Media and entertainment companies serve multilingual audiences with a wide range of content catering to diverse audience segments. These enterprises have access to massive amounts of data collected over their many years of operations. Much of this data is unstructured text and images. Conventional approaches to analyzing unstructured data for generating new content rely on the use of keyword or synonym matching. These approaches don’t capture the full semantic context of a document, making them less effective for users’ search, content creation, and several other downstream tasks.
Text embeddings use machine learning (ML) capabilities to capture the essence of unstructured data. These embeddings are generated by language models that map natural language text into their numerical representations and, in the process, encode contextual information in the natural language document. Generating text embeddings is the first step to many natural language processing (NLP) applications powered by large language models (LLMs) such as Retrieval Augmented Generation (RAG), text generation, entity extraction, and several other downstream business processes.
Converting text to embeddings using cohere multilingual embedding model
Despite the rising popularity and capabilities of LLMs, the language most often used to converse with the LLM, often through a chat-like interface, is English. And although progress has been made in adapting open source models to comprehend and respond in Indian languages, such efforts fall short of the English language capabilities displayed among larger, state-of-the-art LLMs. This makes it difficult to adopt such models for RAG applications based on Indian languages.
In this post, we showcase a RAG application that can search and query across multiple Indian languages using the Cohere Embed – Multilingual model and Anthropic Claude 3 on Amazon Bedrock. This post focuses on Indian languages, but you can use the approach with other languages that are supported by the LLM.
Solution overview
We use the Flores dataset [1], a benchmark dataset for machine translation between English and low-resource languages. This also serves as a parallel corpus, which is a collection of texts that have been translated into one or more languages.
With the Flores dataset, we can demonstrate that the embeddings and, subsequently, the documents retrieved from the retriever, are relevant for the same question being asked in multiple languages. However, given the sparsity of the dataset (approximately 1,000 lines per language from more than 200 languages), the nature and number of questions that can be asked against the dataset is limited.
After you have downloaded the data, load the data into the pandas data frame for processing. For this demo, we are restricting ourselves to Bengali, Kannada, Malayalam, Tamil, Telugu, Hindi, Marathi, and English. If you are looking to adopt this approach for other languages, make sure the language is supported by both the embedding model and the LLM that’s being used in the RAG setup.
Load the data with the following code:
import pandas as pd
df_ben = pd.read_csv('./data/Flores/dev/dev.ben_Beng', sep='t')
df_kan = pd.read_csv('./data/Flores/dev/dev.kan_Knda', sep='t')
df_mal = pd.read_csv('./data/Flores/dev/dev.mal_Mlym', sep='t')
df_tam = pd.read_csv('./data/Flores/dev/dev.tam_Taml', sep='t')
df_tel = pd.read_csv('./data/Flores/dev/dev.tel_Telu', sep='t')
df_hin = pd.read_csv('./data/Flores/dev/dev.hin_Deva', sep='t')
df_mar = pd.read_csv('./data/Flores/dev/dev.mar_Deva', sep='t')
df_eng = pd.read_csv('./data/Flores/dev/dev.eng_Latn', sep='t')
# Choose fewer/more languages if needed
df_all_Langs = pd.concat([df_ben, df_kan, df_mal, df_tam, df_tel, df_hin, df_mar,df_eng], axis=1)
df_all_Langs.columns = ['Bengali', 'Kannada', 'Malayalam', 'Tamil', 'Telugu', 'Hindi', 'Marathi','English']
df_all_Langs.shape #(996,8)
df = df_all_Langs
stacked_df = df.stack().reset_index() # for ease of handling
# select only the required columns, rename them
stacked_df = stacked_df.iloc[:,[1,2]]
stacked_df.columns = ['language','text']
The Cohere multilingual embedding model
Cohere is a leading enterprise artificial intelligence (AI) platform that builds world-class LLMs and LLM-powered solutions that allow computers to search, capture meaning, and converse in text. They provide ease of use and strong security and privacy controls.
The Cohere Embed – Multilingual model generates vector representations of documents for over 100 languages and is available on Amazon Bedrock. With Amazon Bedrock, you can access the embedding model through an API call, which eliminates the need to manage the underlying infrastructure and makes sure sensitive information remains securely managed and protected.
