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Walmart Global Tech Blog
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Achieve million-dollar savings with unified code and configuration-driven data pipelines
Achieve million-dollar savings with unified code and configuration-driven data pipelines
Coauthored by Guru Prakash and Chirag Goel
Keerthipriyan
Aug 6
Unknown powershell backdoor with ties to new Zloader
By: Jason Reaves and Joshua Platt
Jason Reaves
Jul 29
Using Predictive and Gen AI to Improve Product Categorization at Walmart
Using Predictive and Gen AI to Improve Product Categorization at Walmart
Lessons about improving performance, managing hallucination, and exception-handling for large scale AI models.
Adnan Hassan
Jul 19
Unveiling the Intricacies of Data Privacy: A Tale of Technology, Trust, and Transformation
Unveiling the Intricacies of Data Privacy: A Tale of Technology, Trust, and Transformation
Co-author : Govind Saria
Payal Choudhary
Jul 11
AI-Driven Continuous Monitoring: The Future of Third-Party Risk Management
AI-Driven Continuous Monitoring: The Future of Third-Party Risk Management
In the modern interconnected business landscape, third-party vendors play a crucial role in boosting efficiency and fostering innovation…
Emy Emmanuel
Jul 10
Deconstructing the Elastic Search normalizer and analyzer
Deconstructing the Elastic Search normalizer and analyzer
Elastic Search has gained enormous popularity in recent times. It has become the go to search engine due to its capabilities for…
Krishi Bisht
Jul 8
Build your own GPT (BYO-GPT)
Build your own GPT (BYO-GPT)
Building Question-Answering Chatbots on private Knowledge bases using RAG
Rajat Gupta
Jul 7
Textual Titans: A Large Language Model Odyssey
Textual Titans: A Large Language Model Odyssey
“It’s not who I am underneath, but what I do that defines me.” — Bruce Wayne
Rahul Bajaj
Jul 4
Evaluation of RAG Metrics using RAGA
Evaluation of RAG Metrics using RAGA
In the AI domain, Large Language Models (LLMs) are hogging the limelight. These innovative marvels serve as the intelligence core for…
Harika Samala
Jul 4
Deploying RAGs in production — Part 2
Deploying RAGs in production — Part 2
Authors: Chinmay Jain, Osheen Nayak
osheen nayak
Jul 3
Deploying RAGs in production — Part 1
Deploying RAGs in production — Part 1
Large Language Models (LLMs) are a significant advancement in the field of Artificial Intelligence. Their ability to understand and…
osheen nayak
Jul 2
Extracting Product Attributes from PDFs using PAE Framework
Extracting Product Attributes from PDFs using PAE Framework
Using LLMs to extract attributes from image and text descriptions with high accuracy, for use in product catalogs and assortment planning.
Apurva Sinha
Jun 28
Spectre (SPC) v9 Campaigns and Updates
Spectre (SPC) v9 Campaigns and Updates
By Jason Reaves and Joshua Platt
Jason Reaves
Jun 19
Reliably Processing Trillions of Kafka Messages Per Day
Reliably Processing Trillions of Kafka Messages Per Day
Authors: Vilas Athavale, Ravinder Matte, Sid Anand, Shrity Verma, Naresh Gopalani, Bhaven Avalani
Ravinder Matte
Jun 13
Which String Searching Technique is Best? A Comparison
Which String Searching Technique is Best? A Comparison
A presentation of three string searching methodologies
Nick Emerson
May 20
LLM Fine Tuning Series
LLM Fine Tuning Series
2nd in series: Reparameterization Tuning Theoretical
Shubhagyta Jayswal
May 16
Custom TensorFlow Lite model implementation in Android
Custom TensorFlow Lite model implementation in Android
Nowadays everyone is looking to empower their apps with machine learning. One way to add machine learning capabilities in mobile apps is by…
Dheeraj Kumar
May 16
ETL DataFlow — BigQuery to MS SQL
ETL DataFlow — BigQuery to MS SQL
Data moving from Bigquery to MS SQL
Baskar Gopal
May 16
How to Determine Causal Effects when A/B Tests are Infeasible through Adopter Analysis
How to Determine Causal Effects when A/B Tests are Infeasible through Adopter Analysis
Tags: #DataScience #CausalInference #DifferenceinDifference #Matching #MatchedDiD #PropensityScoreMatching
Avanti Chande
Apr 19
Doing Collaborative Research and Design in Emerging Tech to Innovate for Walmart’s Customers
Doing Collaborative Research and Design in Emerging Tech to Innovate for Walmart’s Customers
An Approach from Award-Winning Teams
Stephanie Gannon
Apr 12
Automated Input Data Distribution In a Multinode Workflow
Automated Input Data Distribution In a Multinode Workflow
Authors: Sandesh Balakrishna | Mohsin Kaleem
Sandesh Balakrishna
Apr 1
Empowering Women in Cybersecurity
Empowering Women in Cybersecurity
In March, we come together to celebrate Women’s History Month, a time dedicated to honoring the remarkable historical, cultural, political…
Neha Sherkhane
Mar 25
Resiliency in Spring Reactive Applications
Resiliency in Spring Reactive Applications
In the ever-evolving landscape of software development, the spotlight often shines on the functional features and capabilities of a new…
Manvendra Kumar
Mar 14
Transforming Text Classification with Semantic Search Techniques — Faiss
Transforming Text Classification with Semantic Search Techniques — Faiss
Classification models serve as supervised tools for organising documents into specific categories. Semantic search emerges as a practical…
Harika Samala
Mar 13
Building Walmart’s Seamless Communication: Leveraging Kafka’s Custom Partitioning
Building Walmart’s Seamless Communication: Leveraging Kafka’s Custom Partitioning
Thejasvini K S, Indranath Bardhan, Priyanka Sahoo and Communication Service Team
Rajesh Kumar Sahu
Mar 12
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