<p>⚠️ = extra important resources for beginners.</p>
<ul>
<li>My own AI/ML resources: <a href="https://www.tomsoderlund.com/ai/ai-resources">https://www.tomsoderlund.com/ai/ai-resources</a></li>
<li>Tweet: <a href="https://twitter.com/tomsoderlund/status/1583894481408905216"><em>“Ok, I feel I need to learn AI/ML now.”</em></a></li>
</ul>
<p>Thank you to <a href="https://twitter.com/spimescape">@spimescape</a> for mentoring me!</p>
<h2 id="introduction">Introduction</h2>
<p>I’m bullish about Artificial Intelligence (AI).</p>
<p>I’ve lived through three computing revolutions: the home PC, internet, and smartphones. I firmly believe that AI/ML is a 4th revolution.</p>
<blockquote>
<p>I firmly believe that AI/ML is a 4th computing revolution</p>
</blockquote>
<p>AI/ML is a new computing paradigm shift. Ignoring AI in 2022 is like ignoring the iPhone in 2010.</p>
<p>It requires a new way of thinking and approaching problems. My background as an entrepreneur and a software developer is not completely helpful; some things need to be unlearned and relearned.</p>
<h3 id="ai-is-not-magic">AI is not magic</h3>
<p>The more I learn about artificial intelligence, the less magical it seems. The Singularity is far way, artificial general intelligence (AGI) is nowhere near.</p>
<p>Instead, machine learning is all about math, identifying/generating patterns.</p>
<blockquote>
<p>The more I learn about artificial intelligence, the less magical it seems</p>
</blockquote>
<p>But at the same time <em>it is</em> magical. Example: making a blurry image sharp is <em>creating information</em> out of <em>nothing</em>. Tasks that previously required impossible number of man hours are now very feasible. <strong>AI will change how we work.</strong></p>
<h3 id="ai-or-ml">“AI” or “ML”?</h3>
<p>What’s the difference between Artificial Intelligence (AI) vs. Machine Learning (ML)? Today AI and ML refers to similar things, but AI is more used by media and outsiders, whereas ML is the more correct term used by people who research and build the technology.</p>
<p>More formally:</p>
<ul>
<li><strong>Artificial Intelligence</strong> refers to the general ability of computers to emulate human thought and perform tasks in real-world environments.</li>
<li><strong>Machine Learning</strong> refers to the technologies that enable systems to identify patterns, make decisions, and improve themselves through experience.</li>
</ul>
<h3 id="use-cases-for-aiml">Use cases for AI/ML</h3>
<p>Broadly speaking, we today use Machine Learning (ML) for:</p>
<ol>
<li>Classification &amp; Prediction (e.g. spam detection, find objects/people in images)</li>
<li>Generation &amp; Conversion (e.g. text, images, video, audio, speech)</li>
<li>Search (more advanced search vs pure text search)</li>
<li>Recommendations (e.g. music, film, products, social feeds)</li>
</ol>
<p><img src="/blog/images/ai_use_cases.jpg" alt="Use cases for AI/ML"></p>
<h2 id="learning-resources">Learning resources</h2>
<p>⚠️ Two great free courses for ML beginners: <a href="https://developers.google.com/machine-learning/crash-course/ml-intro">Google’s (theoretical)</a> and <a href="https://course.fast.ai/">FastAI’s (practical)</a>.</p>
<ul>
<li><a href="https://developers.google.com/machine-learning">Google ML Foundational Courses</a><ul>
<li>⚠️ <a href="https://developers.google.com/machine-learning/crash-course/ml-intro">ML Crash Course with TensorFlow APIs</a></li>
<li><a href="https://developers.google.com/machine-learning/problem-framing/problem">Problem Framing</a></li>
<li><a href="https://playground.tensorflow.org/">TensorFlow Playground</a></li>
</ul>
</li>
<li>⚠️ <a href="https://course.fast.ai/">FastAI: “Practical Deep Learning for Coders”</a><ul>
<li><a href="https://www.youtube.com/playlist?list=PLfYUBJiXbdtSvpQjSnJJ_PmDQB_VyT5iU">YouTube video lessons</a></li>
<li>Book: “Deep Learning for Coders with FastAI and PyTorch: AI Applications Without a PhD”: <a href="https://github.com/fastai/fastbook">free on GitHub</a> or <a href="https://www.amazon.com/Deep-Learning-Coders-fastai-PyTorch/dp/1492045527">on Amazon</a></li>
