Announcing Part 2 of our 10-year Anniversary Celebration! This time we are doing it at Google Developer Space! Details below!
Agenda
6.30pm - 7.00pm: Registration, refreshments and networking
7.00pm - 7.10pm: Opening from Google Developer Space & DataScience SG
7.10pm - 7.50pm: BQML : Where GenAI meets Analytics
7.50pm - 8.40pm: How to easily scale your analysis, machine learning and AI tasks serverlessly in a Pythonic way!
8.40pm - 8.45pm: Q&A and closing
Synopsis
BQML: Where GenAI meets Analytics
Want to do generative AI without writing much code, meeting your data where it is, only with SQL queries and without having to worry about security, privacy and compliance issues? Join us to know how BQML (BigQuery ML) helps you perform analytics on data stored in BigQuery using large language models (LLM) from Vertex AI.
How to easily scale your analysis, machine learning and AI tasks serverlessly in a Pythonic way!
In this talk, we will discuss how you can leverage a serverless data platform like BigQuery as a backend for your data task: from analysis to AI/ML using a new Python library called BigFrames (BigQuery DataFrames). BigFrames provide a pandas-compatible API for analytics and scikit-learn-like API for ML; not to mention remote function capabilities for endpoint integration and also (upcoming) LangChain integration.
The key points that will be covered in this talk, include:
- Sharing what the speaker sees and learns from the field (state of data engineer, analyst and scientist), especially here in the SEA region.
- How the speaker sees that users love to use Python as their primary data tool (e.g., pandas, sklearn, etc.), but mostly having an issue on the scalability part. E.g., need to have a bigger (virtual) machine to run a notebook with a big dataframe, need to use and manage a distributed data platform to process a big dataset, need to utilize a specific accelerator for training and inference, etc.
- Sharing how BigFrames can be one of the alternatives for infrastructure abstraction, and why it can help you to focus on what you, an analyst and scientist, can do best. And of course in a Pythonic way!
Speaker Bio(s):
Darshak Makadiya
Customer Engineer, Data Analytics, Google Cloud
Darshak is the Data Analytics Customer Engineer at Google Cloud.
He has vast experience helping large organizations across industries to generate impactful business outcomes by leveraging cloud and modern Data Analytics platforms.
Johanes Alexander
Customer Engineer, Data Analytics Specialist, Google Cloud
Johanes is an experienced data architect, designing an end to end data solution to fulfill business needs. Johanes works with strategic customers across the Southeast Asia region, both digital natives and enterprises, to help them uncover the business value from their own data.
After graduating from Telkom University as Bachelor of Computer Engineering in 2012, Johanes spent the first 7 years of his work experience centered on various engineering/lead architect roles for product, data to machine learning on XL Axiata and Gojek (now GoTo Group). During that time Johanes also pursued a Master of Business Administration at Bandung Institute of Technology, to balance his engineering and business knowledge.
Johanes realized the value of cloud computing during his work and decided to join the hyperscalers to help others realize the opportunities given by cloud technologies. Johanes joined Microsoft Indonesia as a Cloud Solution Architect, Data and AI for almost two years before moving to Google Cloud Asia Pacific as a Customer Engineer, Data Analytics Specialist.
