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Accelerating End-to-End Data Science Workflows

The Accelerating End-to-End Data Science Workflows course by Koenig Original equips data scientists and machine learning engineers with GPU-accelerated techniques to overcome slow, CPU-limited data processing. Learners master RAPIDS libraries like cuDF, cuML, and cuGraph to execute workflows 100x faster, enabling rapid iteration and real-time analytics. With 87% of Fortune 500 companies now adopting GPU acceleration for data science, this course addresses the critical need for speed and scalability in production environments.

This Koenig Original course prepares learners for the NVIDIA-Certified Associate in Accelerated Data Science (NCA-ADS) credential, featuring proctored exam alignment and hands-on labs. Koenig’s Guaranteed-to-Run scheduling ensures training proceeds even with a single registrant, providing unmatched flexibility. Graduates gain a verifiable certification and the practical ability to deploy high-performance data science pipelines, positioning them for roles in AI engineering and data science leadership.

56 Hours (7 Days)
Live Online / Classroom
3+ professionals trained

Training Formats & Pricing

1-on-1 USD 2,850
Dedicated instructor, your schedule Fastest
Public Batch USD 2,250
Group class, fixed schedule Most Popular
Self-Paced On Request
Recorded sessions, learn anytime Best Value

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Course Overview

Master GPU-accelerated data science with this NVIDIA DLI curriculum delivered by Koenig Original. This course provides essential training for professionals managing high-performance workloads, mirroring the infrastructure management skills found in AZ-104 training. You will learn to optimize Azure compute, Azure storage, and Azure networking configurations to support NVIDIA RAPIDS workflows. By leveraging GPU-accelerated data pipelines, you will reduce iteration times and improve model performance. This expert-led program ensures you gain the technical proficiency required to deploy scalable, efficient data science solutions within modern cloud environments, effectively bridging the gap between infrastructure administration and advanced analytics.

Lab Environment

Students engage with cuDF, cuML, cuGraph, and XGBoost within a cloud-based, GPU-accelerated lab environment. Hands-on labs focus on building end-to-end workflows, including accelerating data preprocessing, training machine learning models, and performing graph analytics at scale. All labs are conducted on fully configured NVIDIA DLI servers, ensuring immediate access to high-performance computing resources. This setup allows learners to focus on implementation and optimization without infrastructure delays, utilizing official NVIDIA DLI curriculum materials to ensure industry-standard best practices throughout the learning process.

What You'll Learn

✓Implement GPU-accelerated data preparation using NVIDIA cuDF to reduce data processing time by up to 50x in Accelerating End-to-End Data Science Workflows by Koenig Original.
✓Apply XGBoost and cuML algorithms to achieve a 10x to 100x increase in machine learning model training speed compared to CPU-based workflows.
✓Execute complex graph analysis using RAPIDS cuGraph to decrease computation latency for large-scale network datasets by at least 80%.
✓Optimize end-to-end data science pipelines using Apache Arrow to reduce data serialization and transfer overhead by 40%.
✓Deploy GPU-accelerated interactive visualizations with cuXFilter to maintain sub-second response times for dashboards containing millions of data points.
✓Manage integrated data science pipelines on the Koenig Original platform to improve overall workflow throughput and reduce time-to-insight by 60%.

Skills You'll Gain

cuDF DataFrames Apache Arrow GPU-Accelerated Data Preparation XGBoost cuML Algorithms KNN Classification DBSCAN Clustering Logistic Regression cuGraph Analytics Single-Source Shortest Path cuXFilter Visualization RAPIDS Libraries GPU-Accelerated Machine Learning NVIDIA RAPIDS End-to-End Data Science Workflows GPU Data Science Large-Scale Data Analysis

Prerequisites

Recommended knowledge before taking this course
  • Experience with Python programming language is essential for mastering the Accelerating End-to-End Data Science Workflows course by Koenig Original, a leading provider of data science training. Familiarity with the pandas library and DataFrame operations helps streamline data analysis tasks, making your workflows more efficient. A working knowledge of the scikit-learn machine learning library enables you to build and evaluate models effectively. Understanding GPU-accelerated computing concepts allows you to leverage hardware for faster data processing. Experience with data manipulation using NumPy arrays is crucial for handling large datasets efficiently. Basic understanding of the Apache Arrow memory format ensures optimal data interchange between systems. This prerequisites list is designed to prepare learners for advanced data science techniques, helping you gain the skills to accelerate your data workflows and solve complex problems faster. With over 10 years of training expertise, Koenig Original ensures you gain practical, industry-ready skills that can transform your data science career.
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Certification Exam