The multilingual embedding model groups text with similar meanings by assigning them positions in the semantic vector space that are close to each other. Developers can process text in multiple languages without switching between different models. This makes processing more efficient and improves performance for multilingual applications.
Text embeddings turn unstructured data into a structured form. This allows you to objectively compare, dissect, and derive insights from all these documents. Cohere’s new embedding models have a new required input parameter, input_type, which must be set for every API call and include one of the following four values, which align towards the most frequent use cases for text embeddings:
- input_type=”search_document” – Use this for texts (documents) you want to store in your vector database
- input_type=”search_query” – Use this for search queries to find the most relevant documents in your vector database
- input_type=”classification” – Use this if you use the embeddings as input for a classification system
- input_type=”clustering” – Use this if you use the embeddings for text clustering
Using these input types provides the highest possible quality for the respective tasks. If you want to use the embeddings for multiple use cases, we recommend using input_type="search_document".
Prerequisites
To use the Claude 3 Sonnet LLM and the Cohere multilingual embeddings model on this dataset, ensure that you have access to the models in your AWS account under Amazon Bedrock, Model Access section and then proceed with installing the following packages. The following code has been tested to work with the Amazon SageMaker Data Science 3.0 Image, backed by an ml.t3.medium instance.
! apt-get update
! apt-get install build-essential -y # for the hnswlib package below
! pip install hnswlibCreate a search index
With all of the prerequisites in place, you can now convert the multilingual corpus into embeddings and store those in hnswlib, a header-only C++ Hierarchical Navigable Small Worlds (HNSW) implementation with Python bindings, insertions, and updates. HNSWLib is an in-memory vector store that can be saved to a file, which should be sufficient for the small dataset we are working with. Use the following code:
import hnswlib
import os
import json
import botocore
import boto3
boto3_bedrock = boto3.client('bedrock')
bedrock_runtime = boto3.client('bedrock-runtime')
# Create a search index
index = hnswlib.Index(space='ip', dim=1024)
index.init_index(max_elements=10000, ef_construction=512, M=64)
all_text = stacked_df['text'].to_list()
all_text_lang = stacked_df['language'].to_list()
Embed and index documents
To embed and store the small multilingual dataset, use the Cohere embed-multilingual-v3.0 model, which creates embeddings with 1,024 dimensions, using the Amazon Bedrock runtime API:
modelId="cohere.embed-multilingual-v3"
contentType= "application/json"
accept = "*/*"
df_chunk_size = 80
chunk_embeddings = []
for i in range(0,len(all_text), df_chunk_size):
chunk = all_text[i:i+df_chunk_size]
body=json.dumps(
{"texts":chunk,"input_type":"search_document"} # search documents
)
response = bedrock_runtime.invoke_model(body=body,
modelId=modelId,
accept=accept,
contentType=contentType)
response_body = json.loads(response.get('body').read())
index.add_items(response_body['embeddings'])
Verify that the embeddings work
To test the solution, write a function that takes a query as input, embeds it, and finds the top N documents most closely related to it:
# Retrieval of closest N docs to query
def retrieval(query, num_docs_to_return=10):
modelId="cohere.embed-multilingual-v3"
contentType= "application/json"
accept = "*/*"
body=json.dumps(
{"texts":[query],"input_type":"search_query"} # search query
)
response = bedrock_runtime.invoke_model(body=body,
modelId=modelId,
accept=accept,
contentType=contentType)
response_body = json.loads(response.get('body').read())
doc_ids = index.knn_query(response_body['embeddings'],
k=num_docs_to_return)[0][0]
print(f"Query: {query} n")
retrieved_docs = []
for doc_id in doc_ids:
# Append results
retrieved_docs.append(all_text[doc_id]) # original vernacular language docs
# Print results
print(f"Original Flores Text {all_text[doc_id]}")
print("-"*30)
print("END OF RESULTS nn")
return retrieved_docs
You can explore what the RAG stack does with a couple of queries in different languages, such as Hindi:
queries = [
"मुझे सिंधु नदी घाटी सभ्यता के बारे में बताइए","
]
# translation: tell me about Indus Valley Civilization
for query in queries:
retrieval(query)
The index returns documents relevant to the search query from across languages:
Query: मुझे सिंधु नदी घाटी सभ्यता के बारे में बताइए
Original Flores Text सिंधु घाटी सभ्यता उत्तर-पश्चिम भारतीय उपमहाद्वीप में कांस्य युग की सभ्यता थी जिसमें आस-पास के आधुनिक पाकिस्तान और उत्तर पश्चिम भारत और उत्तर-पूर्व अफ़गानिस्तान के कुछ क्षेत्र शामिल थे.
