--- title: "determined vs ai-engineering-from-scratch" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/determined-ai-determined-vs-rohitg00-ai-engineering-from-scratch" tools: ["determined-ai-determined", "rohitg00-ai-engineering-from-scratch"] --- # determined vs ai-engineering-from-scratch *GraphCanon updated Sep 30, 2026* ## Verdict Pick determined if determined is an open-source machine learning platform that simplifies distributed training and hyperparameter tuning for PyTorch and TensorFlow applications; pick ai-engineering-from-scratch if ai-engineering-from-scratch is a comprehensive course that teaches AI engineering skills from foundational math to advanced AI agents and machine learning techniques, using Python and Node.js for interactive learning and. [determined](https://determined.ai) reports 3.2k GitHub stars, 375 forks, and 108 open issues, last pushed Mar 20, 2025. [ai-engineering-from-scratch](https://aiengineeringfromscratch.com) has 55k stars, 9.6k forks, and 114 open issues, last pushed Sep 7, 2026. Figures are from public GitHub metadata via [determined's repository](https://github.com/determined-ai/determined) and [ai-engineering-from-scratch's repository](https://github.com/rohitg00/ai-engineering-from-scratch). | | [determined](/tools/determined-ai-determined.md) | [ai-engineering-from-scratch](/tools/rohitg00-ai-engineering-from-scratch.md) | | --- | --- | --- | | Tagline | An open-source machine learning platform for distributed training and resource management. | Learn, build, and deploy AI engineering skills from scratch. | | Stars | 3,241 | 54,935 | | Forks | 375 | 9,649 | | Open issues | 108 | 114 | | Language | Go | Python | | Adopt for | Determined is an open-source machine learning platform that simplifies distributed training and hyperparameter tuning for PyTorch and TensorFlow applications. | ai-engineering-from-scratch is a comprehensive course that teaches AI engineering skills from foundational math to advanced AI agents and machine learning techniques, using Python and Node.js for interactive learning and | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | MIT | | Categories | Evaluation & Observability, Model Training | AI Agents, Computer Vision, Developer Tools, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [determined](/tools/determined-ai-determined.md) | [ai-engineering-from-scratch](/tools/rohitg00-ai-engineering-from-scratch.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Active (82%) | | Days since push | 558d | 10d | | Open issues (now) | 108 | 114 | | Stars delta | +14 (30d) | +8.1k (30d) | | Open issues delta | 0 (30d) | +7 (30d) | | Owner type | Organization | User | | Full report | [trust report](/tools/determined-ai-determined/trust.md) | [trust report](/tools/rohitg00-ai-engineering-from-scratch/trust.md) | ## Shared compatibility - **Python**: [determined](/tools/determined-ai-determined.md) - Python runtime; [ai-engineering-from-scratch](/tools/rohitg00-ai-engineering-from-scratch.md) - Python runtime ## Decision facts: determined - **Pricing:** freemium - The open-source version under Apache License is free to use. Additional enterprise features may incur costs, though specific pricing details are not provided here. - **Requirements:** Installation involves using 'pip' for the CLI and subsequent cluster deployment steps via `det deploy`. - **Adopt for:** Determined is an open-source machine learning platform that simplifies distributed training and hyperparameter tuning for PyTorch and TensorFlow applications. - **License detail:** Apache-2.0 ## Decision facts: ai-engineering-from-scratch - **Adopt for:** ai-engineering-from-scratch is a comprehensive course that teaches AI engineering skills from foundational math to advanced AI agents and machine learning techniques, using Python and Node.js for interactive learning and ## Choose when ### Choose determined if… - determined is primarily Go; ai-engineering-from-scratch is Python. - License: determined is Apache-2.0, ai-engineering-from-scratch is MIT. - Pricing: The open-source version under Apache License is free to use. Additional enterprise features may incur costs, though specific pricing details are not provided here.. - Requirements: Installation involves using 'pip' for the CLI and subsequent cluster deployment steps via `det deploy`.. - Tags unique to determined: data-science, distributed training, hyperparameter-optimization, ml-infrastructure. - Also covers Evaluation & Observability. - You require a streamlined solution for distributed model training, particularly if you are working with PyTorch or TensorFlow frameworks. ### Choose ai-engineering-from-scratch if… - ai-engineering-from-scratch is primarily Python; determined is Go. - License: ai-engineering-from-scratch is MIT, determined is Apache-2.0. - Tags unique to ai-engineering-from-scratch: agents, ai-engineering, computer-vision, from-scratch. - Also covers AI Agents, Computer Vision, Developer Tools. - when you need a structured course that covers a wide range of AI engineering topics from scratch, including foundational math, deep learning, and reinforcement learning. ## When NOT to use determined - Seeking a platform that supports more machine learning frameworks beyond PyTorch and TensorFlow. - Your