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<title>Data Science with Z by HP AI Studio</title>
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<h1 id="data-science-with-z-by-hp-ai-studio">Data Science with Z by HP AI Studio</h1>
<p>This is a proposal for an initial structure of public repositories for educational material and demos. The main idea here is to make available a set of 15+ notebooks with end-to-end experiments split into subjects according to different topics. This way, we would have smaller repos (with no more than 5 experiments) - avoiding the current scenario of having to download a single big repo to run any experiment, but also without having too many different repos to give maintenance.</p>
<ol>
<li><a href="#first-course">Using AI Studio in 6 Steps</a>
<ol>
<li><a href="#section1-1">Projects, Workspaces and Github</a></li>
<li><a href="#section1-2">Datafabric</a></li>
<li><a href="#section1-3">Data visualization and monitoring</a></li>
<li><a href="#section1-4">Libraries and custom environments</a></li>
<li><a href="#section1-5">Deploying models locally</a></li>
<li><a href="#section1-6">Introducing CV and NLP</a></li>
</ol>
</li>
<li><a href="#deep-learning-with-AI-Studio">Deep Learning in AI Studio</a>
<ol>
<li><a href="#section2-1">Image Classification</a></li>
<li><a href="#section2-2">Image Transformation: Super resolution</a></li>
<li><a href="#section2-3">Generating text by characters</a></li>
<li><a href="#section2-4">Introducing transformers for answering questions</a></li>
</ol>
</li>
<li><a href="#using-NGC-resources">Integrating with NGC</a>
<ol>
<li><a href="#section3-1">Using RAPIDS to accelerate data processing</a></li>
<li><a href="#section3-2">Extending RAPIDS with data visualization</a></li>
<li><a href="#section3-3">NeMo for Audio and Text translation</a></li>
</ol>
</li>
<li><a href="#using-prometheus">Gen AI with Galileo and AIS</a>
<ol>
<li><a href="#section4-1">Galileo Evaluate on RAG-based chatbot</a></li>
<li><a href="#section4-2">Improving chatbot quality with Galileo Observe and Protect </a></li>
<li><a href="#section4-3">Summarizing text</a></li>
<li><a href="#section4-4">Code Generation</a></li>
<li><a href="#section4-5">Text Generation</a></li>
</ol>
</li>
</ol>
<p>Below, we find a description of each specific subject/repository, as well as the intended demos/tutorials to be included on each one</p>
<p><a id=first-course> </a></p>
<h2 id="1-using-ai-studio-features-in-6-steps">1. Using AI Studio features in 6 steps</h2>
<ul>
<li>Currently saved on ai-studio fundamentals folder</li>
</ul>
<p>This repo would have a different structure than the other ones. Five different notebooks would be used to illustrate different foundational features of AI Studio, in separate tutorials. These notebooks are:</p>
<ul>
<li>Iris classification: One of the most traditional examples in ML, this notebook will be used to illustrate the most simple usage of AI Studio (section 1)</li>
<li>Movie experiment: This notebook is an example of a recommendation system, which can be used to show features as Data Fabric, ML Flow and Tensorboard monitoring and model deployment.</li>
<li>Tale of two cities: A nice example for different data visualization techniques, can also be used to demonstrate data fabric and installation of libraries/customization of environments</li>
<li>MNIST classification: End-to-end introdutory example of Computer Vision with AI Studio</li>
<li>Spam Classification: End-to-end introdutory example of Natural Language Processing with AI Studio</li>
</ul>
<p><a id=section1-1> </a></p>
<h3 id="11-working-with-projects-workspaces-and-github">1.1 Working with projects, workspaces and Github</h3>
<h4 id="notebooks-on-this-session">Notebooks on this session</h4>
<ul>
<li>classification/iris
<ul>
<li>Needs to change the load_data, to use sklearn one</li>
</ul>
</li>
</ul>
<h4 id="content">Content</h4>
<ul>
<li>What is a project on AI Studio, and how does it work?</li>
<li>How to create a simple project?</li>
<li>How to add a simple Workspace inside a project (Minimal vs Data Science workspace)</li>
<li>How to connect to a Github Repository</li>
<li>How to access your notebook inside the workspace</li>
<li>What are the local folders?</li>
</ul>
<p><a id=section1-2> </a></p>
<h3 id="12-using-datafabric">1.2 Using datafabric</h3>
<h4 id="notebooks-on-this-session-1">Notebooks on this session</h4>
<ul>
<li>Introduce Movie experiment example</li>
<li>Introduce tale of two cities project</li>