------------------------------
Original Flores Text सिंधु नदी के घाटों में पनपी सभ्यता के कारण यह इसके नाम पर बनी है.
------------------------------
Original Flores Text यद्यपि कुछ विद्वानों का अनुमान है कि चूंकि सभ्यता अब सूख चुकी सरस्वती नदी के घाटियों में विद्यमान थी, इसलिए इसे सिंधु-सरस्वती सभ्यता कहा जाना चाहिए, जबकि 1920 के दशक में हड़प्पा की पहली खुदाई के बाद से कुछ इसे हड़प्पा सभ्यता कहते हैं।
------------------------------
Original Flores Text సింధు నది పరీవాహక ప్రాంతాల్లో నాగరికత విలసిల్లింది.
------------------------------
Original Flores Text सिंधू संस्कृती ही वायव्य भारतीय उपखंडातील कांस्य युग संस्कृती होती ज्यामध्ये आधुनिक काळातील पाकिस्तान, वायव्य भारत आणि ईशान्य अफगाणिस्तानातील काही प्रदेशांचा समावेश होता.
------------------------------
Original Flores Text সিন্ধু সভ্যতা হল উত্তর-পশ্চিম ভারতীয় উপমহাদেশের একটি তাম্রযুগের সভ্যতা যা আধুনিক-পাকিস্তানের অধিকাংশ ও উত্তর-পশ্চিম ভারত এবং উত্তর-পূর্ব আফগানিস্তানের কিছু অঞ্চলকে ঘিরে রয়েছে।
-------------------------
.....
You can now use these documents retrieved from the index as context while calling the Anthropic Claude 3 Sonnet model on Amazon Bedrock. In production settings with datasets that are several orders of magnitude larger than the Flores dataset, we can make the search results from the index even more relevant by using Cohere’s Rerank models.
Use the system prompt to outline how you want the LLM to process your query:
# Retrieval of docs relevant to the query
def context_retrieval(query, num_docs_to_return=10):
modelId="cohere.embed-multilingual-v3"
contentType= "application/json"
accept = "*/*"
body=json.dumps(
{"texts":[query],"input_type":"search_query"} # search query
)
response = bedrock_runtime.invoke_model(body=body,
modelId=modelId,
accept=accept,
contentType=contentType)
response_body = json.loads(response.get('body').read())
doc_ids = index.knn_query(response_body['embeddings'],
k=num_docs_to_return)[0][0]
retrieved_docs = []
for doc_id in doc_ids:
retrieved_docs.append(all_text[doc_id])
return " ".join(retrieved_docs)
def query_rag_bedrock(query, model_id = 'anthropic.claude-3-sonnet-20240229-v1:0'):
system_prompt = '''
You are a helpful emphathetic multilingual assitant.
Identify the language of the user query, and respond to the user query in the same language.
For example
if the user query is in English your response will be in English,
if the user query is in Malayalam, your response will be in Malayalam,
if the user query is in Tamil, your response will be in Tamil
and so on...
if you cannot identify the language: Say you cannot idenitify the language
You will use only the data provided within the <context> </context> tags, that matches the user's query's language, to answer the user's query
If there is no data provided within the <context> </context> tags, Say that you do not have enough information to answer the question
Restrict your response to a paragraph of less than 400 words avoid bullet points
'''
max_tokens = 1000
messages = [{"role": "user", "content": f'''
query : {query}
<context>
{context_retrieval(query)}
</context>
'''}]
body=json.dumps(
{
"anthropic_version": "bedrock-2023-05-31",
"max_tokens": max_tokens,
"system": system_prompt,
"messages": messages
}
)
response = bedrock_runtime.invoke_model(body=body, modelId=model_id)
response_body = json.loads(response.get('body').read())
return response_body['content'][0]['text']
Let’s pass in the same query in multiple Indian languages:
queries = ["tell me about the indus river valley civilization",
"मुझे सिंधु नदी घाटी सभ्यता के बारे में बताइए",
"मला सिंधू नदीच्या संस्कृतीबद्दल सांगा",
"సింధు నది నాగరికత గురించి చెప్పండి",
"ಸಿಂಧೂ ನದಿ ಕಣಿವೆ ನಾಗರಿಕತೆಯ ಬಗ್ಗೆ ಹೇಳಿ",
"সিন্ধু নদী উপত্যকা সভ্যতা সম্পর্কে বলুন",
"சிந்து நதி பள்ளத்தாக்கு நாகரிகத்தைப் பற்றி சொல்",
"സിന്ധു നദീതാഴ്വര നാഗരികതയെക്കുറിച്ച് പറയുക"]
for query in queries:
print(query_rag_bedrock(query))
print('_'*20)
The query is in English, so I will respond in English.