Everything you need to know about the Accelerating End-to-End Data Science Workflows certification exam

Exam Details
Exam Name
Accelerating End-to-End Data Science Workflows
Exam Cost
$200
Format
Multiple choice, labs & case studies
Questions
Duration
Passing Score
Validity
Retake Policy
Candidates must wait 14 days before retaking the exam if they do not pass. There is no limit on the number of attempts, but each attempt requires a new exam registration and fee payment.
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Course Curriculum

Structured learning with hands-on labs and real-world scenarios

1
Day 1– GPU-Accelerated Data Preparation
This module introduces GPU-accelerated data preparation using NVIDIA cuDF, requiring a CUDA-enabled GPU and Python 3.8+ with Conda. Master GPU-accelerated data preparation using cuDF to achieve 10x-50x faster processing than CPU-based pandas. Leverage Apache Arrow for high-speed memory handling Process massive datasets with RAPIDS data science frameworks Execute rapid data cleaning and transformation on GPUs Optimize end-to-end data pipelines for maximum throughput Accelerate feature extraction for complex data science workflows Integrate cuDF seamlessly with existing data science toolsets Benchmark GPU versus CPU processing speeds for efficiency
2
Day 2– GPU-Accelerated Machine Learning with cuML
This module focuses on scalable machine learning using NVIDIA cuML, optimized for high-performance CUDA environments. Apply cuML algorithms for scalable machine learning performance with 10x-50x speedups over CPU implementations. Train high-speed KNN models using GPU acceleration techniques Execute DBSCAN clustering on massive, complex datasets Perform logistic regression using NVIDIA cuML libraries Compare predictive model accuracy across diverse algorithms Optimize hyperparameters with GPU-accelerated training workflows Evaluate machine learning model performance at enterprise scale Integrate cuML seamlessly into standard scikit-learn pipelines
3
Day 3– Advanced Machine Learning with XGBoost
This module covers advanced gradient boosting techniques using NVIDIA-accelerated XGBoost on compatible GPU hardware. Deploy GPU-accelerated XGBoost training for 10x-50x faster insights compared to traditional CPU training. Fine-tune XGBoost hyperparameters to boost model performance Manage imbalanced datasets effectively using XGBoost techniques Scale gradient boosting for big data processing requirements Compare XGBoost against traditional tree-based modeling methods Export and deploy production-ready XGBoost machine learning models Monitor training convergence metrics on high-performance GPUs Optimize memory usage during intensive model training cycles
4
Day 4– GPU-Accelerated Graph Analytics with cuGraph
This module explores large-scale graph analytics using NVIDIA cuGraph, requiring a CUDA-enabled GPU environment. Execute single-source shortest path analysis with cuGraph, delivering 10x-50x performance gains over CPU-based graph libraries. Perform large-scale graph analytics using GPU acceleration Analyze complex network structures with NVIDIA cuGraph Compute critical graph centrality metrics with high efficiency Visualize intricate graph data using GPU-accelerated rendering Process massive graph datasets in minimal execution time Integrate cuGraph modules into existing data science pipelines Benchmark graph algorithm performance for optimized workflows
5
Day 5– Data Visualization and Workflow Integration
This module integrates end-to-end workflows using NVIDIA cuXFilter for real-time visualization on GPU-accelerated systems. Build interactive visualizations using NVIDIA cuXFilter to enable real-time exploration of datasets 10x-50x faster than CPU tools. Create high-performance GPU-accelerated data dashboards Integrate advanced visualizations into data science workflows Enable real-time data exploration for faster decision-making Combine cuDF, cuML, and cuGraph outputs for insights Deploy end-to-end GPU-accelerated data science pipelines Improve iteration speed using RAPIDS acceleration technology Transition legacy CPU workflows to GPU-accelerated environments

What's Included in Your Training

Every enrollment comes packed with resources to maximise your learning and exam success

Hands-On Lab

Live Lab Sandbox

Real Environment

Practice in a real lab environment with full access to the tools and services covered in the course.

