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Accelerate Data Science Workflows with Zero Code Changes
This 4-hour course on Accelerate Data Science Workflows with Zero Code Changes is designed for data scientists familiar with data processing and Python libraries. By leveraging NVIDIA RAPIDS, you can GPU-accelerate your existing data science tasks without altering your code. The course highlights the benefits of a unified workflow across CPUs and GPUs, enabling quicker and more efficient data handling. Participants will witness a significant reduction in processing time, enhancing productivity and efficiency. By the end, you will be adept at enhancing various data processing and machine learning workflows.
Purchase This Course
USD
View Fees Breakdown
Course Fee | 750 |
Total Fees |
750 (USD) |
USD
View Fees Breakdown
Course Fee | 575 |
Total Fees |
575 (USD) |
USD
View Fees Breakdown
Flexi Video | 16,449 |
Official E-coursebook | |
Exam Voucher (optional) | |
Hands-On-Labs2 | 4,159 |
+ GST 18% | 4,259 |
Total Fees (without exam & Labs) |
22,359 (INR) |
Total Fees (with exam & Labs) |
28,359 (INR) |
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♱ Excluding VAT/GST
You can request classroom training in any city on any date by Requesting More Information
♱ Excluding VAT/GST
You can request classroom training in any city on any date by Requesting More Information
To ensure a successful learning experience in the "Accelerate Data Science Workflows with Zero Code Changes" course offered by Koenig Solutions, it is recommended that participants have:
These prerequisites are designed to ensure that learners can fully benefit from the training and are not overwhelmed by the course content.
Accelerate Data Science Workflows with Zero Code Changes is a 4-hour course designed for data professionals aiming to leverage GPU-acceleration using NVIDIA RAPIDS for faster data processing and machine learning workflows.
Introduction to the Course's Learning Outcomes and Concepts Covered
Accelerate Data Science Workflows with Zero Code Changes is a 4-hour course designed to teach data scientists how to use NVIDIA RAPIDS for GPU acceleration in their existing workflows, leading to faster data processing and increased efficiency.
Learning Objectives and Outcomes