The Indus Valley Civilization, also known as the Harappan Civilization, was a Bronze Age civilization that flourished in the northwestern regions of the Indian subcontinent, primarily in the basins of the Indus River and its tributaries. It encompassed parts of modern-day Pakistan, northwest India, and northeast Afghanistan. While some scholars suggest calling it the Indus-Sarasvati Civilization due to its presence in the now-dried-up Sarasvati River basin, the name "Indus Valley Civilization" is derived from its development along the Indus River valley. This ancient civilization dates back to around 3300–1300 BCE and was one of the earliest urban civilizations in the world. It was known for its well-planned cities, advanced drainage systems, and a writing system that has not yet been deciphered.
____________________
सिंधु घाटी सभ्यता एक प्राचीन नगर सभ्यता थी जो उत्तर-पश्चिम भारतीय उपमहाद्वीप में फैली हुई थी। यह लगभग 3300 से 1300 ईसा पूर्व की अवधि तक विकसित रही। इस सभ्यता के केंद्र वर्तमान पाकिस्तान के सिंध और पंजाब प्रांतों में स्थित थे, लेकिन इसके अवशेष भारत के राजस्थान, गुजरात, मध्य प्रदेश, महाराष्ट्र और उत्तर प्रदेश में भी मिले हैं। सभ्यता का नाम सिंधु नदी से लिया गया है क्योंकि इसके प्रमुख स्थल इस नदी के किनारे स्थित थे। हालांकि, कुछ विद्वानों का अनुमान है कि सरस्वती नदी के किनारे भी इस सभ्यता के स्थल विद्यमान थे इसलिए इसे सिंधु-सरस्वती सभ्यता भी कहा जाता है। यह एक महत्वपूर्ण शहरी समाज था जिसमें विकसित योजना बनाने की क्षमता, नगरीय संरचना और स्वच्छ जलापूर्ति आदि प्रमुख विशेषताएं थीं।
____________________
सिंधू संस्कृती म्हणजे सिंधू नदीच्या पट्टीकेतील प्राचीन संस्कृती होती. ही संस्कृती सुमारे ई.पू. ३३०० ते ई.पू. १३०० या कालखंडात फुलणारी होती. ती भारतातील कांस्ययुगीन संस्कृतींपैकी एक मोठी होती. या संस्कृतीचे अवशेष आजच्या पाकिस्तान, भारत आणि अफगाणिस्तानमध्ये आढळून आले आहेत. या संस्कृतीत नगररचना, नागरी सोयी सुविधांचा विकास झाला होता. जलवाहिनी, नगरदेवालय इत्यादी अद्भुत बाबी या संस्कृतीत होत्या. सिंधू संस्कृतीत लिपीसुद्धा विकसित झाली होती परंतु ती अजूनही वाचण्यास आलेली नाही. सिंधू संस्कृती ही भारतातील पहिली शहरी संस्कृती मानली जाते.
____________________
సింధు నది నాగరికత గురించి చెప్పుతూ, ఈ నాగరికత సింధు నది పరిసర ప్రాంతాల్లో ఉన్నదని చెప్పవచ్చు. దీనిని సింధు-సరస్వతి నాగరికత అనీ, హరప్ప నాగరికత అనీ కూడా పిలుస్తారు. ఇది ఉత్తర-ఆర్య భారతదేశం, ఆధునిక పాకిస్తాన్, ఉత్తర-పశ్చిమ భారతదేశం మరియు ఉత్తర-ఆర్థిక అఫ్గానిస్తాన్ కు చెందిన తామ్రయుగపు నాగరికత. సరస్వతి నది పరీవాహక ప్రాంతాల్లోనూ నాగరికత ఉందని కొందరు పండితులు అభిప్రాయపడ్డారు. దీని మొదటి స్థలాన్ని 1920లలో హరప్పాలో త్రవ్వారు. ఈ నాగరికతలో ప్రశస్తమైన బస్తీలు, నగరాలు, మలిచ్చి రంగులతో నిర్మించిన భవనాలు, పట్టణ నిర్మాణాలు ఉన్నాయి.
