AI Frameworks: Crucial to the Success of GenAI
Develop Your AI Capabilities Now
You play a crucial part in the quickly growing field of generative artificial intelligence (GenAI) as a data scientist. Your proficiency in data analysis, modeling, and interpretation is still essential, even though platforms like Hugging Face and LangChain are at the forefront of AI research.
Although GenAI systems are capable of producing remarkable outcomes, they still mostly depend on clear, organized data and perceptive interpretation areas in which data scientists are highly skilled. You can direct GenAI models to produce more precise, useful predictions by applying your in-depth knowledge of data and statistical techniques. In order to ensure that GenAI systems are based on strong, data-driven foundations and can realize their full potential, your job as a data scientist is crucial. Here’s how to take the lead:
Data Quality Is Crucial
The effectiveness of even the most sophisticated GenAI models depends on the quality of the data they use. By guaranteeing that the data is relevant, AI tools like Pandas and Modin enable you to clean, preprocess, and manipulate large datasets.
Analysis and Interpretation of Exploratory Data
It is essential to comprehend the features and trends of the data before creating the models. Data and model outputs are visualized via a variety of data science frameworks, like Matplotlib and Seaborn, which aid developers in comprehending the data, selecting features, and interpreting the models.
Model Optimization and Evaluation
A variety of algorithms for model construction are offered by AI frameworks like scikit-learn, PyTorch, and TensorFlow. To improve models and their performance, they provide a range of techniques for cross-validation, hyperparameter optimization, and performance evaluation.
Model Deployment and Integration
Tools such as ONNX Runtime and MLflow help with cross-platform deployment and experimentation tracking. By guaranteeing that the models continue to function successfully in production, this helps the developers oversee their projects from start to finish.
Intel’s Optimized AI Frameworks and Tools
The technologies that developers are already familiar with in data analytics, machine learning, and deep learning (such as Modin, NumPy, scikit-learn, and PyTorch) can be used. For the many phases of the AI process, such as data preparation, model training, inference, and deployment, Intel has optimized the current AI tools and AI frameworks, which are based on a single, open, multiarchitecture, multivendor software platform called oneAPI programming model.
Data Engineering and Model Development:
To speed up end-to-end data science pipelines on Intel architecture, use Intel’s AI Tools, which include Python tools and frameworks like Modin, Intel Optimization for TensorFlow Optimizations, PyTorch Optimizations, IntelExtension for Scikit-learn, and XGBoost.
Optimization and Deployment
For CPU or GPU deployment, Intel Neural Compressor speeds up deep learning inference and minimizes model size. Models are optimized and deployed across several hardware platforms including Intel CPUs using the OpenVINO toolbox.
You may improve the performance of your Intel hardware platforms with the aid of these AI tools.
Library of Resources
Discover collection of excellent, professionally created, and thoughtfully selected resources that are centered on the core data science competencies that developers need. Exploring machine and deep learning AI frameworks.
What you will discover:
- Use Modin to expedite the extract, transform, and load (ETL) process for enormous DataFrames and analyze massive datasets.
- To improve speed on Intel hardware, use Intel’s optimized AI frameworks (such as Intel Optimization for XGBoost, Intel Extension for Scikit-learn, Intel Optimization for PyTorch, and Intel Optimization for TensorFlow).
- Use Intel-optimized software on the most recent Intel platforms to implement and deploy AI workloads on Intel Tiber AI Cloud.
How to Begin
Frameworks for Data Engineering and Machine Learning
- Step 1: View the Modin, Intel Extension for Scikit-learn, and Intel Optimization for XGBoost videos and read the introductory papers.
- Modin: To achieve a quicker turnaround time overall, the video explains when to utilize Modin and how to apply Modin and Pandas judiciously. A quick start guide for Modin is also available for more in-depth information.
- Scikit-learn Intel Extension: This tutorial gives you an overview of the extension, walks you through the code step-by-step, and explains how utilizing it might improve performance. A movie on accelerating silhouette machine learning techniques, PCA, and K-means clustering is also available.
- Intel Optimization for XGBoost: This straightforward tutorial explains Intel Optimization for XGBoost and how to use Intel optimizations to enhance training and inference performance.
- Step 2: Use Intel Tiber AI Cloud to create and develop machine learning workloads.
- On Intel Tiber AI Cloud, this tutorial runs machine learning workloads with Modin, scikit-learn, and XGBoost.
- Step 3: Use Modin and scikit-learn to create an end-to-end machine learning process using census data.
- Run an end-to-end machine learning task using 1970–2010 US census data with this code sample. The code sample uses the Intel Extension for Scikit-learn module to analyze exploratory data using ridge regression and the Intel Distribution of Modin.
Deep Learning Frameworks
- Step 4: Begin by watching the videos and reading the introduction papers for Intel’s PyTorch and TensorFlow optimizations.
- Intel PyTorch Optimizations: Read the article to learn how to use the Intel Extension for PyTorch to accelerate your workloads for inference and training. Additionally, a brief video demonstrates how to use the addon to run PyTorch inference on an Intel Data Center GPU Flex Series.
- Intel’s TensorFlow Optimizations: The article and video provide an overview of the Intel Extension for TensorFlow and demonstrate how to utilize it to accelerate your AI tasks.
- Step 5: Use TensorFlow and PyTorch for AI on the Intel Tiber AI Cloud.
- In this article, it show how to use PyTorch and TensorFlow on Intel Tiber AI Cloud to create and execute complicated AI workloads.
- Step 6: Speed up LSTM text creation with Intel Extension for TensorFlow.
- The Intel Extension for TensorFlow can speed up LSTM model training for text production.
- Step 7: Use PyTorch and DialoGPT to create an interactive chat-generation model.
- Discover how to use Hugging Face’s pretrained DialoGPT model to create an interactive chat model and how to use the Intel Extension for PyTorch to dynamically quantize the model.
