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Veenit Ankam's Projects

amazon-sagemaker-examples icon amazon-sagemaker-examples

Example 📓 Jupyter notebooks that demonstrate how to build, train, and deploy machine learning models using 🧠 Amazon SageMaker.

aws-deep-learning-containers icon aws-deep-learning-containers

AWS Deep Learning Containers (DLCs) are a set of Docker images for training and serving models in TensorFlow, TensorFlow 2, PyTorch, and MXNet.

aws-eks-best-practices icon aws-eks-best-practices

A best practices guide for day 2 operations, including operational excellence, security, reliability, performance efficiency, and cost optimization.

aws-lambda-builders icon aws-lambda-builders

Python library to compile, build & package AWS Lambda functions for several runtimes & framework

aws-sdk-pandas icon aws-sdk-pandas

pandas on AWS - Easy integration with Athena, Glue, Redshift, Timestream, Neptune, OpenSearch, QuickSight, Chime, CloudWatchLogs, DynamoDB, EMR, SecretManager, PostgreSQL, MySQL, SQLServer and S3 (Parquet, CSV, JSON and EXCEL).

hands-on-llms icon hands-on-llms

🦖 𝗟𝗲𝗮𝗿𝗻 about 𝗟𝗟𝗠𝘀, 𝗟𝗟𝗠𝗢𝗽𝘀, and 𝘃𝗲𝗰𝘁𝗼𝗿 𝗗𝗕𝘀 for free by designing, training, and deploying a real-time financial advisor LLM system ~ 𝘴𝘰𝘶𝘳𝘤𝘦 𝘤𝘰𝘥𝘦 + 𝘷𝘪𝘥𝘦𝘰 & 𝘳𝘦𝘢𝘥𝘪𝘯𝘨 𝘮𝘢𝘵𝘦𝘳𝘪𝘢𝘭𝘴

llm-course icon llm-course

Course to get into Large Language Models (LLMs) with roadmaps and Colab notebooks.

machine-learning-interview icon machine-learning-interview

Machine Learning Interviews from FAANG, Snapchat, LinkedIn. I have offers from Snapchat, Coupang, Stitchfix etc. Blog: mlengineer.io.

ml-course-notes icon ml-course-notes

🎓 Sharing course notes on all topics related to machine learning, NLP, and AI.

ml-notebooks icon ml-notebooks

:fire: A series of code examples for all sorts of machine learning tasks and applications.

production-ml icon production-ml

A curated list of awesome open source libraries to deploy, monitor, version and scale your machine learning

python_computer_vision_from_scratch icon python_computer_vision_from_scratch

This repository explores the variety of techniques commonly used to analyze and interpret images. It also describes challenging real-world applications where vision is being successfully used, both for specialized applications such as medical imaging, and for fun, consumer-level tasks such as image editing and stitching, which students can apply to their own personal photos and videos.

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