30+ Guided Labs

30+

Step-by-step lab exercises designed to reinforce each module with practical, hands-on tasks.

Lab Manual Included

Full Guide

Comprehensive lab guide with detailed instructions, screenshots, and troubleshooting tips.

Post-Training Access

30 Days

30 days of extended lab access after your training ends so you can continue practicing.

Career Outcomes

88%

of Accelerating End-to-End Data Science Workflows certified professionals report career advancement within 6 months

Salary Impact

+28%

Average salary increase reported after obtaining the Accelerating End-to-End Data Science Workflows certification

Typical Salary Range (Global)
Entry$90,000–$115,000
Mid$115,000–$145,000
Senior$145,000–$180,000

*Source: Glassdoor / LinkedIn 2025

Job Roles

6
  • Machine Learning Engineer
  • Data Scientist
  • AI DevOps Engineer
  • GPU Solutions Architect
  • Applied Data Scientist
  • Deep Learning Performance Engineer

Companies Hiring

5,000+
NVIDIA Google Microsoft Amazon IBM Accenture Deloitte Wipro Infosys Capgemini

and 5,000+ organizations worldwide seeking Accelerating End-to-End Data Science Workflows certified professionals

Real Transformations

Course Student Reviews

Real results from IT professionals who trained with Koenig — rated 4.9/5 from 18,400+ verified reviews.

18,400+
Verified Reviews
4.9 / 5
Average Rating
95%
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  • ★★★★★

    “Passed AZ-104 on first attempt. The MCT knew the exact exam patterns and the labs were exactly what Microsoft tests. Worth every penny.”

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    Rahul M.

    Azure Administrator

    AZ-104 Certified ✓ Verified
  • ★★★★★

    “I trained 15 of my team members for SC-200. Koenig's on-site delivery was seamless and all 15 passed within 3 months.”

    Sarah K.

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    CISO, Financial Services

    Enterprise Client ✓ Verified
  • ★★★★★

    “The 1-on-1 format was a game changer. My trainer adjusted the pace to my schedule and I cleared PL-300 while working full-time.”

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    Business Intelligence Lead

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  • ★★★★★

    “From AZ-900 to AZ-305 in 6 months. Koenig's structured roadmap and MCT mentoring made the expert level achievable.”

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    Cloud Solutions Architect

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    “SC-900 and SC-300 back to back — both cleared first try. The security curriculum at Koenig is incredibly thorough and up to date.”

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    Data Platform Engineer

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  • ★★★★★

    “Our whole DevOps team got AZ-400 certified through Koenig's corporate training. Smooth logistics and top-tier MCTs throughout.”

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    Carlos R.

    Engineering Manager

    AZ-400 Team Training ✓ Verified
  • ★★★★★

    “Passed AZ-104 on first attempt. The MCT knew the exact exam patterns and the labs were exactly what Microsoft tests. Worth every penny.”

    Rahul M.

    Rahul M.

    Azure Administrator

    AZ-104 Certified ✓ Verified
  • ★★★★★

    “I trained 15 of my team members for SC-200. Koenig's on-site delivery was seamless and all 15 passed within 3 months.”

    Sarah K.

    Sarah K.

    CISO, Financial Services

    Enterprise Client ✓ Verified
  • ★★★★★

    “The 1-on-1 format was a game changer. My trainer adjusted the pace to my schedule and I cleared PL-300 while working full-time.”

    Ahmed R.

    Ahmed R.

    Business Intelligence Lead

    PL-300 Certified ✓ Verified
  • ★★★★★

    “From AZ-900 to AZ-305 in 6 months. Koenig's structured roadmap and MCT mentoring made the expert level achievable.”

    Priya S.

    Priya S.

    Cloud Solutions Architect

    AZ-305 Expert ✓ Verified
  • ★★★★★

    “As an L&D head I've used 5 training vendors. Koenig's MCT quality, MOC materials, and ESI compliance is in a different league.”

    James T.

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    Head of L&D, UK Enterprise

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  • ★★★★★

    “SC-900 and SC-300 back to back — both cleared first try. The security curriculum at Koenig is incredibly thorough and up to date.”

    Aisha N.

    Aisha N.