</ul>
</li>
<li><a href="https://www.kaggle.com/learn/intro-to-machine-learning">Kaggle course “Intro to Machine Learning”</a></li>
<li>O’Reilly:<ul>
<li>Courses: <a href="https://learning.oreilly.com/courses/dataml-engineer/">Data/ML engineer</a></li>
<li>Book: <a href="https://learning.oreilly.com/library/view/python-machine-learning/9781789955750/Text/Chapter_1.xhtml#_idParaDest-23">“Python Machine Learning”</a></li>
<li><a href="https://learning.oreilly.com/playlists/907fa947-4eba-4c1c-bcbd-9cc5167568d7/">Playlist</a></li>
</ul>
</li>
<li><a href="https://code.visualstudio.com/docs/datascience/data-science-tutorial">VS Code: “Data Science in VS Code tutorial”</a></li>
<li><a href="https://www.w3schools.com/python/python_ml_getting_started.asp">W3schools Python course</a></li>
<li><a href="https://twitter.com/1littlecoder/status/1580555066791972866">“Build your AI Product MVP in less than 24 hours”</a></li>
</ul>
<h2 id="people">People</h2>
<ul>
<li><a href="https://twitter.com/i/lists/1577855041150541826">Tom’s Twitter list “AI &amp; ML”</a></li>
</ul>
<h2 id="tools">Tools</h2>
<ul>
<li><a href="#programming-language">Programming language</a></li>
<li><a href="#%E2%9A%A0%EF%B8%8F-notebook-software">⚠️ Notebook software</a></li>
<li><a href="#%E2%9A%A0%EF%B8%8F-ml-code-examples">⚠️ ML code examples</a></li>
<li><a href="#frameworks--libraries">Frameworks &amp; libraries</a></li>
<li><a href="#models">Models</a></li>
<li><a href="#datasets">Datasets</a></li>
</ul>
<h3 id="programming-language">Programming language</h3>
<p><a href="https://github.com/tomsoderlund/toms-guidebooks/blob/master/Development/Python.md">Python</a> is the main programming language for AI/ML.</p>
<h3 id="⚠️-notebook-software">⚠️ Notebook software</h3>
<p>For writing/documenting/sharing ML code – essentially word processors with embedded (Python) code blocks. Uses <code>.ipynb</code> files, which are JSON structures containing Python code blocks and Markdown text blocks:</p>
<ul>
<li><a href="https://jupyter.org/">Jupyter Notebook</a>: the original (see below)</li>
<li><a href="https://code.visualstudio.com/docs/datascience/jupyter-notebooks">VS Code</a> has built in Jupyter support</li>
<li><a href="https://colab.research.google.com/">Google Colab</a>: work with files on Google Drive and GitHub</li>
<li><a href="https://www.kaggle.com/">Kaggle</a></li>
<li><a href="https://gradio.app/">Gradio</a> for building UI’s inside notebooks</li>
<li>IPython: interactive Python and Jupyter.</li>
<li>REPL: interactive Python session. Read, Evaluate, Print and Loop.</li>
</ul>
<h4 id="jupyter-notebook">Jupyter Notebook</h4>
<ul>
<li>Press Tab to get autocompletion suggestions</li>
<li>Inside the parentheses of a function, pressing Shift + Tab will display signature and description of the function</li>
<li><code>?[function]</code> to get help on function: <code>?verify_images</code>, or <code>??verify_images</code> for full source code</li>
<li><code>doc([function])</code> to get documentation page</li>
<li><code>%debug</code>: inspect every variable</li>
</ul>
<h3 id="⚠️-ml-code-examples">⚠️ ML code examples</h3>
<ul>
<li><a href="https://huggingface.co/spaces">HuggingFace Spaces</a>: discover ML apps</li>
<li><a href="https://www.kaggle.com/code">Kaggle Code</a>: explore, run, share ML code</li>
</ul>
<h3 id="frameworks--libraries">Frameworks &amp; libraries</h3>
<ul>
<li><a href="https://pytorch.org/">PyTorch</a> – open source machine learning framework</li>
<li><a href="https://www.tensorflow.org/">TensorFlow</a> – Google<ul>
<li><a href="https://keras.io/">Keras</a> for deep learning</li>
</ul>
</li>
<li><a href="https://jax.readthedocs.io/en/latest/">JAX</a></li>
<li><a href="https://docs.fast.ai/">FastAI</a></li>
</ul>
<h4 id="pytorch">PyTorch</h4>
<p>Libraries:</p>
<ul>
<li><strong>torch</strong>: main library<ul>
<li><a href="https://pytorch.org/tutorials/beginner/basics/intro.html">Introductory neural network tutorial</a></li>
</ul>
</li>
<li><strong>torchaudio</strong>: sound</li>
