Podcast Title: Inside the ML Engineer’s Toolbox: MLOps & LLMOps Unveiled

Abstract: Join us in a dynamic discussion with two accomplished machine learning engineers from VoiceFlow. Together, these two experts will unravel the complexities of MLOps and LLMOps and shedding light on the tools, techniques, strategies, and emerging trends around GenAI and large language models (LLMs). Tune in to gain valuable insights from these experts and enhance your understanding of the machine learning engineer’s ever evolving toolbox.

About the Speakers:
Yoyo is a Machine Learning Engineer at Voiceflow. She works on building and maintaining Voiceflow’s large-scale real-time NLU that plays an integral part in powering the AI assistants for end users. Previously, she was the Lead Data Scientist at Toromont CAT. She has over 5 years of hands-on machine learning experience and has worked in an array of different industries including SaaS, heavy equipment, finance, and oil & gas.

Denys is the ML lead at Voiceflow focused on building the ML platform and data science offerings. His focus is on realtime NLP systems that help Voiceflow’s 60+ enterprise customers build better conversational assistants. His role alternates between product management, ML research and ML platform building. Previously he worked at large global bank as a senior cloud architect.

Podcast Title: Bytes and Baskets: Crafting Canada’s eCommerce Experience with MLOps Mastery

Abstract: Join us on “Bytes & Baskets” as we delve into the intricacies of building a machine learning platform for eCommerce experiences with some of Canada’s leading brands in online grocery shopping, beauty, pharmacy, and apparel. Uncover the challenges, triumphs, and innovations that shape the future of online retail through engaging discussions with Indrani Gorti.

The Stacked Up! series with Hien Luu, Head of ML Platform, DoorDash is a response to our MLOps World Committee’s concern about the lack of understanding and clarity around the tools, and ML stacks used across different ML teams, and application areas. As part of our mandate to give back to the ML community we’ve designed this series to be a non-commercial, open discussion around what tech companies use behind the scenes in order to help shed light for others working in the field.

There is no “one size fits all solution” or “perfect” stack. Often, core challenges centre around different factors; budget, data, models availability and a variety of other inhibitors specific to a given application area. The space can be ripe with options for solutions, but what works for some is completely unsuitable for others.

This is why conversations on the topic are important and helpful. What’s worked, what hasn’t, and how teams have landed on their current ML tech stack are important conversations that will help drive our ML community forward.

If this is something you’re interested in helping with – we look forward to hearing from you!