataset.zip" -OutFile "C:\datasets\dataset.zip"

• Start a Jupyter notebook:
  powershell

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  jupyter notebook


PowerShell Scripts for AI Workflows:

• Automate model training tasks, dataset preprocessing, or environment setup using custom PowerShell scripts.
• Schedule tasks using Task Scheduler combined with PowerShell to periodically run machine learning jobs or backup AI project files.

Windows Terminal: The Modern, Multi-Tab Command-Line Experience

While PowerShell is the powerhouse for scripting and automation, Windows Terminal is the sleek, modern interface that makes working with PowerShell, Command Prompt, Git Bash, and other CLI tools a breeze. It brings together different command-line experiences into one window with tabbing support, themes, customizations, and more.

Why Windows Terminal is a Game-Changer:

1. Multiple Tabs: You can run PowerShell, Command Prompt, Git Bash, and even WSL (Windows Subsystem for Linux) side-by-side in different tabs within the same window. This is especially useful when you’re juggling different tasks—say, you’re running a Python script in one tab while monitoring system performance with PowerShell in another.
2. Customization: Windows Terminal allows you to customize your environment to suit your preferences, whether it’s changing the color scheme, font, or even setting up specific profiles for different environments like Python virtual environments, Git repositories, or Docker containers.
3. Improved Productivity: With features like split panes, keyboard shortcuts, and high-performance rendering, you can work more efficiently and effectively. For AI professionals, this means running multiple model training sessions, managing databases, and interacting with cloud services—all in one unified interface.
4. Access to WSL: If you’re working in a mixed environment (Windows + Linux), Windows Terminal makes it easy to access and manage Windows Subsystem for Linux (WSL). This is great for AI developers who use Linux-based tools (such as TensorFlow on Ubuntu) but want the convenience of working in Windows.

How to Use Windows Terminal:

• Open Multiple Tabs: Click the “+” button to open new tabs with different shells.
• Split Panes: Use the Alt + Shift + D shortcut to split your terminal window into multiple panes, allowing you to run multiple commands in parallel.
• Create Custom Profiles: Customize profiles for different environments (e.g., Python, TensorFlow, Jupyter), specifying startup commands and themes for each.

GitHub: The Heart of Version Control and Collaboration

When it comes to collaboration, GitHub is a must-have for any tech professional or AI enthusiast. It’s the most widely-used platform for version control, and it’s particularly powerful for managing complex, collaborative AI projects.

Why GitHub is Critical for AI Workflows:

1. Version Control: GitHub helps track changes to your AI code, models, datasets, and experiments. It provides a full history of changes made to your project, which is invaluable for debugging, collaboration, or revisiting previous versions of your AI model.
2. Collaboration: AI projects often involve teams of developers and data scientists. GitHub allows easy collaboration with features like pull requests, code reviews, issue tracking, and team management.
3. Integration with Other Tools: GitHub integrates seamlessly with a variety of tools commonly used in AI workflows, such as continuous integration/continuous deployment (CI/CD) tools, Docker, Kubernetes, and cloud services. This enables automated testing, deployment, and scaling of AI models.
4. Open Source AI Projects: GitHub is home to countless open-source AI projects. As a developer, you can leverage these projects, contribute to them, or even fork repositories to build your own models. Whether it’s a pre-trained model or a library like fastai, GitHub makes it easy to find, share, and collaborate on AI code.

Example GitHub Workflow for AI Projects:

1. Clone a repository:
   bash

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   git clone https://github.com/username/repository.git

2. Create a new branch for a feature:
   bash

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   git checkout -b new-feature

3. Push changes to GitHub:
   bash

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   git add .
git commit -m "Implemented new feature"
git push origin new-feature

4. Merge a pull request: Once the changes are reviewed and approved, you can merge them into the main branch.