- Machine Learning TO (MLTO) Monthly MeetupOneEleven, Toronto, ON
Agenda
6:00-6:30 PM Arrivals and Networking
6:30-7:00 PM Tech Talk #1
7:00-7:30 PM Tech Talk #2
7:30-8:30 PM Networking & Wrap-up
8:30 PM Tear-down and DeparturesTech Talks
Talk #1 - Optimizing and Scaling Information Retrieval Pipelines - Abhi Anand, Data Scientist @ WattpadIn the age of AI-driven applications, information retrieval is the backbone of numerous systems, from Retrieval-Augmented Generation (RAG) applications to recommender systems that personalize user experiences. However, scaling these systems effectively while maintaining performance is a significant challenge many organizations struggle to overcome, often resulting in suboptimal performance and increased cost.
In this talk, Abhimanyu will share how the team at Wattpad tackled these challenges. We’ll explore the essential steps in an information retrieval pipeline, discuss the unique difficulties encountered at scale, and delve into the experiments the team at Wattpad conducted to optimize different components of the pipeline. This includes optimizing text-embedding generation at scale, improving their in-house vector database solution, and increasing the relevancy of recommendations using novel graph neural network architectures.
Talk #2 - Building Data Science Product & Teams in the Wild - Jan Scholz, Sr. Director of Data Science at Loblaw Companies Limited
This talk explores the challenges and strategies for building data science product and teams using machine learning (ML) at scale. By discussing challenges faced and lessons learned for structuring teams, integrating ML models with existing systems, and ensuring scalable, robust deployment, real-world examples of doing data science "in the wild" will highlight how to overcome obstacles like data silos, maintaining model performance in production, and providing actionable insights for business stakeholders and business decision making.
About the Speaker
Abhimanyu Anand is a Data Scientist at Wattpad, an online social storytelling platform, where he leads the development of recommender systems and NLP based applications for content recommendation. He holds an M.Sc. in Big Data Analytics from Trent University, with a specialization in natural language processing. He has developed and implemented robust AI solutions throughout his career across diverse domains, including internet-scale platforms, metals and mining, oil and gas, and e-commerce.Jan Scholz is the Senior Director of Data Science at Loblaw. He is passionate about helping organizations across industries build and scale high-performance ML and data science teams while developing transformative AI strategies, and designing and delivering custom, user-centered machine learning solutions using rapid development practices in the cloud. Jan has worked in machine learning in the consulting space since 2016 and holds a PhD in Neuroscience from Oxford.
Speak or Support MLTO!
We are constantly looking for new speakers and sponsors to share their knowledge and help build the AI community in the GTA! If you are interested please get in touch directly or apply via one of the links below:- Speaker Application Form: mlto.ca/speak
- Sponsor Inquiry Form: mlto.ca/sponsor
- Volunteer Application Form: mlto.ca/volunteer
- Machine Learning TO (MLTO) Monthly MeetupOneEleven, Toronto, ON
Agenda
6:00-6:30 PM Arrivals and Networking
6:30-7:00 PM Tech Talk #1
7:00-7:30 PM Tech Talk #2
7:30-8:30 PM Networking & Wrap-up
8:30 PM Tear-down and DeparturesTech Talks
Talk #1 - Three levels of Agent building - Deterministic, Workflows and Agentic - Denys Linkov, Head of ML @ VoiceflowUsing the right tools for the right job is an essential component of building products and software engineering. In this talk, we'll cover how to approach agent building along a spectrum of Agentic behaviour. We'll focus on tradeoffs in terms of latency, cost, time to build, accuracy and more.
Talk #2 - Generative AI and Agents on Vertex AI - Devang Sharma, Software Development Engineer @ Amazon
About the Speakers
Denys Linkov is the Head of Machine Learning at Voiceflow, ML Advisor and Linkedin Learning Course Instructor. He's worked with 50+ enterprises in their conversational AI journey, and his Gen AI courses have helped 150,000+ learners build key skills. He's worked across the AI product stack, being hands on building key ML systems, managing product delivery teams, and working directly with customers on best practices.Devang Sharma is a Senior Full Stack Developer with 7+ Years of hands-on experience in Analysis, Development, and Implementation with solid Programming expertise in - Java, Golang, Node.js, C#, Perl, Javascript, Typescript, ReactJs, Angular, PHP, C/C++, Python, Microservices and Distributed Services along with System Design and Object-Oriented Methodologies with a demonstrated history of working in the Internet Industry.
Speak or Support MLTO!
We are constantly looking for new speakers and sponsors to share their knowledge and help build the AI community in the GTA! If you are interested please get in touch directly or apply via one of the links below:- Speaker Application Form: mlto.ca/speak
- Sponsor Inquiry Form: mlto.ca/sponsor
- Volunteer Application Form: mlto.ca/volunteer
- Machine Learning TO (MLTO) Monthly MeetupOneEleven, Toronto, ON
Agenda
6:00-6:30 PM Arrivals and Networking
6:30-7:00 PM Tech Talk #1
7:00-7:30 PM Tech Talk #2
7:30-8:30 PM Networking & Wrap-up
8:30 PM Tear-down and DeparturesTech Talks
Talk #1 - TBDTalk #2 - TBD
About the Speakers
TBDSpeak or Support MLTO!
We are constantly looking for new speakers and sponsors to share their knowledge and help build the AI community in the GTA! If you are interested please get in touch directly or apply via one of the links below:- Speaker Application Form: mlto.ca/speak
- Sponsor Inquiry Form: mlto.ca/sponsor
- Volunteer Application Form: mlto.ca/volunteer
