---
title: "TinyML: Edge & Embedded"
---

## HarvardX Professional Certificate in Tiny Machine Learning

The complete courseware from the [HarvardX TinyML Professional Certificate on edX](https://www.edx.org/professional-certificate/harvardx-tiny-machine-learning) — 4 courses across 5 chapters covering ML fundamentals through embedded deployment and MLOps. 178 slide decks, 127 readings, and 23 supplementary materials. Originally developed by Harvard SEAS and Google TensorFlow.

```{=html}
<div style="margin-bottom: 1.5rem;">
  <a href="https://github.com/harvard-edge/cs249r_book/releases/download/slides-latest/MLSysBook-TinyML-All.zip" class="btn-accent" target="_blank">Download All (ZIP)</a>
  <a href="https://github.com/tinyMLx/courseware/tree/master/edX" class="btn-outline" target="_blank">View Source</a>
  <a href="https://www.edx.org/professional-certificate/harvardx-tiny-machine-learning" class="btn-outline" target="_blank">edX Course</a>
</div>

<!-- Course Cards -->
<div class="section-header">Courses</div>
<div class="vol-grid">
  <a href="tinyml-fundamentals.html" class="vol-card">
    <span class="tag">Course 1</span>
    <h3>Fundamentals of TinyML</h3>
    <p>ML basics, deep learning building blocks, CNNs, computer vision, and responsible AI design.</p>
    <div class="card-meta">10 sections · 53 resources · 16 Colabs · Chapters 1–2</div>
  </a>
  <a href="tinyml-applications.html" class="vol-card">
    <span class="tag">Course 2</span>
    <h3>Applications of TinyML</h3>
    <p>TensorFlow Lite, quantization, keyword spotting, visual wake words, anomaly detection, and data engineering.</p>
    <div class="card-meta">10 sections · 79 resources · 14 Colabs · Chapter 3</div>
  </a>
  <a href="tinyml-deploying.html" class="vol-card">
    <span class="tag">Course 3</span>
    <h3>Deploying TinyML</h3>
    <p>Embedded hardware, TFLite Micro internals, and hands-on deployment of KWS, VWW, and gesture recognition on Arduino.</p>
    <div class="card-meta">11 sections · 67 resources · 2 Colabs · Chapter 4</div>
  </a>
  <a href="tinyml-mlops.html" class="vol-card">
    <span class="tag">Course 4</span>
    <h3>MLOps for Scaling TinyML</h3>
    <p>ML development lifecycle, continuous training, model conversion, deployment at scale, prediction serving, and monitoring.</p>
    <div class="card-meta">12 sections · 126 resources · 2 Colabs · Chapter 5</div>
  </a>
</div>

<!-- Related Resources -->
<div class="section-header">Related Resources</div>
<table class="inv-table">
  <tr>
    <td>TinyML Syllabus</td>
    <td>10–12 week semester plan with weekly assignments, learning objectives, and adaptation guides.</td>
    <td><a href="https://mlsysbook.ai/instructors/tinyml-syllabus.html">Syllabus &rarr;</a></td>
  </tr>
  <tr>
    <td>Hardware Kits</td>
    <td>Arduino, Raspberry Pi, and Seeed deployment labs for hands-on TinyML projects.</td>
    <td><a href="https://mlsysbook.ai/kits/">Kits &rarr;</a></td>
  </tr>
  <tr>
    <td>Textbook</td>
    <td>Volume I covers the systems foundations: compression, hardware acceleration, benchmarking, and deployment.</td>
    <td><a href="https://mlsysbook.ai/vol1/">Vol I &rarr;</a></td>
  </tr>
  <tr>
    <td>edX Certificate</td>
    <td>Self-paced online version of this curriculum on the HarvardX edX platform.</td>
    <td><a href="https://www.edx.org/professional-certificate/harvardx-tiny-machine-learning" target="_blank">edX &rarr;</a></td>
  </tr>
</table>

<!-- ================================================================ -->
<!-- TEAM -->
<!-- ================================================================ -->
<div class="team-box">
  <h3>The TinyMLx Team</h3>
  <p><strong>Instructors:</strong> Vijay Janapa Reddi, Laurence Moroney, Pete Warden, Lara Suzuki</p>
  <p><strong>Guest Instructor:</strong> Susan Kennedy</p>
  <p><strong>Staff Lead:</strong> Brian Plancher</p>
  <p><strong>Staff:</strong> Colby Banbury, Benjamin Brown, Dhilan Ramaprasad, J. Evan Smith, Matthew Stewart</p>
  <p><strong>Contributors:</strong> Sharad Chitlangia, Radhika Ghosal, Srivatsan Krishnan, Maximilian Lam, Mark Mazumder</p>
</div>
```

::: {.callout-note}
These materials were originally developed for the [HarvardX Professional Certificate in Tiny Machine Learning](https://www.edx.org/professional-certificate/harvardx-tiny-machine-learning) on edX. See the [original curriculum](tinyml/README-edx-original.md) for the full item-by-item breakdown including forum prompts and quizzes not listed above.
:::