Boost Your AI Skills Today
Looking to advance your expertise in data science? At the end of this article, make sure to review our resource collection.
As a data scientist, you hold a key role in the rapidly expanding generative AI (GenAI) world. While platforms like Hugging Face* and LangChain are at the forefront of AI innovation, your expertise in data analysis, modeling, and interpretation remains crucial. GenAI tools can generate impressive results, but they still rely heavily on clean, well-structured data and insightful interpretation—areas where data scientists excel. With your deep understanding of data and statistical methods, you can guide GenAI models to make more accurate, actionable predictions. Far from being sidelined, your role as a data scientist is pivotal in ensuring GenAI systems are built on solid, data-driven foundations, enabling them to reach their full potential. Here’s how you can lead the way:
- Data Quality Is Key – Even the advanced GenAI models are only as effective as the data they rely upon. AI tools like pandas and Modin* allow you to clean, preprocess, and manipulate huge datasets by ensuring the data is meaningful.
- Exploratory Data Analysis and Interpretation – Before developing the models, it is crucial to understand the data’s characteristics and patterns. Various data science frameworks such as Matplotlib and seaborn visualize data and model outputs, helping the developers to understand the data, decide on features, and interpret the models.
- Model Optimization and Evaluation: AI frameworks such as scikit-learn*, PyTorch*, and TensorFlow* provide a range of algorithms for model development. They offer various methods for conducting cross-validation, optimizing hyperparameters, and evaluating performance to refine models and enhance their performance.
- Model Deployment and Integration: Tools like MLflow and ONNX* Runtime assist in experimentation tracking and cross-platform deployment. This makes it easier for the developers to manage their projects end to end by ensuring the models continue to perform well in production.
Optimized AI Frameworks and Tools from Intel
Developers can use the existing software that they are familiar with in data analytics, machine learning, and deep learning (for example, Modin, NumPy, scikit-learn, and PyTorch). Intel has optimized the existing AI tools and frameworks, which are built on the foundation of a unified, open multiarchitecture, multivendor software platform oneAPI programming model for different stages of AI workflow, including data preparation, model training, inference, and deployment.
For example:
- Data Engineering and Model Development: Use AI Tools from Intel, which includes Python* tools and frameworks such as Modin, Intel® Optimization for XGBoost*, Intel® Extension for Scikit-learn*, PyTorch* Optimizations from Intel, and TensorFlow* Optimizations from Intel to accelerate end-to-end data science pipelines on Intel® architecture.
- Optimization and Deployment: Intel® Neural Compressor performs model optimization to reduce the model size and increase the speed of deep learning inference for deployment on CPUs or GPUs. OpenVINO™ toolkit is used for optimizing and deploying models across Intel® processors and different hardware platforms.
These AI tools will help you to achieve increased performance on your Intel hardware platforms.
Resource Library
Explore our set of high-quality, expertly developed, and carefully chosen resources focused on the fundamental data science skills developers require. It covers both machine learning and deep learning frameworks.
What you’ll learn:
- Analyze huge datasets and speed up the extract, transform, and load (ETL) process for large DataFrames using Modin
- Use optimized AI frameworks from Intel (such as Intel Optimization for XGBoost, Intel Extension for Scikit-learn, Intel Optimization for PyTorch, and Intel Optimization for TensorFlow) to accelerate performance on Intel hardware
- Implement and deploy AI workloads on Intel® Tiber™ AI Cloud using Intel-optimized software on the latest Intel platforms
How to Get Started
Data Engineering and Machine Learning Frameworks
Step 1: Watch the videos and read the getting started articles for Modin, Intel Extension for Scikit-learn, and Intel Optimization for XGBoost.
Modin: The video covers when to use Modin and how to apply Modin and pandas selectively to get the faster overall turnaround time. For detailed information, there is also a quick start guide for Modin.
Intel® Extension for Scikit-learn: This guide introduces you to the extension, provides a step-by-step code walkthrough, and highlights the performance benefits of using it. Additionally, there is a video on how to speed up K-means clustering, PCA, and silhouette machine learning algorithms.
Intel Optimization for XGBoost: This simple guide presents Intel Optimization for XGBoost and how to improve training and inference performance with Intel optimizations.
Step 2: Build and develop machine learning workloads on Intel Tiber AI Cloud.
Check out this guide on how to use Intel Tiber AI Cloud and run machine learning workloads on it using Modin, scikit-learn, and XGBoost.
Step 3: Build an end-to-end machine learning workflow on census data using Modin and scikit-learn*.
Implement the code sample presented in this article to run an end-to-end machine learning workload on US census data from 1970 to 2010. The code sample demonstrates how to perform exploratory data analysis using Intel Distribution of Modin and the ridge regression algorithm using the Intel Extension for Scikit-learn library.
Deep Learning Frameworks
Step 4: Get started with the videos and read the introductory articles for PyTorch Optimizations from Intel and TensorFlow Optimizations from Intel.
PyTorch Optimizations from Intel: Check out the article on how to get started with Intel Extension for PyTorch and jump-start your training and inference workloads with it. There is also a short video that shows how to run PyTorch inference on an Intel® Data Center GPU Flex Series using the extension.
TensorFlow Optimizations from Intel: The video and the article introduce Intel® Extension for TensorFlow and show how to use the extension to jump-start your AI workloads.
Step 5: Harness PyTorch and TensorFlow for AI on Intel® Tiber™ AI Cloud.
In this article, we demonstrate how to develop and run complex AI workloads using PyTorch and TensorFlow on Intel Tiber AI Cloud.
Step 6: Accelerate text generation with LSTM using Intel® Extension for TensorFlow.
We present a code sample in this article to show how to train your long short-term memory (LSTM) model faster for text generation by using Intel® Extension for TensorFlow.
Step 7: Build an interactive chat-generation model using DialoGPT and PyTorch.
Learn how to create an interactive chat model with the pretrained DialoGPT model from Hugging Face and Intel Extension for PyTorch to perform dynamic quantization on the model.