Instructor Qualifications For DLI Workshops

Instructor Qualifications for all current DLI Courses

Follow the links below to ensure you have the qualifications necessary for the workshop you are interested in.

Applications of AI for Anomaly Detection

Candidates must demonstrate significant experience in data science, machine learning, deep learning, and the telecommunications industry, having worked on at least one significant AI application, either in a commercial or academic capacity, and explain their work. Qualifying experience includes:

A role as a major contributor to a project that used Deep Learning
A role as a major contributor to a project that used other Machine Learning techniques
A role as a major contributor to a project that required data science

Candidates must also demonstrate teaching experience, such as:

Classroom teaching experience
Significant presentation experience

Candidates should also have the following:

Professional Data Science Experience using Python
A working understanding of NVIDIA RAPIDS
Significant experience in machine and deep learning, specifically the use of XG Boost, AutoEncoder, and GAN models
Exposure to the telecommunications industry and cybersecurity, specifically networking and the threat of network intrusion.

Applications of AI for Predictive Maintenance

Candidates must demonstrate experience working on at least one Deep Learning application, either in a commercial or academic capacity, and explain their work. Qualifying experience includes:

Deep Learning for time-series data, work/research experience with variations of auto-encoder models, recurrent models (LSTMs) and GANs.
Measures of model accuracy, preferably in the context of industrial applications.
Familiarity with machine learning techniques. Having a thorough understanding of XGBoost algorithm is crucial to the success of course delivery.
Minimum of one deep learning library. Keras and TensorFlow are preferred

Candidates must also demonstrate teaching experience, such as:

Classroom teaching experience
Significant presentation experience

Candidates should also have the following:

Familiarity with deep learning concepts (At minimum level, having knowledge of artificial neural networks)
Python (and common python libraries used in DL, e.g., numpy, pandas, sklearn,...)
Having knowledge of TensorFlow and keras

Building AI Based Cybersecurity Pipelines

Instructor qualifications:
Candidates must have professional experience in the domain of defensive cybersecurity and data analysis. Candidates should be able to discuss their work as it relates to topics such as:

Methods and tooling used in service of defensive cybersecurity for data collection, preparation, analysis, storage etc.
Approaches to defending and resolving common cybersecurity attacks such as DOS, phishing, hijacked accounts etc.
Effective data analysis through the use of machine and deep learning models like XGBoost, autoencoders, transformers etc.
The use of GPU-accelerated libraries for use in data analysis, particularly those found in NVIDIA RAPIDS, including CLX.

Candidates must also demonstrate teaching experience, such as:

Classroom teaching experience - in person or remote
Significant presentation experience

Building Conversational AI Applications

Instructor qualifications:
Candidates must demonstrate experience working on at least one conversational AI application using automatic speech recognition (ASR) and natural language understanding (NLU), either in a commercial or academic capacity, and explain their work. Qualifying experience includes:

A professional role (Ex: Engineer, Data Scientist) on a conversational AI project that used an ASR model to transcribe spoken language and process it
A completed conversational AI project for a virtual assistant application
Academic coursework in conversational AI using neural networks

Candidates should have the following:

Basic Python competency including familiarity with variable types, loops, conditional statements, functions, array manipulations, and class objects/methods
Experience using TAO Toolkit and Riva
Basic Linux command line experience
Experience using Docker
Experience using Helm Charts and Kubernetes

Candidates must also demonstrate teaching experience, such as:

Classroom teaching experience - in person or remote
Significant presentation experience

Building Intelligent Recommender Systems

Instructor qualifications:
Candidates must demonstrate experience working on at least one end-to-end Recommender System, either in a professional or meaningful academic scenario, and explain their work. Qualifying experience includes:

A professional role (Ex: Data Engineer, Data Scientist) on a Recommender Systems project that used Deep Learning
A completed end-to-end Recommender Systems project that used Deep Learning
Academic coursework in Deep Learning for Recommender Systems

Candidates must also demonstrate teaching experience, such as:

Classroom teaching experience - both in-person and remotely
Significant presentation experience

Candidates should also have the following:

Basic Python competency including familiarity with variable types, loops, conditional statements, functions, array manipulations, and list/dict comprehension
Basic NumPy competency including familiarity with ndarrays and broadcasting rules
Experience using Docker and Docker Compose

Building Transformer Based Natural Language Processing

Instructor qualifications:
Candidates must demonstrate experience working on at least one Natural Language Processing application using a Transformer-based architecture (such as BERT), either in a commercial or academic capacity, and explain their work. Qualifying experience includes:

A professional role (Ex: Engineer, Data Scientist) on an NLP project that used a Transformer-based architecture
A completed NLP project that used a Transformer-based architecture
Academic coursework in NLP Transformer-based networks

Candidates must also demonstrate teaching experience, such as:

Classroom teaching experience - in person or remote
Significant presentation experience

Candidates should also have the following:

Basic Python competency including familiarity with variable types, loops, conditional statements, functions, array manipulations, and class objects/methods
Basic pandas and NeMo competency
Experience using NVIDIA Triton Inference Server

Computer Vision for Industrial Inspection

Instructor qualifications:
Candidates must demonstrate experience working on at least one Deep Learning application, either in a commercial or academic capacity, and explain their work. Qualifying experience includes:

Using Deep Learning techniques to tackle classification problems, preferably in the context of industrial applications.
A professional role on a computer vision project that used Deep Learning techniques.
Significant coursework in Deep Learning for computer vision that covers the various stages of the development workflow.

Candidate should have the following:

Python (and common python libraries used in DL, e.g., numpy and pandas)
Familiarity with end-to-end machine learning workflow
Familiarity with manipulating data using pandas DataFrame
Familiarity with deep learning concepts including knowledge of convolutional neural networks
Familiarity of at least one deep learning framework (Keras and TensorFlow are preferred)
Familiarity with metrics such as accuracy and inference performance
Familiarity with command-line interface and basic linux commands
Familiarity with transfer learning and fine-tuning models
Knowledge of NVIDIA’s DALI, TAO Toolkit, TensorRT, and Triton Inference Server

Candidates must also demonstrate teaching experience, such as:

Classroom teaching experience
Significant presentation experience

Data Parallelism: How to Train Deep Learning Models on Multiple GPUs

Deploying deep learning training workloads to multiple GPUs and preferably multi-node clusters
Data Parallel approaches to distributed Deep Learning
Profiling and optimizing the deep learning code
Using NGC containers
Experience in building neural networks with PyTorch
Using PyTorch DDP to deploy distributed training

Candidates must also demonstrate teaching experience, such as:

Classroom teaching experience
Significant presentation experience

Candidate should have the following:

Good understanding of the literature discussing implications of training deep neural networks with large batches. In particular, a good understanding of the LARS/LARC algorithm.
Understanding of the process used in training deep neural networks. In particular understanding of the Stochastic Gradient Descent and backpropagation algorithms.

Fundamentals of Deep Learning

Instructor qualifications:
Candidates must demonstrate experience working on a computer vision task - image classification, object detection, etc. - using deep learning in either a professional or academic setting. Foundational knowledge of natural language processing (NLP), reinforcement learning (RL) and other neural network architectures such as RNNs / LSTMs and GANs is required. Qualifying experience includes:

A professional role (Ex: Data Engineer, Data Scientist) architecting computer vision projects that use deep learning
Academic coursework in computer vision, NLP, RL and neural network architectures

Candidates must also demonstrate teaching experience, such as:

Classroom teaching experience - either via in-person or distance setting
Significant presentation experience

Candidates should also have the following:

Familiarity with basic programming fundamentals such as functions and variables
Basic Python competency

Model Parallelism: Building and Deploying Large Neural Networks

Instructor qualifications:
Candidates must demonstrate experience working on a model parallelism related task using deep learning in either a professional or academic setting foundational knowledge of optimization techniques such as activation checkpointing, mixed precision training, and gradient accumulation is required. Qualifying experience includes:

A professional role (Ex: Data Engineer, Data Scientist) architecting deep learning projects that use distributed systems such as the cloud or multi-GPU machines.
Academic coursework in large neural network architectures such as GPT-3.

Candidate should have the following:

Classroom teaching experience - either via in-person or distance setting
Significant presentation experience

Candidates must also demonstrate teaching experience, such as:

An understanding of the Slurm, NVIDIA Triton and DeepSpeed technologies
An understanding of the differences between Model and Data Parallelism

Accelerating CUDA C++ Applications with Multiple GPUs

Instructor qualifications:
Candidates must demonstrate significant experience with multiple CUDA-accelerated applications in the past, either in a professional or meaningful academic scenario, and be able to explain their work with these applications. These applications should involve the use of multiple GPUs and concurrent streams.

How your applications provide meaningful acceleration on a problem that could not be addressed as successfully in a CPU-only environment
The specifics of optimization strategies that the applications use
Specific CUDA-related technical challenges that arose while developing the applications

Candidates should have the following:

Advanced CUDA C++ experience.
Mastery of multiple techniques for performing copy/compute overlap in single and multiple GPU applications, and the ability to discuss them clearly in detail and at length.
Experience with NSight Systems.

