About Me


I am an Author, Speaker ,AI/ML and Cloud Evangelist.

Mona  Mona currently works as an AI/ML (Artificial Intelligence Machine learning) specialist in Google Public Sector. She was a Sr AI/ML specialist Solutions Architect at Amazon before joining Google. She has earned her masters in Computer Information Systems from Georgia State University. 

Mona Mona is a published author of books Natural Language Processing with AWS AI Services: Derive strategic insights from unstructured data with Amazon Textract and Amazon Comprehend and Google Cloud Certified Professional Machine Learning Study Guide. She has authored 19 blogs on AI/ML and cloud technology and a co-author on a research paper on CORD19 Neural Search which won an award for Best Research Paper at the prestigious AAAI (Association for the Advancement of Artificial Intelligence) conference.

Mona Mona is a member of SWE(Society for women engineers) and ACM(The Association for Computing Machinery) and she mentors early career graduates looking to pursue careers in AI and Cloud Computing.

She is also a frequent speaker at multiple conferences such as ISMB 2022, AWS Re:Invent 2020,2019,2018 and AWS DC Summit 


Publications 

Natural Language Processing with AWS AI Services: Derive strategic insights from unstructured data with Amazon Textract and Amazon Comprehend


Google Cloud Certified Professional Machine Learning Study Guide 1st Edition


AWS CORD-19 Search: A neural search engine for COVID-19 literature


https://cloud.google.com/blog/products/ai-machine-learning/rad-lab-alphafold-module-for-vertex-ai


https://aws.amazon.com/blogs/machine-learning/custom-document-annotation-for-extracting-named-entities-in-documents-using-amazon-comprehend/


https://aws.amazon.com/blogs/machine-learning/announcing-model-improvements-and-lower-annotation-limits-for-amazon-comprehend-custom-entity-recognition/


https://aws.amazon.com/blogs/machine-learning/segment-paragraphs-and-detect-insights-with-amazon-textract-and-amazon-comprehend/


https://aws.amazon.com/blogs/machine-learning/detect-abnormal-equipment-behavior-and-review-predictions-using-amazon-lookout-for-equipment-and-amazon-a2i/


https://aws.amazon.com/blogs/machine-learning/process-documents-containing-handwritten-tabular-content-using-amazon-textract-and-amazon-a2i/


https://aws.amazon.com/blogs/machine-learning/training-debugging-and-running-time-series-forecasting-models-with-the-gluonts-toolkit-on-amazon-sagemaker/


https://aws.amazon.com/blogs/machine-learning/identify-bottlenecks-improve-resource-utilization-and-reduce-ml-training-costs-with-the-new-profiling-feature-in-amazon-sagemaker-debugger/


https://aws.amazon.com/blogs/machine-learning/using-amazon-rekognition-custom-labels-and-amazon-a2i-for-detecting-pizza-slices-and-augmenting-predictions/


https://aws.amazon.com/blogs/machine-learning/setting-up-human-review-of-your-nlp-based-entity-recognition-models-with-amazon-sagemaker-ground-truth-amazon-comprehend-and-amazon-a2i/


https://aws.amazon.com/blogs/machine-learning/announcing-the-launch-of-amazon-comprehend-custom-entity-recognition-real-time-endpoints/


https://aws.amazon.com/blogs/machine-learning/deriving-conversational-insights-from-invoices-with-amazon-textract-amazon-comprehend-and-amazon-lex/


https://aws.amazon.com/blogs/machine-learning/ml-explainability-with-amazon-sagemaker-debugger/


https://aws.amazon.com/blogs/machine-learning/building-an-nlp-powered-search-index-with-amazon-textract-and-amazon-comprehend/