</ul>
<h4 id="content-1">Content</h4>
<ul>
<li>How to add local folders to my project</li>
<li>How to access these local folders from inside the workspace</li>
<li>How to add cloud folders to my project</li>
<li>Why should you restart your workspace to access data fabric</li>
</ul>
<p><a id=section1-3> </a></p>
<h3 id="13-data-visualization-and-experiments-monitoring">1.3 Data visualization and experiments monitoring</h3>
<h4 id="notebooks-on-this-session-2">Notebooks on this session</h4>
<ul>
<li>Show data visualization in previous examples</li>
<li>Use movie experiment example to show monitoring
<ul>
<li>Can we change TB logging to use tensorboard library instead of TF</li>
</ul>
</li>
</ul>
<h4 id="content-2">Content</h4>
<ul>
<li>Data visualization tools included</li>
<li>Using MLFlow to monitoring</li>
<li>Using Tensorboard to monitoring</li>
</ul>
<p><a id=section1-4> </a></p>
<h3 id="14-installing-libraries-and-configuring-environments">1.4 Installing libraries and configuring environments</h3>
<h4 id="notebooks-on-this-session-3">Notebooks on this session</h4>
<ul>
<li>Use the same notebooks in previous sessions
<ul>
<li>Try to run them on minimal workspace, to show how to show the effects on environment</li>
</ul>
</li>
</ul>
<h4 id="content-3">Content</h4>
<ul>
<li>Installing libraries with PIP</li>
<li>Custom workspaces/environments</li>
<li>Using conda environments manually</li>
</ul>
<p><a id=section1-5> </a></p>
<h3 id="15-deploying-models-locally">1.5 Deploying models locally</h3>
<h4 id="notebooks-on-this-session-4">Notebooks on this session</h4>
<ul>
<li>Use movie experiment example to show Model Service (make sure it works)
<ul>
<li>Create a quick UI later</li>
</ul>
</li>
</ul>
<h4 id="content-4">Content</h4>
<ul>
<li>Logging and registering models in MLFlow</li>
<li>Deploying a service (swagger interface)</li>
<li>Adding a UI to the service</li>
</ul>
<p><a id=section1-6> </a></p>
<h3 id="16-introducing-text-and-image-processing">1.6 Introducing text and image processing</h3>
<h4 id="notebooks-on-this-session-5">Notebooks on this session</h4>
<ul>
<li>MNIST (change Keras to scikit learn, so we do not use Tensorflow)</li>
<li>SpamClassification</li>
</ul>
<h4 id="content-5">Content</h4>
<ul>
<li>Use MNIST to show how to work with images</li>
<li>Use Spam classification to show how to work with text</li>
</ul>
<h3 id="extra-material">Extra Material</h3>
<h4 id="notebooks-on-this-session-6">Notebooks on this session</h4>
<ul>
<li>Select in the future</li>
</ul>
<h4 id="content-6">Content</h4>
<ul>
<li>Briefly explain the extra notebooks</li>
</ul>
<hr>
<p><a id=deep-learning-with-AI-Studio></a></p>
<h2 id="2-deep-learning-with-z-by-hp-ai-studio">2. Deep Learning with Z by HP AI Studio</h2>
<ul>
<li>Folder: deep-learning-in-ais</li>
</ul>
<p>Starting in this second subject, each individual demo/tutorial is associated with a single notebook (and auxiliary files). In this section we will have 4 examples on how to use Tensorflow and Pytorch inside AI Studio, using GPU resources and our Deep Learning workspaces to easily put in practice to process images and language.</p>
<p><a id=section2-1> </a></p>
<h3 id="21-classifying-images-with-tensorflowpytorch">2.1 Classifying images with TensorFlow/PyTorch</h3>
<h4 id="notebooks-on-this-session-7">Notebooks on this session</h4>
<ul>
<li>Basic Image Classification notebook</li>
</ul>
<h4 id="content-7">Content</h4>
<ul>
<li>Use Deep Learning image to work with a Image Classification example</li>
<li>Use Data from datafabric</li>
<li>Ensure that MLFlow/Tensorboard are being used in the code</li>
<li>Ensure that multiple runs are made, with different configurations, to allow comparison</li>
<li>Ensure that GPU is being used</li>
</ul>
<p><a id=section2-2> </a></p>
<h3 id="22-image-transformation-with-tensorflowpytorch-a-different-one-from-the-previous-session">2.2 Image transformation with Tensorflow/Pytorch (a different one from the previous session)</h3>
<h4 id="notebooks-on-this-session-8">Notebooks on this session</h4>
<ul>
<li>Super resolution example</li>
</ul>
<h4 id="content-8">Content</h4>
<ul>
<li>Use Deep Learning image and the super resolution problem</li>
<li>Use cloud data from Data Fabric</li>
<li>Ensure that MLFlow/Tensorboard are being used</li>
<li>Deploy a super resolution service with UI</li>
</ul>
<p><a id=section2-3> </a></p>
<h3 id="23-generating-text-by-character">2.3 Generating text by character</h3>
<h4 id="notebooks-on-this-session-9">Notebooks on this session</h4>
