Nvidia Enhancing Data Science Outcomes With Efficient Workflow (EDSOEW)
Classroom Schulung | Deutsch | Anspruch
Schulungsdauer: 1 Tag
Ziele
Learn how to create an end-to-end, hardware-accelerated machine learning pipeline for large datasets. Throughout the development process, you’ll use diagnostic tools to identify delays and learn to mitigate common pitfalls.
Please note that once a booking has been confirmed, it is non-refundable. This means that after you have confirmed your seat for an event, it cannot be cancelled and no refund will be issued, regardless of attendance.
Zielgruppe
This course is designed for data engineers, machine learning engineers, and data scientists who work with large-scale data processing and want to optimize ETL pipelines and deep learning training workflows. It is ideal for professionals in industries such as finance, healthcare, e-commerce, and AI who need to efficiently process and analyze massive datasets. Participants should have experience with Python, data transformation frameworks (e.g., Pandas, Dask, or CuDF), and a basic understanding of machine learning or deep learning concepts. Prior experience with GPU acceleration is helpful but not required.
Voraussetzungen
- 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.
Agenda
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.
Ziele
Learn how to create an end-to-end, hardware-accelerated machine learning pipeline for large datasets. Throughout the development process, you’ll use diagnostic tools to identify delays and learn to mitigate common pitfalls.
Please note that once a booking has been confirmed, it is non-refundable. This means that after you have confirmed your seat for an event, it cannot be cancelled and no refund will be issued, regardless of attendance.
Zielgruppe
This course is designed for data engineers, machine learning engineers, and data scientists who work with large-scale data processing and want to optimize ETL pipelines and deep learning training workflows. It is ideal for professionals in industries such as finance, healthcare, e-commerce, and AI who need to efficiently process and analyze massive datasets. Participants should have experience with Python, data transformation frameworks (e.g., Pandas, Dask, or CuDF), and a basic understanding of machine learning or deep learning concepts. Prior experience with GPU acceleration is helpful but not required.
Voraussetzungen
- 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.
Agenda
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.
