Shashank Prasanna

Developer Relations Leader AI/ML Technical Expert Developer Marketing Strategist Technology Educator

Hi!šŸ‘‹šŸ½ I’m Shashank — 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 (acquired by Qualcomm), and MathWorks, I’ve helped thousands of developers discover, evaluate, and 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 on the World Wide Developer Relations (WWDR) team, where I plan and lead AI/ML developer messaging and content strategy, coach engineering speakers to tell technical stories with clarity and impact at WWDC, define and execute targeted App developer engagement programs to drive adoption among top-priority app developers on the App Store.

I’m a hands-on AI/ML DevRel strategist who works across the developer lifecycle: awareness, technical consideration, evaluation, and adoption. My work combines developer messaging, technical education, live programming, workshops, partner programs, and direct architectural guidance.

Developer Marketing Leadership

15+ years building AI/ML developer programs across Apple, NVIDIA, AWS, Meta, Modular AI, and MathWorks.

Owned developer marketing programs, coached engineering leaders presentations, hired and led contributors, and guided partner programs with Intel, Meta, Microsoft, NVIDIA, and AWS.

Apple Developer Adoption

Led AI/ML developer messaging, content strategy, and adoption programs for Apple developer audiences.

Guided WWDC content with 440k+ views, delivered Apple talks and code-alongs with 265k+ views, and supported adoption across 20-30 priority App Store apps plus indie developers.

Technical Authority at Scale

Delivered 100+ technical talks, workshops, livestreams, and articles across AI infrastructure, open-source ML frameworks, on-device AI, and accelerated computing.

Published deep technical articles with 150k+ and 50k+ views, and ran AI/ML workshops and bootcamps often reaching 300-500 live attendees.

Developer Adoption Journey

  • Awareness: WWDC developer sessions, launch content, livestreams, technical blogs, partner programs, and community events for new AI/ML platforms and tools.
  • Consideration: code-alongs, workshops, demos, benchmark-driven content, PyTorch and Mojo deep dives, and direct conversations with technical decision makers.
  • Evaluation: private workshops, architecture reviews, hands-on guidance, and feedback loops with product and engineering teams.
  • Adoption: targeted support for priority developers, last-mile unblocking, performance and shipping readiness assessment.

I believe the best developer programs are built on:

  1. The highest-quality technical education that reduces developer friction, no compromise.
  2. Tight feedback loops with developers and a relentless focus on developer productivity.
  3. Open ecosystem, community connection and sharing.

When I’m not writing, reading, or sharing what I learn, you’ll find me running šŸƒ local streets and trails, testing the latest running shoes, reading running books, or brewing the perfect cup of coffee ā˜•ļø.

Beyond strategy, I am a hands-on technical educator, known for delivering high-quality and deeply technical talks, running hands-on workshops, consulting developers on AI/ML best practices, writing developer messaging, and launching developer-first products that make advanced AI/ML frameworks, libraries and tools more accessible. I’ve built global developer engagement strategies and coached speakers for major AI/ML events such as WWDC, GTC and re:Invent.

I’ve spoken at events, conferences and meetups across the globe, including NVIDIA GTC AWS re:Invent Apple Developer Center Events Open Data Science Conference (ODSC) Docker events Future Technologies Conference (FTC) O’Reilly AI AWS re:MARS AWS Summits AWS Innovate Dev Days Collision Conference AI Accelerator Conf. University & Community Meetups and at numerous community meetups and university events. You’ll find many of those talks, workshops, YouTube videos and published articles below.

ML, PyTorch, MojošŸ”„ and Open-source

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 MojošŸ”„ for Python programmers
Implementing NumPy style matrix slicing in MojošŸ”„
How to setup a MojošŸ”„ development environment with Docker containers
Introduction to Tensors in MojošŸ”„
Using the Mojo šŸ”„ Visual Studio Extension šŸš€
Using MojošŸ”„ with Docker containers
Getting started with the Mojo SDKšŸ”„
Speeding up Python code with MojošŸ”„: Mandelbrot example
What’s 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
What’s 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
What’s 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 Valentine’s Day Using Mojo and Python
What Is Loop Unrolling? How You Can Speed Up Mojo
Mojo SDK v0.7 Now Available for Download

LivestreamsšŸŽ„ & WorkshopsšŸ‘©ā€šŸ’»

 Apple livestreams
Code along with the Foundation Models framework | Meet with Apple

šŸŽ„ MojošŸ”„ 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 - MAXāš”ļøDeveloper Edition!
Modular Community Livestream - MojošŸ”„ SDK v0.7 edition!
Modular Community Livestream – ModCon recap + Q&A
Modular Community Livestream - MojošŸ”„ SDK v0.5 edition!
Modular Community Livestream - MojošŸ”„ on Mac
Modular Community Livestream - MojošŸ”„ SDK
Modular Community Q&A Livestream

šŸŽ„ 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

šŸ› ļø Workshops
A Tour of PyTorch 2.0
PyTorch Distributed Training on AWS

šŸ“£ Announcements & Company News
Key announcements from ModCon 2023
What’s new in Mojo SDK v0.5?
MojošŸ”„ 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!