    Security Analyst

    SC-300 Certified ✓ Verified
  • ★★★★★

    “AI-102 was daunting but the trainer broke it down perfectly. Real Azure OpenAI labs made the difference. Highly recommend.”

    David L.

    David L.

    AI Engineer

    AI-102 Certified ✓ Verified
  • ★★★★★

    “DP-600 Fabric certification done in 3 weeks of part-time study. The customised schedule around my timezone was a lifesaver.”

    Mei W.

    Mei W.

    Data Platform Engineer

    DP-600 Certified ✓ Verified
  • ★★★★★

    “Our whole DevOps team got AZ-400 certified through Koenig's corporate training. Smooth logistics and top-tier MCTs throughout.”

    Carlos R.

    Carlos R.

    Engineering Manager

    AZ-400 Team Training ✓ Verified
  • ★★★★★

    “Passed AZ-104 on first attempt. The MCT knew the exact exam patterns and the labs were exactly what Microsoft tests. Worth every penny.”

    Rahul M.

    Rahul M.

    Azure Administrator

    AZ-104 Certified ✓ Verified
  • ★★★★★

    “I trained 15 of my team members for SC-200. Koenig's on-site delivery was seamless and all 15 passed within 3 months.”

    Sarah K.

    Sarah K.

    CISO, Financial Services

    Enterprise Client ✓ Verified
  • ★★★★★

    “The 1-on-1 format was a game changer. My trainer adjusted the pace to my schedule and I cleared PL-300 while working full-time.”

    Ahmed R.

    Ahmed R.

    Business Intelligence Lead

    PL-300 Certified ✓ Verified
  • ★★★★★

    “Passed AZ-104 on first attempt. The MCT knew the exact exam patterns and the labs were exactly what Microsoft tests. Worth every penny.”

    Rahul M.

    Rahul M.

    Azure Administrator

    AZ-104 Certified ✓ Verified
  • ★★★★★

    “I trained 15 of my team members for SC-200. Koenig's on-site delivery was seamless and all 15 passed within 3 months.”

    Sarah K.

    Sarah K.

    CISO, Financial Services

    Enterprise Client ✓ Verified
  • ★★★★★

    “The 1-on-1 format was a game changer. My trainer adjusted the pace to my schedule and I cleared PL-300 while working full-time.”

    Ahmed R.

    Ahmed R.

    Business Intelligence Lead

    PL-300 Certified ✓ Verified
  • ★★★★★

    “From AZ-900 to AZ-305 in 6 months. Koenig's structured roadmap and MCT mentoring made the expert level achievable.”

    Priya S.

    Priya S.

    Cloud Solutions Architect

    AZ-305 Expert ✓ Verified
  • ★★★★★

    “As an L&D head I've used 5 training vendors. Koenig's MCT quality, MOC materials, and ESI compliance is in a different league.”

    James T.

    James T.

    Head of L&D, UK Enterprise

    100+ Learners Trained ✓ Verified
  • ★★★★★

    “SC-900 and SC-300 back to back — both cleared first try. The security curriculum at Koenig is incredibly thorough and up to date.”

    Aisha N.

    Aisha N.

    Security Analyst

    SC-300 Certified ✓ Verified
  • ★★★★★

    “From AZ-900 to AZ-305 in 6 months. Koenig's structured roadmap and MCT mentoring made the expert level achievable.”

    Priya S.

    Priya S.

    Cloud Solutions Architect

    AZ-305 Expert ✓ Verified
  • ★★★★★

    “As an L&D head I've used 5 training vendors. Koenig's MCT quality, MOC materials, and ESI compliance is in a different league.”

    James T.

    James T.

    Head of L&D, UK Enterprise

    100+ Learners Trained ✓ Verified
  • ★★★★★

    “SC-900 and SC-300 back to back — both cleared first try. The security curriculum at Koenig is incredibly thorough and up to date.”

    Aisha N.

    Aisha N.

    Security Analyst

    SC-300 Certified ✓ Verified
  • ★★★★★

    “AI-102 was daunting but the trainer broke it down perfectly. Real Azure OpenAI labs made the difference. Highly recommend.”

    David L.

    David L.

    AI Engineer

    AI-102 Certified ✓ Verified
  • ★★★★★

    “DP-600 Fabric certification done in 3 weeks of part-time study. The customised schedule around my timezone was a lifesaver.”

