Talia Chopra
Software Engineer at Apple
San Diego, California, United States
Summary
🤩
Rockstar🎓
Top SchoolTalia Chopra is a software engineer and machine learning practitioner with eight years of experience building and productionizing high-impact ML models, currently working at Apple in San Diego. She has deep hands-on experience with AWS SageMaker from her time as an AWS ML/AI technical writer and as an ML engineer, including contributing documentation to high-profile repos like the SageMaker Python SDK and MXNet. Her background spans end-to-end model development—low-latency inference pipelines, hyperparameter tuning, batch transforms—and stakeholder-facing work such as Looker dashboards for model monitoring. She has applied ML across finance, fraud, and computer vision domains and has practical experience translating business requirements into measurable accuracy metrics. Talia pairs her technical skills with clear technical communication, having created notebooks, examples, and release notes that helped production adoption. Her prior roles in business development and financial risk bring uncommon cross-disciplinary perspective to ML productization and deployment.
8 years of coding experience
7 years of employment as a software developer
St. Paul's SchoolMaster's degree Computer Science, Master's degree Computer Science at Georgia Institute of TechnologyComputer Science, Computer Science at Foothill CollegeUniversity of California Santa Cruz
Github Skills (26)
restructuredtext10
learn-ruby-on-rails10
r10
user-manual10
mxnet10
machine-learning10
rs10
basics10
sagemaker10
amazon-sagemaker10
aws10
jupyter-notebook10
documentation10
data-science9
pytorch9
Programming languages (4)
Github contributions (5)
aws/amazon-sagemaker-examples
Jun 2020 - May 2021
Example 📓 Jupyter notebooks that demonstrate how to build, train, and deploy machine learning models using 🧠 Amazon SageMaker.
Role in this project:
ML Engineer
ML EngineerContributions:48 reviews, 20 commits, 39 PRs in 11 months
Contributions summary:Talia primarily worked on examples demonstrating machine learning model development, training, and deployment using Amazon SageMaker. Their contributions focused on modifying and refining Jupyter notebooks to showcase hyperparameter optimization, batch transformation, and model deployment, specifically within the context of the R programming language. The user made iterative edits to various notebooks, updating documentation, code snippets, and fixing errors to ensure the examples functioned correctly. They also contributed to improvements in 3D point cloud and other Ground Truth related notebooks.
pythonjupyter-notebooktrainingawssagemaker
aws/sagemaker-python-sdk
Jan 2020 - May 2021
A library for training and deploying machine learning models on Amazon SageMaker
Role in this project:Technical Writer
Technical WriterContributions:17 reviews, 19 commits, 31 PRs in 1 year 4 months
Contributions summary:Talia primarily contributed to the documentation of the SageMaker Python SDK, focusing on the SageMaker Distributed Model Parallel library. Their work involved adding and updating documentation for various APIs, including examples and release notes, and fixing documentation errors. They also made updates to the documentation concerning CUDA 11 requirements for SageMaker Distributed Data Parallel.
pytorchsagemakerdeployingmxnetpython
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