Candidates must also demonstrate teaching experience, such as:

Classroom teaching experience
Significant presentation experience

Fundamentals of Accelerated Computing with CUDA C/C++

Instructor qualifications:
Please provide some evidence of having worked significantly with a CUDA-accelerated application in the past, either in a professional or meaningful academic scenario, and be prepared to talk about your work with others. You should be able to discuss:

How your applications provide meaningful acceleration on a problem that could not be addressed as successfully in a CPU-only environment
The specifics of optimization strategies that the applications use
Specific CUDA-related technical challenges that arose while developing the applications

Candidates should also have the following:

Basic C/C++ competency including familiarity with variable types, loops, conditional statements, functions, and array manipulation.

Fundamentals of Accelerated Computing with CUDA Python

How your applications provide meaningful acceleration on a problem that could not be addressed as successfully in a CPU-only environment
The specifics of optimization strategies that the applications use
Specific CUDA-related technical challenges that arose while developing the applications

Candidates should also have the following:

Basic Python competency including familiarity with variable types, loops, conditional statements, functions, and array manipulations. Basic NumPy competency including familiarity ndarrays and ufuncs.

Scaling CUDA C++ Applications to Multiple Nodes

Instructor qualifications:
Candidates must have professional or academic experience developing CUDA C++ applications in the SPMD paradigm (MPI or SHEMEM derivatives) on compute clusters, and should be able to discuss their work on these applications in detail. In particular, candidates should be able to discuss:

The technologies used to scale their application to multiple nodes
Details about the compute cluster used to deploy their applications, including details about intra and inter-node networking
Inter-GPU and inter-node communication patterns required to successfully run the application
The reasoning behind communication patterns employed in the application

Candidates should have the following:

Instructors should have either experience, or the ability at least to discuss and describe, prototypical scientific applications such as a Jacobi solver or wave simulation.

Candidates must also demonstrate teaching experience, such as:

Classroom teaching experience - in person or remote
Significant presentation experience

Accelerating Data Engineering Pipelines

Instructor qualifications:
Candidates must demonstrate experience working on at least one ETL data pipeline, either in a professional or meaningful academic scenario, and explain their work. Qualifying experience includes:

A professional role (Ex: Data Engineer, Data Scientist, Business Analyst) on an ETL pipeline
A completed data dashboard project from data source to front end visualization
Academic coursework in Computer Based Information Systems or Database Systems

Candidates should have the following:

Experience using CuDF and CuPy
Experience with a dashboarding tool (Plotly, Matplotlib, Tableau)
Mastery and the ability to clearly explain MapReduce and DAG frameworks

Candidates must also demonstrate teaching experience, such as:

Classroom teaching experience
Significant presentation experience

Fundamentals of Accelerated Data Science with RAPIDS

Instructor qualifications:
Candidates must demonstrate significant experience with Data Science in Python and should be able to discuss about their previous work:

Specifics about all aspects of their end-to-end workflows, explaining their decisions, and speaking knowledgeably about tools and libraries used
The use of many DS/ML algorithms in their work, explaining their decisions
Extensive use of Python DS libraries like Pandas, NumPy, scikit-learn
Encouraged, previous work with Dask
Encouraged, previous work with or on RAPIDS

Candidates must also demonstrate teaching experience, such as:

Classroom teaching experience
Significant presentation experience

Enhancing Data Science Outcomes with Efficient Workflows

Instructor qualifications:
Candidates must demonstrate significant experience with Data Science in Python using distributed computing for large datasets and should be able to discuss about their previous work:

Specifics about all aspects of their end-to-end workflows, explaining their decisions, and speaking knowledgeably about tools and libraries used
The use of various data transformations applied on input data for model consumption
The use of various Machine Learning algorithms in their work, explaining their decisions
Extensive use of Python Data Science libraries like pandas, NumPy, scikit-learn, and xgboost
Previous work with or on RAPIDS and Dask
Recognition of the iterative nature of Data Science and appreciation of hardware acceleration for rapid experimentation

Candidates should have the following:

Python and common Data Science libraries like pandas, NumPy, scikit-learn, and xgboost
Proficiency with DataFrame manipulation
Familiarity with distributed computing using Dask
Familiarity with end-to-end machine learning workflow
Proficiency with various Machine Learning models, specifically those of tree-based variant
Proficiency with model performance metrics such as accuracy and inference performance
Familiarity with model tuning and its benefits
Knowledge of NVIDIA’s RAPIDS, NVTabular, and Triton Inference Server

Candidates must also demonstrate teaching experience, such as:

Classroom teaching experience
Significant presentation experience