<li><strong>torchtext</strong>: text/NLP</li>
<li><strong>torchvision</strong>: images and video</li>
<li><strong>torcharrow</strong>: data preprocessing in deep learning</li>
<li><strong>TorchData</strong>: data loading primitives for easily constructing flexible and performant data pipelines</li>
<li><strong>TorchRec</strong>: recommendations</li>
<li><strong>TorchServe</strong>: server</li>
<li><strong>TorchX</strong>: job launcher for PyTorch applications</li>
</ul>
<h3 id="models">Models</h3>
<p>Collections of models:</p>
<ul>
<li><a href="https://huggingface.co/models">HuggingFace</a>: collection of models, “GitHub for AI models”</li>
<li><a href="https://www.eleuther.ai/">EleutherAI</a>: creator of open source AI models</li>
<li><a href="https://openai.com/">OpenAI</a>: creator of private/closed source – GPT-3, DALL·E</li>
</ul>
<p>Some models:</p>
<ul>
<li>Text-to-Text:<ul>
<li>GPT-3: OpenAI</li>
<li>GPT-Neo 1.3B: a transformer model designed using EleutherAI’s replication of the GPT-3 architecture.</li>
<li>GPT-J 6B: a transformer model trained using Ben Wang&#39;s Mesh Transformer JAX.</li>
</ul>
</li>
<li>Text-to-Image:<ul>
<li>DALL·E: OpenAI</li>
<li>Midjourney</li>
<li>Stable Diffusion: Stability AI</li>
<li>Imagen: Google</li>
<li>Parti: Google</li>
</ul>
</li>
<li>Text-to-Video:<ul>
<li>Imagen Video: Google</li>
<li>Phenaki: Google</li>
<li>Make-a-Video: Meta</li>
</ul>
</li>
</ul>
<h3 id="datasets">Datasets</h3>
<ul>
<li><a href="https://www.kaggle.com/datasets">Kaggle Datasets</a></li>
<li><a href="https://toolbox.google.com/datasetsearch">Google Dataset Search</a></li>
<li><a href="https://scholar.google.com/">Google Scholar</a></li>
<li><a href="http://mlr.cs.umass.edu/ml/">UCI Machine Learning Repository</a></li>
<li><a href="https://www.visualdata.io/">VisualData</a></li>
<li><a href="https://guides.library.cmu.edu/machine-learning/datasets">CMU Libraries</a></li>
<li><a href="https://datasets.quantumstat.com/">The Big Bad NLP Database</a></li>
<li>Lists of datasets:<ul>
<li><a href="https://pub.towardsai.net/best-datasets-for-machine-learning-data-science-computer-vision-nlp-ai-c9541058cf4f">TowardsAI</a></li>
<li><a href="http://yulanguo.me/dataset.html">Image/video/3D datasets (Yulan Guo)</a></li>
</ul>
</li>
</ul>
<h2 id="machine-learning-theory">Machine Learning Theory</h2>
<p>Machine learning algorithm:</p>
<pre><code>Inputs + Weights → Model → Results + Loss
…then adjust Weights
</code></pre>
<p>⚠️ ML input data is <strong>always in number format</strong> (with decimals), even though it came from text/image/video/sound. See “Feature engineering” below.</p>
<h3 id="terminology">Terminology</h3>
<ul>
<li><strong>Model</strong>: what does the actual predicting; a file that has been trained to recognize certain types of patterns.</li>
<li><strong>Features</strong>: model inputs, <code>x₁</code>, <code>x₂</code>, etc.</li>
<li><strong>Label</strong>: model output, <code>y</code>, what we want to predict.</li>
<li><strong>Example</strong>: one piece of data (features, labeled or unlabeled).</li>
<li><strong>Dataset</strong>: a whole collection of examples.</li>
<li><strong>Batch</strong>: the set of examples used in one training iteration.</li>
<li><strong>Epoch</strong>: number of passes of the entire training dataset the machine learning algorithm has completed.</li>
<li><strong>Vector</strong>: an array of numbers.</li>
<li><strong>Matrix</strong>: a 2D array of numbers, rows × columns, e.g. 150×4.</li>
<li><strong>Tensor</strong>: a specialized data structure that are very similar to arrays and matrices. A tensor interacts with other entities in a system and changes its values when other values change.</li>
<li><strong>Slope</strong>: <code>m</code>, angle of the line in linear regression.</li>
<li><strong>Weight</strong>: <code>w</code>, the importance of a feature</li>
<li><strong>Bias</strong>: <code>b</code>, how well a model matches the training set.</li>
<li><strong>Loss</strong>: mismatch with model, <em>mean square error</em>.</li>
<li><strong>Learning rate</strong>: granularity when training a model</li>