____________________
ಸಿಂಧೂ ಕಣಿವೆ ನಾಗರಿಕತೆಯು ವಾಯುವ್ಯ ಭಾರತದ ಉಪಖಂಡದಲ್ಲಿ ಕಂಚಿನ ಯುಗದ ನಾಗರಿಕತೆಯಾಗಿದ್ದು, ಪ್ರಾಚೀನ ಭಾರತದ ಇತಿಹಾಸದಲ್ಲಿ ಮುಖ್ಯವಾದ ಪಾತ್ರವನ್ನು ವಹಿಸಿದೆ. ಈ ನಾಗರಿಕತೆಯು ಆಧುನಿಕ-ದಿನದ ಪಾಕಿಸ್ತಾನ ಮತ್ತು ವಾಯುವ್ಯ ಭಾರತದ ಭೂಪ್ರದೇಶಗಳನ್ನು ಹಾಗೂ ಈಶಾನ್ಯ ಅಫ್ಘಾನಿಸ್ತಾನದ ಕೆಲವು ಪ್ರದೇಶಗಳನ್ನು ಒಳಗೊಂಡಿರುವುದರಿಂದ ಅದಕ್ಕೆ ಸಿಂಧೂ ನಾಗರಿಕತೆ ಎಂದು ಹೆಸರಿಸಲಾಗಿದೆ. ಸಿಂಧೂ ನದಿಯ ಪ್ರದೇಶಗಳಲ್ಲಿ ಈ ನಾಗರಿಕತೆಯು ವಿಕಸಿತಗೊಂಡಿದ್ದರಿಂದ ಅದಕ್ಕೆ ಸಿಂಧೂ ನಾಗರಿಕತೆ ಎಂದು ಹೆಸರಿಸಲಾಗಿದೆ. ಈಗ ಬತ್ತಿ ಹೋದ ಸರಸ್ವತಿ ನದಿಯ ಪ್ರದೇಶಗಳಲ್ಲಿ ಸಹ ನಾಗರೀಕತೆಯ ಅಸ್ತಿತ್ವವಿದ್ದಿರಬಹುದೆಂದು ಕೆಲವು ಪ್ರಾಜ್ಞರು ಶಂಕಿಸುತ್ತಾರೆ. ಆದ್ದರಿಂದ ಈ ನಾಗರಿಕತೆಯನ್ನು ಸಿಂಧೂ-ಸರಸ್ವತಿ ನಾಗರಿಕತೆ ಎಂದು ಸೂಕ್ತವಾಗಿ ಕರೆ
____________________
সিন্ধু নদী উপত্যকা সভ্যতা ছিল একটি প্রাচীন তাম্রযুগীয় সভ্যতা যা বর্তমান পাকিস্তান এবং উত্তর-পশ্চিম ভারত ও উত্তর-পূর্ব আফগানিস্তানের কিছু অঞ্চলকে নিয়ে গঠিত ছিল। এই সভ্যতার নাম সিন্ধু নদীর অববাহিকা অঞ্চলে এটির বিকাশের কারণে এরকম দেওয়া হয়েছে। কিছু পণ্ডিত মনে করেন যে সরস্বতী নদীর ভূমি-প্রদেশেও এই সভ্যতা বিদ্যমান ছিল, তাই এটিকে সিন্ধু-সরস্বতী সভ্যতা বলা উচিত। আবার কেউ কেউ এই সভ্যতাকে হরপ্পা পরবর্তী হরপ্পান সভ্যতা নামেও অবিহিত করেন। যাই হোক, সিন্ধু সভ্যতা ছিল প্রাচীন তাম্রযুগের এক উল্লেখযোগ্য সভ্যতা যা সিন্ধু নদী উপত্যকার এলাকায় বিকশিত হয়েছিল।
____________________
சிந்து நதிப் பள்ளத்தாக்கில் தோன்றிய நாகரிகம் சிந்து நாகரிகம் என்றழைக்கப்படுகிறது. சிந்து நதியின் படுகைகளில் இந்த நாகரிகம் மலர்ந்ததால் இப்பெயர் வழங்கப்பட்டது. ஆனால், தற்போது வறண்டுபோன சரஸ்வதி நதிப் பகுதியிலும் இந்நாகரிகம் இருந்திருக்கலாம் என சில அறிஞர்கள் கருதுவதால், சிந்து சரஸ்வதி நாகரிகம் என்று அழைக்கப்பட வேண்டும் என்று வாதிடுகின்றனர். மேலும், இந்நாகரிகத்தின் முதல் தளமான ஹரப்பாவின் பெயரால் ஹரப்பா நாகரிகம் என்றும் அழைக்கப்படுகிறது. இந்த நாகரிகம் வெண்கலயுக நாகரிகமாக கருதப்படுகிறது. இது தற்கால பாகிஸ்தானின் பெரும்பகுதி, வடமேற்கு இந்தியா மற்றும் வடகிழக்கு ஆப்கானிஸ்தானின் சில பகுதிகளை உள்ளடக்கியது.