current stack does not include any of the supported infrastructures like Kubernetes or cloud services where Determined can be deployed. ## When NOT to use ai-engineering-from-scratch - if you are looking for a tool that focuses solely on theoretical knowledge without practical application, as this course emphasizes hands-on learning. - when you do not have access to Node.js or Python, as these are required for running the course and its interactive components. - if you prefer a more traditional learning approach without the use of terminal-based learning tools, as the course is designed for interactive terminal sessions. - when you are working with a development environment that does not support skill-capable hosts, as the course is optimized for such environments. ## Common questions ### What is the difference between determined and ai-engineering-from-scratch? determined: An open-source machine learning platform for distributed training and resource management.. ai-engineering-from-scratch: Learn, build, and deploy AI engineering skills from scratch.. See the comparison table for live GitHub stats and shared categories. ### When should I choose determined over ai-engineering-from-scratch? Choose determined over ai-engineering-from-scratch when determined is primarily Go; ai-engineering-from-scratch is Python; License: determined is Apache-2.0, ai-engineering-from-scratch is MIT; Pricing: The open-source version under Apache License is free to use. Additional enterprise features may incur costs, though specific pricing details are not provided here.; Requirements: Installation involves using 'pip' for the CLI and subsequent cluster deployment steps via `det deploy`.; Tags unique to determined: data-science, distributed training, hyperparameter-optimization, ml-infrastructure; Also covers Evaluation & Observability; You require a streamlined solution for distributed model training, particularly if you are working with PyTorch or TensorFlow frameworks. ### When should I choose ai-engineering-from-scratch over determined? Choose ai-engineering-from-scratch over determined when ai-engineering-from-scratch is primarily Python; determined is Go; License: ai-engineering-from-scratch is MIT, determined is Apache-2.0; Tags unique to ai-engineering-from-scratch: agents, ai-engineering, computer-vision, from-scratch; Also covers AI Agents, Computer Vision, Developer Tools; when you need a structured course that covers a wide range of AI engineering topics from scratch, including foundational math, deep learning, and reinforcement learning. ### When should I avoid determined? Seeking a platform that supports more machine learning frameworks beyond PyTorch and TensorFlow. Your current stack does not include any of the supported infrastructures like Kubernetes or cloud services where Determined can be deployed. ### When should I avoid ai-engineering-from-scratch? if you are looking for a tool that focuses solely on theoretical knowledge without practical application, as this course emphasizes hands-on learning. when you do not have access to Node.js or Python, as these are required for running the course and its interactive components. if you prefer a more traditional learning approach without the use of terminal-based learning tools, as the course is designed for interactive terminal sessions. when you are working with a development environment that does not support skill-capable hosts, as the course is optimized for such environments. ### Is determined or ai-engineering-from-scratch more popular on GitHub? ai-engineering-from-scratch has more GitHub stars (54,935 vs 3,241). Stars measure visibility, not whether either tool fits your constraints. ### Are determined and ai-engineering-from-scratch open source? Yes - both are open-source projects on GitHub (determined: Apache-2.0, ai-engineering-from-scratch: MIT). ### Where can I find alternatives to determined or ai-engineering-from-scratch? GraphCanon lists graph-backed alternatives at [determined alternatives](/tools/determined-ai-determined/alternatives) and [ai-engineering-from-scratch alternatives](/tools/rohitg00-ai-engineering-from-scratch/alternatives) ([determined markdown twin](/tools/determined-ai-determined/alternatives.md), [ai-engineering-from-scratch markdown twin](/tools/rohitg00-ai-engineering-from-scratch/alternatives.md)), ranked by typed relationship edges rather than popularity votes. ### Is there a machine-readable version of this comparison? Yes. The markdown twin at [this comparison](/compare/determined-ai-determined-vs-rohitg00-ai-engineering-from-scratch.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, determined or ai-engineering-from-scratch? determined: Dormant. ai-engineering-from-scratch: Active. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance. ### Where are the full trust reports for determined and ai-engineering-from-scratch? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [determined trust report](/tools/determined-ai-determined/trust); [ai-engineering-from-scratch trust report](/tools/rohitg00-ai-engineering-from-scratch/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=determined-ai-determined`](/api/graphcanon/graph?tool=determined-ai-determined) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_