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Hasib Rahman
Data & AI Transformation Leader | Data Architect | Fintech | IIoT | LLMOps | MLOps | Health Platform & Informatics | HL7 FHIR
Hasib Rahman is a prominent professional in the field of Data & AI, currently serving as the Head of Analytics Products at Trianz.12 Based in the San Francisco Bay Area, Rahman has established himself as a leader and strategist with extensive experience in data analytics and artificial intelligence.13
Professional Background
Rahman's career trajectory includes notable positions at several companies:
- Trianz: Currently holds the position of Head of Analytics Products2
- Deloitte: Previously worked here, contributing to his diverse experience2
- TechAnts: Another former employer that has shaped his professional journey2
Expertise and Skills
Hasib Rahman is known for his:
- Leadership in Data & AI
- Strategic thinking in analytics
- Extensive experience in data analytics
- Proficiency in artificial intelligence applications
LinkedIn Presence
Rahman maintains an active professional profile on LinkedIn with the username "hasibrahman".1 His LinkedIn profile showcases his role at Trianz and highlights his expertise in the data and analytics domain. With over 500 connections, he has established a significant professional network in his field.1
Role at Trianz
At Trianz, Rahman plays a crucial role in the Data and Analytics team.4 His position as Head of Analytics Products likely involves:
- Overseeing the development and implementation of analytics solutions
- Driving data-driven decision-making across the organization
- Leading initiatives related to data analytics and AI integration
- Collaborating with other teams to enhance the company's analytical capabilities
Hasib Rahman's expertise and leadership in analytics and AI make him a valuable asset to Trianz and a notable figure in the data analytics industry.
Highlights
In our industry, there are data "so-called experts" and hands-on data folks who solve complex issues and bring insights quickly. Naveen belongs to the latter group. When he pitched the idea of ChatWithCensus leveraging LLM-based technology, I immediately decided to join forces.
After months of hard work, debates, and perseverance, ChatWithCensus is live. So, what does it do?
ChatWithCensus simplifies engagement with ABS data. Users can ask questions in plain language, and our custom-built NLU engine converts these queries into SQL for rapid data retrieval. Because it is simple and doesn't require complex loading or SQL coding, we hope this becomes the go-to tool for smaller research organisations.
A huge thank you to those who made ChatWithCensus possible:
- Naveen Y., my partner in crime, Thanks, mate!
- Pere Martra, thank you for stepping in when we faced our first hurdle.
- Stephen Johansen your mentorship and generosity are invaluable.
- Belal Hossain, we wouldn't be here if it weren't for your late-night coding.
- Asher Hussain Thank you for SSL magic! Again, thank you!
- Prof. Jing and PhD student Haoyang Li, for their insights.
Finally, I would appreciate it if you try it out and share your feedback:
𝐋𝐋𝐌 𝐉𝐨𝐮𝐫𝐧𝐞𝐲 𝐭𝐨 𝐏𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐨𝐧: 𝐏𝐚𝐫𝐭 𝟏 - 𝐂𝐨𝐧𝐭𝐫𝐨𝐥𝐥𝐢𝐧𝐠 𝐎𝐩𝐞𝐧𝐀𝐈 𝐀𝐏𝐈 𝐄𝐱𝐩𝐞𝐧𝐬𝐞𝐬
Building an app with LLMs has been a wild ride—full of ups, downs, and loop-de-loops. Thankfully, you don’t tend to stay down long in 𝐋𝐋𝐌 development. The ecosystem is evolving at the speed of a caffeinated kangaroo, and there's always someone coming up with a clever new approach to the problems you’re wrestling with.
Truthfully, we're driving forward by collaborating with ML researchers, practitioners, academics, and even a few adventurous basement-dwelling ML hobbyists across the globe.
One of our first major challenges was the prohibitive 𝐜𝐨𝐬𝐭 𝐨𝐟 𝐫𝐮𝐧𝐧𝐢𝐧𝐠 𝐭𝐡𝐞 𝐎𝐩𝐞𝐧𝐀𝐈 𝐀𝐏𝐈. During our QA phase, it became clear that our operating costs would skyrocket once launched, primarily due to the input token count. While a shorter prompt might save costs, it also reduces accuracy.
Just when we hit a roadblock, a team of NLP researchers at Renmin University of China came to our rescue. We’ve been collaborating closely to develop the open-sourced 𝐋𝐋𝐌-𝐩𝐨𝐰𝐞𝐫𝐞𝐝 App.They introduced us to the 𝐒𝐜𝐡𝐞𝐦𝐚 𝐅𝐢𝐥𝐭𝐞𝐫 technique. Used in conjunction with supervised fine-tuning, this approach reduced the token count, which in turn significantly cut our costs.
𝐏𝐫𝐨𝐟. 𝐉𝐢𝐧𝐠 𝐙𝐇𝐀𝐍𝐆, thank you for enabling your Ph.D. students to collaborate with us, particularly 𝐇𝐚𝐨𝐲𝐚𝐧𝐠 𝐋𝐢. From patiently answering our numerous questions to meticulously reviewing our XLM-RoBERTa-XL write-up and expertly guiding us through the code base, Haoyang Li was simply outstanding. Haoyang Li, I have no doubt that your contributions to NLP will soon echo throughout the industry.
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Prof: Jing ZHANG https://lnkd.in/gH3-Qd9X
Haoyang Li https://lnkd.in/gYwAm7J4
Pere Martra Pedram Bidhendi Gerard Skybey Nick Golovin 🇺🇦 Samuel P. Naveen Y. Cameron Price Najah Naaji Eugene Dubossarsky Lee Rewell Giles Nunis Utsav Sinha Mohammadreza Pourreza Hammad Khan Dipankar Saha Raj Thakkar Tim Jelliffe Glenn Vanbavinckhove Ph.D. MBA Subashini Inderjeeth Asad J. Asher Hussain