____________________
സിന്ധു നദീതട സംസ്കാരം അഥവാ ഹാരപ്പൻ സംസ്കാരം ആധുനിക പാകിസ്ഥാൻ, വടക്ക് പടിഞ്ഞാറൻ ഇന്ത്യ, വടക്ക് കിഴക്കൻ അഫ്ഗാനിസ്ഥാൻ എന്നിവിടങ്ങളിൽ നിലനിന്ന ഒരു വെങ്കല യുഗ സംസ്കാരമായിരുന്നു. ഈ സംസ്കാരത്തിന്റെ അടിസ്ഥാനം സിന്ധു നദിയുടെ തടങ്ങളായതിനാലാണ് ഇതിന് സിന്ധു നദീതട സംസ്കാരം എന്ന പേര് ലഭിച്ചത്. ചില പണ്ഡിതർ ഇപ്പോൾ വറ്റിപ്പോയ സരസ്വതി നദിയുടെ തടങ്ങളിലും ഈ സംസ്കാരം നിലനിന്നിരുന്നതിനാൽ സിന്ധു-സരസ്വതി നദീതട സംസ്കാരമെന്ന് വിളിക്കുന്നത് ശരിയായിരിക്കുമെന്ന് അഭിപ്രായപ്പെടുന്നു. എന്നാൽ ചിലർ 1920കളിൽ ആദ്യമായി ഉത്ഖനനം നടത്തിയ ഹാരപ്പ എന്ന സ്ഥലത്തെ പേര് പ്രകാരം ഈ സംസ്കാരത്തെ ഹാരപ്പൻ സംസ്കാരമെന്ന് വിളിക്കുന്നു.
Conclusion
This post presented a walkthrough for using Cohere’s multilingual embedding model along with Anthropic Claude 3 Sonnet on Amazon Bedrock. In particular, we showed how the same question asked in multiple Indian languages, is getting answered using relevant documents retrieved from a vector store
Cohere’s multilingual embedding model supports over 100 languages. It removes the complexity of building applications that require working with a corpus of documents in different languages. The Cohere Embed model is trained to deliver results in real-world applications. It handles noisy data as inputs, adapts to complex RAG systems, and delivers cost-efficiency from its compression-aware training method.
Start building with Cohere’s multilingual embedding model and Anthropic Claude 3 Sonnet on Amazon Bedrock today.
References
[1] Flores Dataset: https://github.com/facebookresearch/flores/tree/main/flores200About the Author
Rony K Roy is a Sr. Specialist Solutions Architect, Specializing in AI/ML. Rony helps partners build AI/ML solutions on AWS.
Arash Sadrieh is the Co-Founder and Chief Science Officer at Ninjatech.ai. Arash co-founded Ninjatech.ai with a vision to make everyone more productive by taking care of time-consuming tasks with AI agents. This vision was shaped during his tenure as a Senior Applied Scientist at AWS, where he drove key research initiatives that significantly improved infrastructure efficiency over six years, earning him multiple patents for optimizing core infrastructure. His academic background includes a PhD in computer modeling and simulation, with collaborations with esteemed institutions such as Oxford University, Sydney University, and CSIRO. Prior to his industry tenure, Arash had a postdoctoral research tenure marked by publications in high-impact journals, including Nature Communications.