<li><strong>Gradient descent</strong>: algorithm for finding minima.</li>
<li><strong>Hyperparameters</strong>: the “knobs” that programmers tweak in machine learning algorithms.</li>
<li><strong>Deep learning</strong>: artificial neural networks with representation learning.</li>
<li><strong>Data augmentation</strong>: creating random variations of our input data so they appear different, but do not actually change the meaning of the data. E.g. image rotation and flipping.</li>
<li><strong>Generalization</strong>: a model’s ability to adapt properly to new, previously unseen data.</li>
<li><strong>Feature engineering</strong>: transform inputs so they can be used in model.</li>
<li><strong>One-hot encoding</strong>: as 1 or 0.</li>
<li><strong>Binning</strong>: put category values into “buckets”.</li>
<li><strong>Feature crosses</strong>: combine features to create synthetic features.</li>
<li><strong>Decision boundaries</strong>: a surface that separates data points belonging to different class labels.</li>
<li><strong>Regularization</strong>: reducing complexity of model (e.g. L2).</li>
<li><strong>Logistic regression</strong>: output is a <em>probability</em> (0.0-1.0).</li>
</ul>
<h3 id="learning-types">Learning types</h3>
<ul>
<li><strong>Supervised learning</strong>: labeled data/output</li>
<li><strong>Reinforcement learning</strong>: reward signal, e.g. chess engine</li>
<li><strong>Unsupervised learning</strong>: hidden structures – clustering, dimensionality reduction</li>
</ul>
<p>Also: feature learning/representation learning</p>
<h3 id="neural-networks">Neural networks</h3>
<p>Neural networks definitely shine where you have data that is not easily transformable into features – for example images used to be hard to transform into good features.</p>
<p>NN math, simplified: multiply, add them up, replace negatives with zeroes.</p>
<ul>
<li><strong>CNN: Convolutional Neural Network</strong>, a deep learning neural network sketched for processing structured arrays of data such as portrayals.</li>
<li><strong>RNN: Recurrent Neural Network</strong>, a class of artificial neural networks where connections between nodes can create a cycle, allowing output from some nodes to affect subsequent input to the same nodes.</li>
<li><strong>Transformer</strong>: is a deep learning model that adopts the mechanism of self-attention, differentially weighting the significance of each part of the input data. It is used primarily in the fields of natural language processing (NLP) and computer vision (CV).</li>
</ul>
<h3 id="text-processing-with-large-language-models-llms">Text processing with Large Language Models (LLM’s)</h3>
<ul>
<li><strong>LLM</strong>: Large Language Model</li>
<li><strong>NER</strong>: Named Entity Recognition</li>
</ul>
<h3 id="vector-search-and-semantic-search">Vector search and Semantic search</h3>
<ul>
<li><strong>Vector search</strong>: matching search key’s vectors’ proximity in vector space with other items.</li>
<li><strong>Semantic search</strong>: NLP for transforming search phrase into vectors, then vector search for matching their proximity in vector space with other items. These vectors are usually called <strong>embeddings</strong>.</li>
<li><strong>Embeddings</strong>: special type of vector that is produced as a result of running an input through an ML model - usually a neural network.</li>
<li><strong>Dense retrieval</strong>: using embeddings to search.</li>
</ul>
<h2 id="deployment">Deployment</h2>
<p>There are two main use cases for deploying ML code: <strong>training</strong> and <strong>inference</strong>.</p>
<p>For inference, there are several options:</p>
<ol>
<li>Use your own cloud compute/GPU and have your backend use that.</li>
<li>AWS SageMaker Inference service and ping that from your backend.</li>
<li>GCP’s ML Inference service (Vertex AI) and ping that from your backend.</li>
<li>HuggingFace Inference Endpoints: <a href="https://huggingface.co/inference-endpoints">https://huggingface.co/inference-endpoints</a></li>
</ol>