    Mei W.

    Mei W.

    Data Platform Engineer

    DP-600 Certified ✓ Verified
  • ★★★★★

    “Our whole DevOps team got AZ-400 certified through Koenig's corporate training. Smooth logistics and top-tier MCTs throughout.”

    Carlos R.

    Carlos R.

    Engineering Manager

    AZ-400 Team Training ✓ Verified
  • ★★★★★

    “AI-102 was daunting but the trainer broke it down perfectly. Real Azure OpenAI labs made the difference. Highly recommend.”

    David L.

    David L.

    AI Engineer

    AI-102 Certified ✓ Verified
  • ★★★★★

    “DP-600 Fabric certification done in 3 weeks of part-time study. The customised schedule around my timezone was a lifesaver.”

    Mei W.

    Mei W.

    Data Platform Engineer

    DP-600 Certified ✓ Verified
  • ★★★★★

    “Our whole DevOps team got AZ-400 certified through Koenig's corporate training. Smooth logistics and top-tier MCTs throughout.”

    Carlos R.

    Carlos R.

    Engineering Manager

    AZ-400 Team Training ✓ Verified

Frequently Asked Questions

Everything you need to know about the Accelerating End-to-End Data Science Workflows training course

Is the certification exam included in the Accelerating End-to-End Data Science Workflows course fee, and what is the cost?
The certification exam is not included in the Koenig Original course fee. Learners pay a separate $200 fee to NVIDIA for the NCP-Accelerated Data Science certification. This credential validates GPU-accelerated workflow proficiency via Certiverse.
What training formats are available for Accelerating End-to-End Data Science Workflows, and does Koenig offer Guaranteed-to-Run scheduling?
Koenig provides live online 1-on-1, public instructor-led, and self-paced formats for this course with Guaranteed-to-Run scheduling. This ensures training starts as planned for single participants, offering maximum flexibility for diverse global learning schedules.
How long is lab access provided for Accelerating End-to-End Data Science Workflows, and what environment is used?
Learners access fully configured, GPU-accelerated cloud servers for the course duration. NVIDIA DLI typically provides 8 hours of dedicated lab time. Extensions are available via the DLI dashboard to ensure full exercise completion.
What is the Koenig rescheduling and cancellation policy for the Accelerating End-to-End Data Science Workflows course?
Koenig applies a 50% cancellation fee if training is rescheduled or cancelled within 10 days of the start date. Rescheduling is limited to one instance to maintain resource planning and accommodate urgent participant needs.
What is the format, question count, time limit, and passing score for the Accelerating End-to-End Data Science Workflows exam?
The exam features 60–70 multiple-choice and coding questions with a 120-minute limit, proctored remotely. The passing score is set by the NVIDIA certification board using psychometric analysis of overall candidate performance metrics.
How long is the Accelerating End-to-End Data Science Workflows certification valid, and what is the renewal process?
The NCP-Accelerated Data Science certification remains valid for two years. Recertification requires retaking the $200 exam. This process ensures professionals maintain current expertise in rapidly evolving GPU-accelerated data science and computing techniques.
What post-training support does Koenig provide after completing the Accelerating End-to-End Data Science Workflows course?
Koenig offers post-training support via email at flexi@koenig-solutions.com and access to expert-led doubt-clearing sessions. We also provide a Happiness Guarantee, offering full refund eligibility if you are dissatisfied with your training quality.
What are the prerequisites or experience needed for the Accelerating End-to-End Data Science Workflows course?
Learners require two to three years of hands-on experience in accelerated data science. A solid foundation in machine learning, GPU-accelerated computing, and practical experience with GPU-based optimization strategies and end-to-end workflows is essential.
What is the typical salary impact for data scientists after completing the Accelerating End-to-End Data Science Workflows course?
Data scientists with GPU-accelerated skills earn between $92,487 and $149,500 annually. Experienced professionals at top tech firms can earn up to $330,000 in total compensation, significantly exceeding standard market salary averages.
How does the Accelerating End-to-End Data Science Workflows course compare to self-study in terms of effectiveness?
The instructor-led course delivers structured learning in 8 hours, whereas self-study may take weeks. Koenig's Guaranteed-to-Run scheduling and expert access accelerate your mastery of RAPIDS libraries and complex GPU-accelerated data science workflows.

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