Enhancing Data Science Outcomes With Efficient WorkflowEDSOEWNVNvidiaNV-EDSOEW1.0<ul>
<li>Develop and deploy an accelerated end-to-end data processing pipeline for large datasets</li><li>Scale data science workflows using distributed computing</li><li>Perform DataFrame transformations that take advantage of hardware acceleration and avoid hidden slowdowns</li><li>Enhance machine learning solutions through feature engineering and rapid experimentation</li><li>Improve data processing pipeline performance by optimizing memory management and hardware utilization</li></ul><ul>
<li>Basic knowledge of a standard data science workflow on tabular data. To gain an adequate understanding, we recommend this article.</li><li>Knowledge of distributed computing using Dask. To gain an adequate understanding, we recommend the “Get Started” guide from Dask.</li><li>Completion of the DLI’s Fundamentals of Accelerated Data Science course or an ability to manipulate data using cuDF and some experience building machine learning models using cuML.</li></ul><p><strong>Introduction</strong>
</p>
<ul>
<li>Meet the instructor.</li><li>Create an account at courses.nvidia.com/join</li></ul><p><strong>Advanced Extract, Transform, and Load (ETL)</strong>
</p>
<ul>
<li>Learn to process large volumes of data efficiently for downstream analysis:<ul>
<li>Discuss current challenges of growing data sizes.</li><li>Perform ETL efficiently on large datasets.</li><li>Discuss hidden slowdowns and perform DataFrame transformations properly.</li><li>Discuss diagnostic tools to monitor and optimize hardware utilization.</li><li>Persist data in a way that’s conducive for downstream analytics.</li></ul></li></ul><p><strong>Training on Multiple GPUs With PyTorch Distributed Data Parallel (DDP)</strong>
</p>
<ul>
<li>Learn how to improve data analysis on large datasets:<ul>
<li>Build and compare classification models.</li><li>Perform feature selection based on predictive power of new and existing features.</li><li>Perform hyperparameter tuning.</li><li>Create embeddings using deep learning and clustering on embeddings.</li></ul></li></ul><p><strong>Deployment</strong>
</p>
<ul>
<li>Learn how to deploy and measure the performance of an accelerated data processing pipeline:</li><li>Deploy a data processing pipeline with Triton Inference Server.</li><li>Discuss various tuning parameters to optimize performance.</li></ul><p><strong>Assessment and Q&A</strong></p>- Develop and deploy an accelerated end-to-end data processing pipeline for large datasets
- Scale data science workflows using distributed computing
- Perform DataFrame transformations that take advantage of hardware acceleration and avoid hidden slowdowns
- Enhance machine learning solutions through feature engineering and rapid experimentation
- Improve data processing pipeline performance by optimizing memory management and hardware utilization- Basic knowledge of a standard data science workflow on tabular data. To gain an adequate understanding, we recommend this article.
- Knowledge of distributed computing using Dask. To gain an adequate understanding, we recommend the “Get Started” guide from Dask.
- Completion of the DLI’s Fundamentals of Accelerated Data Science course or an ability to manipulate data using cuDF and some experience building machine learning models using cuML.Introduction
- Meet the instructor.
- Create an account at courses.nvidia.com/join
Advanced Extract, Transform, and Load (ETL)
- Learn to process large volumes of data efficiently for downstream analysis:
- Discuss current challenges of growing data sizes.
- Perform ETL efficiently on large datasets.
- Discuss hidden slowdowns and perform DataFrame transformations properly.
- Discuss diagnostic tools to monitor and optimize hardware utilization.
- Persist data in a way that’s conducive for downstream analytics.
Training on Multiple GPUs With PyTorch Distributed Data Parallel (DDP)
- Learn how to improve data analysis on large datasets:
- Build and compare classification models.
- Perform feature selection based on predictive power of new and existing features.
- Perform hyperparameter tuning.
- Create embeddings using deep learning and clustering on embeddings.
Deployment
- Learn how to deploy and measure the performance of an accelerated data processing pipeline:
- Deploy a data processing pipeline with Triton Inference Server.
- Discuss various tuning parameters to optimize performance.
Assessment and Q&A0.5 days500.00500.00500.00500.00500.00420.00500.00690.00