What problems Artificial Intelligence (AI) can solve today. What tools to use. How to integrate machine learning (ML) in your websites and apps.

Beginners introduction to Artificial Intelligence (AI) and Machine Learning (ML)

<p>My own resources for Artificial Intelligence (AI) and Machine Learning (ML):</p>
<ul>
<li><a href="/ai/ai-introduction">Beginners introduction to AI/ML with notes &amp; links</a></li>
<li>“AI for Mere Mortals” course (coming): <a href="https://tomorroworld.podia.com/ai-for-mere-mortals">https://tomorroworld.podia.com/ai-for-mere-mortals</a></li>
<li>Code:<ul>
<li>REST API with Gradio and Huggingface Spaces: <a href="https://www.tomsoderlund.com/ai/building-ai-powered-rest-api">blog post</a>, <a href="https://huggingface.co/spaces/tomsoderlund/swedish-entity-recognition">code</a></li>
<li>ML model with a REST API (local only): <a href="https://github.com/tomsoderlund/ml-on-rest-api">https://github.com/tomsoderlund/ml-on-rest-api</a></li>
<li>Some ML code snippets: <a href="https://github.com/tomsoderlund/toms-guidebooks/blob/master/Development/AI%20and%20Machine%20Learning.md">https://github.com/tomsoderlund/toms-guidebooks/blob/master/Development/AI%20and%20Machine%20Learning.md</a></li>
</ul>
</li>
</ul>