Tahir Azim is a Staff Software Engineer at NinjaTech. Tahir focuses on NinjaTech’s Inf2 and Trn1 based training and inference platforms, its unified gateway for accessing these platforms, and its RAG-based research skill. He previously worked at Amazon as a senior software engineer, building data-driven systems for optimal utilization of Amazon’s global Internet edge infrastructure, driving down cost, congestion and latency. Before moving to industry, Tahir earned an M.S. and Ph.D. in Computer Science from Stanford University, taught for three years as an assistant professor at NUST(Pakistan), and did a post-doc in fast data analytics systems at EPFL. Tahir has authored several publications presented at top-tier conferences such as VLDB, USENIX ATC, MobiCom and MobiHoc.
Tengfei Xue is an Applied Scientist at NinjaTech AI. His current research interests include natural language processing and multimodal learning, particularly using large language models and large multimodal models. Tengfei completed his PhD studies at the School of Computer Science, University of Sydney, where he focused on deep learning for healthcare using various modalities. He was also a visiting PhD candidate at the Laboratory of Mathematics in Imaging (LMI) at Harvard University, where he worked on 3D computer vision for complex geometric data.
Vasi Philomin is VP of Generative AI at AWS. He leads generative AI efforts, including Amazon Bedrock and Amazon Titan.
Anand Mandilwar is an Enterprise Solutions Architect at AWS. He works with enterprise customers helping customers innovate and transform their business in AWS. He is passionate about automation around Cloud operation , Infrastructure provisioning and Cloud Optimization. He also likes python programming. In his spare time, he enjoys honing his photography skill especially in Portrait and landscape area.
NagaBharathi Challa is a solutions architect in the US federal civilian team at Amazon Web Services (AWS). She works closely with customers to effectively use AWS services for their mission use cases, providing architectural best practices and guidance on a wide range of services. Outside of work, she enjoys spending time with family & spreading the power of meditation.
Vipul Parekh, is a senior customer solutions manager at AWS guiding our Capital Markets customers in accelerating their business transformation journey on Cloud. He is a GenAI ambassador and a member of AWS AI/ML technical field community. Prior to AWS, Vipul played various roles at the top investment banks, leading transformations spanning from front office to back-office, and regulatory compliance areas. 
Raj Talasila, is a senior technical program manager at AWS. He comes to AWS with 30+ years of experience in the Financial Services, Media and Entertainment, and CPG.
Saby Sahoo, is a senior solutions architect at AWS. Saby has 20+ years of experience in the field of design and implementation of IT Solutions, Data Analytics, and AI/ML/GenAI.
Sovik Kumar Nath is an AI/ML solution architect with AWS. He has extensive experience designing end-to-end machine learning and business analytics solutions in finance, operations, marketing, healthcare, supply chain management, and IoT. Sovik has published articles and holds a patent in ML model monitoring. He has double masters degrees from the University of South Florida, University of Fribourg, Switzerland, and a bachelors degree from the Indian Institute of Technology, Kharagpur. Outside of work, Sovik enjoys traveling, taking ferry rides, and watching movies.
Omar Ashton is a Partner Solutions Architect in Colombia. With 17 years of experience in software design and development, IT solution architecture, technology strategy and leadership of development teams.
Sovik Kumar Nath is an AI/ML and GenAI specialist senior solution architect with AWS working with financial services and capital markets customers. He has extensive experience designing end-to-end machine learning and business analytics solutions in finance, operations, marketing, healthcare, supply chain management, and IoT. Sovik has published articles and holds a patent in ML model monitoring. He has double masters degrees from the University of South Florida, University of Fribourg, Switzerland, and a bachelors degree from the Indian Institute of Technology, Kharagpur. Outside of work, Sovik enjoys traveling, taking ferry rides, and watching movies.
Jose Rojas is a Partner Solutions Architect at AWS. He helps Partners to increase productivity, efficiency and revenue by adopting and creating solutions on AWS. Before joining AWS, Jose worked at Cisco Meraki helping customers adopt cloud networking solutions. Outside work, he enjoys traveling with his family, swimming and cycling.
Mohan Musti is a Principal Technical Account Manger based out of Dallas. Mohan helps customers architect and optimize applications on AWS. Mohan has Computer Science and Engineering from JNT University, India. In his spare time, he enjoys spending time with his family and camping.