<ul>
<li>Shakespeare example</li>
</ul>
<h4 id="content-9">Content</h4>
<ul>
<li>Explain basic character generation using statistical patterns</li>
</ul>
<p><a id=section2-4> </a></p>
<h3 id="24-simple-qa-with-bert">2.4 Simple Q&A with Bert</h3>
<h4 id="notebooks-on-this-session-10">Notebooks on this session</h4>
<ul>
<li>Bert QA</li>
</ul>
<h4 id="content-10">Content</h4>
<ul>
<li>Explain basic usage of Hugging Face and transformers</li>
</ul>
<hr>
<p><a id=using-NGC-resources></a></p>
<h2 id="3-interating-nvidias-ngc-resources-with-ai-studio">3. Interating NVidia's NGC Resources with AI Studio</h2>
<ul>
<li>Folder: ngc-integration</li>
</ul>
<p>Here, we will aggregate the demos that use NGC resources, to show how to use them to our use cases</p>
<p><a id=section3-1> </a></p>
<h3 id="31-using-rapids-to-accelerate-data-processing">3.1 Using Rapids to accelerate data processing</h3>
<h4 id="notebooks-on-this-session-11">Notebooks on this session</h4>
<ul>
<li>Rapids/Pandas Stock Demo</li>
</ul>
<h4 id="content-11">Content</h4>
<ul>
<li>Show how Rapids can accelerate data operations done in pandas</li>
</ul>
<p><a id=section3-2> </a></p>
<h3 id="32-geoprocessing-with-rapids">3.2 GeoProcessing with Rapids</h3>
<h4 id="notebooks-on-this-session-12">Notebooks on this session</h4>
<ul>
<li>Rapids OpenCellID example</li>
</ul>
<h4 id="content-12">Content</h4>
<ul>
<li>Expand Rapids acceleration to Data visualization of geo processing</li>
</ul>
<p><a id=section3-3> </a></p>
<h3 id="33-using-nemo-for-audio-and-language-processing">3.3 Using NeMo for audio and language processing</h3>
<h4 id="notebooks-on-this-session-13">Notebooks on this session</h4>
<ul>
<li>Audio translation examples</li>
</ul>
<h4 id="content-13">Content</h4>
<ul>
<li>Nemo Framework image and how to use it in AI Studio</li>
<li>Download models using NGC integration</li>
<li>Running the models inside notebook</li>
<li>Publishing a service using the models</li>
</ul>
<hr>
<p><a id=using-prometheus></a></p>
<h2 id="4-gen-ai-with-ai-studio-and-galileo">4. Gen AI with AI Studio and Galileo</h2>
<p>This actually is the same repository as the templates for <a href="https://github.com/HPInc/aistudio-galileo-templates">Prometheus</a></p>
<p><a id=section4-1> </a></p>
<h3 id="41-general-chatbot-with-cloud-model">4.1 General Chatbot with cloud model</h3>
<h4 id="notebooks-on-this-session-14">Notebooks on this session</h4>
<ul>
<li>Prometheus chatbot template</li>
</ul>
<h4 id="content-14">Content</h4>
<ul>
<li>Creating a chatbot with langchain</li>
<li>Using OpenAI model</li>
<li>Evaluating experiment with Galileo Evaluate</li>
<li>Using feedbacks from Galileo Evaluate to improve prompt</li>
</ul>
<p><a id=section4-2> </a></p>
<h3 id="42-galileo-observe-and-protect">4.2 Galileo Observe and Protect</h3>
<h4 id="notebooks-on-this-session-15">Notebooks on this session</h4>
<ul>
<li>Prometheus chatbot template</li>
</ul>
<h4 id="content-15">Content</h4>
<ul>
<li>Instrumenting the code with Galileo Observe</li>
<li>Monitoring the code with Galileo Observe interface</li>
<li>Instrumenting the code with Galileo Protect</li>
<li>Deploying the model locally</li>
<li>Monitoring Galileo Protect errors and alerts</li>
</ul>
<p><a id=section4-3> </a></p>
<h3 id="43-summarization-with-local-model">4.3 Summarization with local model</h3>
<h4 id="notebooks-on-this-session-16">Notebooks on this session</h4>
<ul>
<li>Prometheus summarization template</li>
</ul>
<h4 id="content-16">Content</h4>
<ul>
<li>Creating a custom pipeline for summarization</li>
<li>Using multiple data connectors</li>
<li>Using locally deployed model</li>
<li>Custom chains on Galileo Evaluate</li>
<li>Custom scorers on Galileo Evaluate</li>
<li>Deploying the service and adding Observe and Protect</li>
</ul>
<p><a id=section4-4> </a></p>
<h3 id="44-code-generation-with-ai-studio-and-galileo">4.4 Code Generation with AI Studio and Galileo</h3>
<h4 id="notebooks-on-this-session-17">Notebooks on this session</h4>
<ul>
<li>Prometheus code generation example</li>
</ul>
<h4 id="content-17">Content</h4>
<ul>
<li>Explain the content of this example</li>
</ul>
<p><a id=section4-5> </a></p>
<h3 id="45-text-generation-with-ai-studio-and-galileo">4.5 Text Generation with AI Studio and Galileo</h3>
<h4 id="notebooks-on-this-session-18">Notebooks on this session</h4>
<ul>
<li>Prometheus text generation example</li>
</ul>
<h4 id="content-18">Content</h4>
<ul>
<li>Explain the content of this example</li>
</ul>
</body>
</html>