My own resources for Artificial Intelligence (AI) and Machine Learning (ML):
	Beginners introduction to AI/ML with notes

My AI/ML resources

<p>I want to use machine learning models in my web and mobile apps, but to do so I must host my ML app somewhere.</p>
<p>Hosting a pre-trained ML model is called <em>inference</em>. I just want to drop in some Python ML code and quickly get a REST API, but finding a simple way to do this has proven more difficult than I expected.</p>
<blockquote>
<p>“I just want to drop in some Python ML code and get a REST API”</p>
</blockquote>
<p>There are plenty of hosting providers, including the big ones like Amazon AWS and Google GCP, but the process of using these are complex and often requires building your own <a href="https://flask.palletsprojects.com/">Flask</a> REST API.</p>
<p>Luckily, <a href="https://twitter.com/prundin/status/1597595173562859523?s=20&amp;t=L72CeSiIQCVcgom0_cMAJA">@prundin and @abidlabs had a solution</a>: Gradio and Huggingface Spaces.</p>
<h2 id="create-a-new-space-on-huggingface">Create a new Space on Huggingface</h2>
<p><a href="https://huggingface.co/">Huggingface</a> is like GitHub but for ML models and apps. A “Space” on Huggingface is an ML app that you can update via Git. Spaces are <a href="https://huggingface.co/pricing#spaces">priced based on CPU type</a>, and the simplest one is free!</p>
<p><a href="https://gradio.app/">Gradio</a> is a library of user interface components (e.g. input fields) for ML apps.</p>
<p>Create a new Space by:</p>
<ul>
<li>Go to <a href="https://huggingface.co/spaces">https://huggingface.co/spaces</a> and click <strong>Create new Space</strong>.</li>
<li>Select the <a href="https://huggingface.co/blog/open_rail">OpenRAIL license</a> if you’re unsure.</li>
<li>Select <strong>Gradio</strong> as Space SDK.</li>
</ul>
<p><img src="/blog/images/huggingface-new-space.png" alt="Create a new Huggingface Space"></p>
<h2 id="local-development-setup">Local development setup</h2>
<p>Go to your development folder and clone your space:</p>
<pre><code>git clone https://huggingface.co/spaces/USER_NAME/SPACE_NAME

cd SPACE_NAME
</code></pre>
<p>Set up Python, Gradio, etc:</p>
<pre><code># Create and activate a “safe” virtual Python environment (exit with command “deactivate”)
python3 -m venv env
source env/bin/activate

# Create a .gitignore file to exclude the packages in `env` folder
echo &quot;env/&quot; &gt;&gt; .gitignore

# Install Gradio
pip3 install gradio

# Update the list of required packages (do this every time you add packages)
pip3 freeze &gt; requirements.txt
</code></pre>
<h2 id="settings-in-the-readme-file">Settings in the README file</h2>
<p>The <code>README.md</code> contains some crucial settings for your app.</p>
<p>I struggled with some errors in my live environment on Huggingface, until I realized I had to lock down the Python version to be the same as I use locally. By running:</p>
<pre><code>python3 --version
</code></pre>
<p>locally, and then adding the version number to <code>README.md</code> like this:</p>
<pre><code>python_version: 3.9.13
</code></pre>
<h2 id="creating-a-simple-user-interface-and-rest-api">Creating a simple user interface and REST API</h2>
<p>Create a blank app.py file:</p>
<pre><code>touch app.py
</code></pre>
<p>Edit <code>app.py</code>:</p>
<pre><code>import gradio

def my_inference_function(name):
  return &quot;Hello &quot; + name + &quot;!&quot;