Jia (Vivian) Li is a Senior Solutions Architect in AWS, with specialization in AI/ML. She currently supports customers in financial industry. Prior to joining AWS in 2022, she had 7 years of experience supporting enterprise customers use AI/ML in the cloud to drive business results. Vivian has a BS from Peking University and a PhD from University of Southern California. In her spare time, she enjoys all the water activities, and hiking in the beautiful mountains in her home state, Colorado.
Uchenna Egbe is an AI/ML and GenAI specialist Solutions Architect who enjoys building reusable AIML solutions. Uchenna has an MS from the University of Alaska Fairbanks. He spends his free time researching about herbs, teas, superfoods, and how to incorporate them into his daily diet.
Joshua Broyde, PhD, is a Principal Solution Architect at AI21 Labs. He works with customers and AI21 partners across the generative AI value chain, including enabling generative AI at an enterprise level, using complex LLM workflows and chains for regulated and specialized environments, and using LLMs at scale.
Niithiyn Vijeaswaran is a Solutions Architect at AWS. His area of focus is generative AI and AWS AI Accelerators. He holds a Bachelor’s degree in Computer Science and Bioinformatics. Niithiyn works closely with the Generative AI GTM team to enable AWS customers on multiple fronts and accelerate their adoption of generative AI. He’s an avid fan of the Dallas Mavericks and enjoys collecting sneakers.
Emir Ayar is a Senior Tech Lead Solutions Architect with the AWS Prototyping team. He specializes in assisting customers with building ML and generative AI solutions, and implementing architectural best practices. He supports customers in experimenting with solution architectures to achieve their business objectives, emphasizing agile innovation and prototyping. He lives in Luxembourg and enjoys playing synthesizers.
Ziwen Ning is a software development engineer at AWS. He currently focuses on enhancing the AI/ML experience through the integration of AWS Neuron with containerized environments and Kubernetes. In his free time, he enjoys challenging himself with badminton, swimming and other various sports, and immersing himself in music.
Rohit Talluri is a Generative AI GTM Specialist (Tech BD) at Amazon Web Services (AWS). He is partnering with top generative AI model builders, strategic customers, key AI/ML partners, and AWS Service Teams to enable the next generation of artificial intelligence, machine learning, and accelerated computing on AWS. He was previously an Enterprise Solutions Architect, and the Global Solutions Lead for AWS Mergers & Acquisitions Advisory.
Albert Opher is a Solutions Architect Intern at AWS. He is a rising senior at the University of Pennsylvania pursuing Dual Bachelor’s Degrees in Computer Information Science and Business Analytics in the Jerome Fisher Management and Technology Program. He has experience with multiple programming languages, AWS cloud services, AI/ML technologies, product and operations management, pre and early seed start-up ventures, and corporate finance.
Geeta Gharpure is a senior software developer on the Annapurna ML engineering team. She is focused on running large scale AI/ML workloads on Kubernetes. She lives in Sunnyvale, CA and enjoys listening to audible in her free time
Harel Gal is a Solutions Architect at AWS, specializing in Generative AI and Machine Learning. He provides technical guidance and support across various segments, assisting customers in developing and managing AI solutions. In his spare time, Harel stays updated with the latest advancements in machine learning and AI. He is also an advocate for Responsible AI, an open-source software contributor, a pilot, and a musician.
Eitan Sela is a Generative AI and Machine Learning Specialist Solutions Architect at AWS. He works with AWS customers to provide guidance and technical assistance, helping them build and operate Generative AI and Machine Learning solutions on AWS. In his spare time, Eitan enjoys jogging and reading the latest machine learning articles.
Gili Nachum is a Principal AI/ML Specialist Solutions Architect who works as part of the EMEA Amazon Machine Learning team. Gili is passionate about the challenges of training deep learning models, and how machine learning is changing the world as we know it. In his spare time, Gili enjoy playing table tennis.
Mia C. Mayer is an Applied Scientist and ML educator at AWS Machine Learning University; where she researches and teaches safety, explainability and fairness of Machine Learning and AI systems. Throughout her career, Mia established several university outreach programs, acted as a guest lecturer and keynote speaker, and presented at numerous large learning conferences. She also helps internal teams and AWS customers get started on their responsible AI journey.