Shashank Prasanna
Developer Relations Leader | AI/ML Technical Expert | Developer Marketing StrategistDocendo discimus (by teaching, we learn)
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Hi!šš½ Iām Shashank ā I build, grow, and inspire AI/ML developer communities around the world.
With over 15 years leading developer relations and marketing programs at Apple, NVIDIA, AWS, Meta, Modular AI and MathWorks, Iāve helped thousands of developers adopt new AI/ML frameworks, AI cloud services and infrastructure, on-device AI frameworks, and AI accelerators for training and inference.
I currently work at Apple as an AI/ML Technologies Evangelist. Here, I plan and lead the AI/ML developer content at WWDC, shape messaging for new frameworks and APIs, and coach speakers to tell their stories with clarity and impact. I also educate app developers on how to create groundbreaking AI/ML-driven apps on Apple platforms.
Iām a hands-on educator and AI/ML technologies evangelist ā known for delivering deeply technical talks, running hands-on workshops, writing clear developer messaging, and launching developer-first products that make advanced AI/ML frameworks, libraries and tools more accessible. From planning global developer engagement strategies to coaching speakers and producing content for major events such as WWDC, GTC and re:Invent my focus is simple: help developers succeed, and build communities that last.
I believe the best developer programs are built on three things: (1) the highest-quality technical education, (2) a relentless focus on developer productivity, and (3) honest, consistent community connection and sharing.
When Iām not writing, reading, or sharing what I learn, youāll find me running š local streets and trails, testing the latest running shoes, reading running books, or brewing the perfect cup of coffee āļø.
Featured Talks, Publications and Videos
Iāve spoken at events, conferences and meetups across the globe, including NVIDIA GTC
O’Reilly AI
Open Data Science Conference (ODSC)
Docker events
Future Technologies Conference (FTC)
AWS re:Invent
AWS re:MARS
AWS Summits
AWS Innovate
Dev Days
Collision Conference
AI Accelerator Conf.
Apple Developer Center Events
University & Community Meetups
and at numerous community meetups and university events.
Youāll find many of those talks, workshops, YouTube videos and published articles below.
Choosing the right GPU for deep learning on AWS (š„150k+ views)
A complete guide to AI accelerators for deep learning inference (š„50k+ views)
AI accelerators, machine learning algorithms and their co-design and evolution
How Docker Runs Machine Learning on NVIDIA GPUs, AWS Inferentia, and Other Hardware AI Accelerators
Deploying GPU-Optimized Machine Learning Models to the Cloud and the Edge
TensorRT 3: Faster TensorFlow Inference and Volta Support
Production DeepĀ LearningĀ with NVIDIA GPU Inference Engine
DeepĀ LearningĀ for Computer Vision with MATLAB and cuDNN
DeepĀ LearningĀ for Object Detection with DIGITS
GTC: A Developerās Guide to Choosing the Right GPUs for Deep Learning
GTC: A Developerās Guide to Improving GPU Utilization and Reducing Deep Learning Costs
GTC: Improve ML Training Performance with Amazon SageMaker Debugger
Accelerating Data Science with NVIDIA RAPIDS on AWS
GTC: GPU-Accelerated Deep Learning at Scale with TensorFlow, PyTorch, and MXNet in the Cloud
Why use Docker containers for machine learning development?
Introducing Amazon SageMaker Components for Kubeflow Pipelines
A quick guide to distributed training with TensorFlow and Horovod
Choose the right instance for inference deployment with SageMaker Inference Recommender
Speeding up deep learning training with SageMaker Training Compiler
Introduction to Amazon SageMaker Serverless Inference | Concepts & Code examples
Get access to FREE GPU-powered JupyterLab based IDE for ML with Amazon SageMaker Studio Lab
Machine Learning with Containers and Amazon SageMaker
Keynote: How machine learning is making customer experience more human
Using Containers for Deep Learning Workflows
Train Deep Learning Models on GPUs using Amazon EC2 Spot Instances (outdated approach)
7 things you should know about AI & Machine Learning launches at re:invent 2021
How Pytorch 2.0 accelerates deep learning with operator fusion and CPU/GPU code-generation
Machine learning with AutoGluon, an open source AutoML library
Introduction to TorchServe, an open-source model serving library for PyTorch
Deploying PyTorch models for inference at scale using TorchServe
A quick guide to managing machine learning experiments
How to scale machine learning experiments
How to debug machine learning models to catch issues early and often
An easy introduction to Mojoš„ for Python programmers
Implementing NumPy style matrix slicing in Mojoš„
How to setup a Mojoš„ development environment with Docker containers
Introduction to Tensors in Mojoš„
Using the Mojo š„ Visual Studio Extension š
Using Mojoš„ with Docker containers
Getting started with the Mojo SDKš„
Speeding up Python code with Mojoš„: Mandelbrot example
Whatās New in Mojo 24.4: Improved Collections, New Traits, OS Module Features, and Core Language Enhancements
Fast K-Means Clustering in Mojo: Guide to Porting Python to Mojo for Accelerated K-Means Clustering
Whatās New in Mojo 24.3: Community Contributions, Pythonic Collections, and Core Language Enhancements
Row-Major vs Column-Major Matrices: A Performance Analysis in Mojo and NumPy
Whatās New in Mojo 24.2: Mojo Nightly, Enhanced Python Interop, OSS Stdlib, and More
Deploying MAX on Amazon SageMaker
Mojo Pi: Approximating Pi with Mojo Using Monte Carlo Methods
Evaluating MAX Engine Inference Accuracy on the ImageNet Dataset
Optimize and Deploy AI Models with MAX Engine and MAX Serving
Getting Started with MAX Developer Edition
What Are Dunder Methods? A Guide in Mojo
Mojo & Python: Calculating and Plotting a Valentineās Day Using Mojo and Python
What Is Loop Unrolling? How You Can Speed Up Mojo
Mojo SDK v0.7 Now Available for Download
š„ Mojoš„ livestreams
Modular Community Livestream - New in MAX 24.4
Modular Community Livestream - New in MAX 24.3
Modular Community Livestream - New in MAX 24.2
Modular Community Livestream - MAXā”ļøDeveloper Edition!
Modular Community Livestream - Mojoš„ SDK v0.7 edition!
Modular Community Livestream – ModCon recap + Q&A
Modular Community Livestream - Mojoš„ SDK v0.5 edition!
Modular Community Livestream - Mojoš„ on Mac
Modular Community Livestream - Mojoš„ SDK
Modular Community Q&A Livestream
š„ PyTorch livestreams
PyTorch 2.0 Live Q&A Series: PyTorch 2.0 Export
PyTorch 2.0 Live Q&A Series: A Deep Dive on TorchDynamo
PyTorch 2.0 Q&A: Deep Dive into TorchInductor and PT2 Backend Integration
PyTorch 2.0 Q&A: Optimizing Transformers for Inference
PyTorch 2.0 Q&A: Dynamic Shapes and Calculating Maximum Batch Size
PyTorch 2.0 Q&A: TorchRL
2-D Parallelism using DistributedTensor and PyTorch DistributedTensor
š ļø Workshops
A Tour of PyTorch 2.0
PyTorch Distributed Training on AWS
š£ Announcements & Company News
Key announcements from ModCon 2023
What’s new in Mojo SDK v0.5?
Mojoš„ is now available on Mac
What’s the difference between the AI Engine and Mojo?
Modular to bring NVIDIA Accelerated Computing to the MAX Platform
Modular partners with Amazon Web Services (AWS) to bring MAX Enterprise Edition exclusively to AWS services
ModCon 2023 sessions you don’t want to miss!