gradio_interface = gradio.Interface(
  fn = my_inference_function,
  inputs = &quot;text&quot;,
  outputs = &quot;text&quot;
)
gradio_interface.launch()
</code></pre>
<p>Upload the files to Huggingface using Git:</p>
<pre><code>git add .
git commit -m &quot;Creating app.py&quot;
git push
</code></pre>
<h2 id="testing-your-app-on-huggingface">Testing your app on Huggingface</h2>
<p>Believe it or not, but you now have a functional app with a REST API!</p>
<p>Open <a href="https://huggingface.co/spaces/USER_NAME/SPACE_NAME">https://huggingface.co/spaces/USER_NAME/SPACE_NAME</a> in your browser.</p>
<p>See an example here: <a href="https://huggingface.co/spaces/tomsoderlund/rest-api-with-gradio">https://huggingface.co/spaces/tomsoderlund/rest-api-with-gradio</a></p>
<p><img src="/blog/images/huggingface-space-running.png" alt="Huggingface space when running"></p>
<p><strong>User interface:</strong> You can enter something in the <code>name</code> box and press <strong>Submit</strong>.</p>
<p><strong>REST API interface:</strong> At the bottom of the page you have a link called “Use via API”. Click it for instructions, but you can now call your app with REST:</p>
<pre><code>curl -X POST -H &#39;Content-type: application/json&#39; --data &#39;{ &quot;data&quot;: [&quot;Jill&quot;] }&#39; https://USER_NAME-SPACE_NAME.hf.space/run/predict
</code></pre>
<p>The REST API would return:</p>
<pre><code>{
  &quot;data&quot;:[
    &quot;Hello Jill!&quot;
  ],
  &quot;is_generating&quot;:false,
  &quot;duration&quot;:0.00015354156494140625,
  &quot;average_duration&quot;:0.00015354156494140625
}
</code></pre>
<h2 id="testing-your-app-locally">Testing your app locally</h2>
<p>Locally, you can run:</p>
<pre><code>python3 app.py
</code></pre>
<p>You can now test your app interactively on: <a href="http://127.0.0.1:7860/">http://127.0.0.1:7860/</a>
and access the REST API on <a href="http://127.0.0.1:7860/run/predict">http://127.0.0.1:7860/run/predict</a></p>
<p>(You need to stop (Ctrl+C) and restart your app when you modify <code>app.py</code>)</p>
<h2 id="going-further">Going further</h2>
<p>You can now <a href="https://huggingface.co/models">explore all the models on Huggingface</a>, including <a href="https://huggingface.co/stabilityai/stable-diffusion-2">Stable Diffusion 2</a> and <a href="https://huggingface.co/EleutherAI/gpt-neo-2.7B">GPT-Neo</a>, and add them to your Spaces app.</p>
<p>See a complete ML example on: <a href="https://huggingface.co/spaces/tomsoderlund/swedish-entity-recognition">https://huggingface.co/spaces/tomsoderlund/swedish-entity-recognition</a> – you can see how few lines of code are actually needed to invoke the ML model.</p>
<p><img src="/blog/images/huggingface-rest-api-running.png" alt="A complete ML example with REST API"></p>


I just want to drop in some Python ML code and get a REST API

Building an AI powered REST API with Gradio and Huggingface Spaces – for free!

<p><img src="/blog/images/AI_for_Mere_Mortals.jpg" alt="AI for Mere Mortals"></p>
<p>I’m bullish about Artificial Intelligence (AI).</p>
<p>I firmly believe that it is a new computing paradigm shift, on the same magnitude as smartphones and the internet. Ignoring AI in 2022 is like ignoring the iPhone in 2010.</p>
<p>It requires a new way of thinking and approaching problems. My background as an entrepreneur and a software developer is not completely helpful; some things need to be unlearned and relearned.</p>
<p>Wanna learn with me?</p>
<p>I’m collecting my learnings into the curriculum I was missing when I got started learning AI/ML:</p>
<p><a href="https://tomorroworld.podia.com/ai-for-mere-mortals">https://tomorroworld.podia.com/ai-for-mere-mortals</a></p>


AI for Mere Mortals

I’m bullish about Artificial Intelligence (AI).

I firmly believe that it is a new computing paradigm shift, on the same magnitude as smartphones and the internet. Ignoring AI in

AI for Mere Mortals

<p><img src="/blog/images/06070-1btnyhbyjmu6v650a3ngcoq.png" alt=""></p>
<p>It might sound like science-fiction, but it makes sense. AI’s are becoming increasingly powerful, but creativity is not their strongest suite. But they’re great at performing tasks such as organizing, counting KPI’s, and doing administrative tasks. Tasks which creative entrepreneurs are less adept for.</p>
<p>Paired with a creative entrepreneur, they could form a strong team. Man and machine, working as one.</p>
<p>Additionally, AI is red hot among VC’s, and I bet some of them would be excited to fund the first hybrid human/AI founder teams.</p>


It might sound like science-fiction, but it makes sense. AI’s are becoming increasingly powerful, but creativity is not their strongest suite. But they’re great at performing tasks such as organizi

When will we see AI’s as part of startup founder teams?

Read Tom Söderlund’s articles about artificial intelligence & machine learning.

Artificial Intelligence & Machine Learning

Beginners introduction to Artificial Intelligence (AI) and Machine Learning (ML)

My AI/ML resources

Building an AI powered REST API with Gradio and Huggingface Spaces – for free!

AI for Mere Mortals

When will we see AI’s as part of startup founder teams?