Why Emotion Matters More Than Sound

MLOps.community · Intermediate ·📊 Data Analytics & Business Intelligence ·3mo ago
// Abstract Voice AI isn’t about words — it’s about emotion. Anoop Dawar and Ajeet Grewal explore how to build systems that don’t just speak, but feel — and why that might be the next frontier in human-computer interaction. // Bio Anoop Dawar As Chief Strategy Officer, Anoop brings extensive experience in driving strategic growth and transformation at leading technology companies. With a proven track record of success at GitLab, Meta, Aerohive (Acq: EXTR), MapR (Acq: HPE), and Cisco, Anoop specializes in scaling startups from inception to IPO and beyond. His expertise spans product management, pricing, operations, marketing, and engineering, with a focus on building solutions for enterprises and SMBs across AI, analytics, data, SaaS, security, open source, and infrastructure. Anoop's strategic vision and leadership have been instrumental in guiding companies through pivotal milestones, including IPOs and acquisitions. He holds an MBA from the Wharton School of Business, a master's in computer science from the University of Texas at Austin. Ajeet Grewal Ajeet Grewal leads engineering for Voice AI at Sierra. Previously, they co-founded and served as CEO of Receptive AI, a voice-tech startup for SMBs that was acquired by Sierra for its technology and team. Before that, they led the Search and Recommendations group at Twitter, helping drive significant user engagement and partnering with Elon Musk and the transition team to transform Twitter into X while delivering major initiatives on schedule.
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Our 1st MLOps Meetup // Luke Marsden // MLOps Meetup #1
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2 Remote Collaboration as a Data Scientist
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3 MLOps Manifesto with Luke Marsden from Dotscience
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6 Life purpose and too many spreadsheets
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7 Explainability, Black boxes and EU white paper on reproducibility
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8 Hierarchy of Machine Learning Needs // Phil Winder // MLOps Meetup #3
Hierarchy of Machine Learning Needs // Phil Winder // MLOps Meetup #3
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9 Automatically Retrain Machine Learning Models? Are best practices worth it?
Automatically Retrain Machine Learning Models? Are best practices worth it?
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10 Building an MLOps Team? Key ideas to keep in mind
Building an MLOps Team? Key ideas to keep in mind
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11 Hierarchy of MLOps Needs
Hierarchy of MLOps Needs
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12 Bare necessities for getting an ML model into production
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13 MLOps and Monitoring
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14 How Phil Winder got into Data Science and Software Engineering
How Phil Winder got into Data Science and Software Engineering
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15 Provenance and Reproducibility in Machine Learning; what is it and why you need it?
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16 Friction Between Data Scientists and Software Engineers
Friction Between Data Scientists and Software Engineers
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17 MLOps Problems in different size companies
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18 ML tooling in large companies
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19 ML Platforms - The build vs buy question
ML Platforms - The build vs buy question
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20 ML Services Gateway at SurveyMonkey
ML Services Gateway at SurveyMonkey
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21 Message buses, Async and sync architecture
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22 MLOps #4: Shubhi Jain - Building an ML Platform @SurveyMonkey
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23 Hybrid Data Science Teams @SurveyMonkey
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25 Doing ML with Personal Information
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26 Evolution of the ML feature store @SurveyMonkey
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27 Developing a Machine Learning Feature Store
Developing a Machine Learning Feature Store
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28 Auto retrain ML models is not the question
Auto retrain ML models is not the question
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29 3 key parts to Machine Learning monitoring
3 key parts to Machine Learning monitoring
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30 MLOps Meetup #6: Mid-Scale Production Feature Engineering with Dr. Venkata Pingali
MLOps Meetup #6: Mid-Scale Production Feature Engineering with Dr. Venkata Pingali
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31 MLOps meetup #5 High Stakes ML: Active Failures, Latent Factors with Flavio Clesio
MLOps meetup #5 High Stakes ML: Active Failures, Latent Factors with Flavio Clesio
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32 MLOps: Airflow Pros and Cons
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33 Specific challenges in Machine Learning
Specific challenges in Machine Learning
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34 Current State Of Machine Learning
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35 Humans in the Loop are a defining factor in Machine Learning
Humans in the Loop are a defining factor in Machine Learning
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36 Learning from real life Machine Learning failures
Learning from real life Machine Learning failures
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37 Survivorship Bias in machine learning tutorials
Survivorship Bias in machine learning tutorials
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38 Swiss Cheese model in Machine Learning
Swiss Cheese model in Machine Learning
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39 Resume driven development in Machine learning & software engineering
Resume driven development in Machine learning & software engineering
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40 Who has the highest standards in ML?
Who has the highest standards in ML?
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41 Venkata Pingali of Scribble Data Thoughts on the Current State of Machine Learning
Venkata Pingali of Scribble Data Thoughts on the Current State of Machine Learning
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42 Dependable data and being able to Trust in your Data with Venkata Pengali of Scribble Data
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43 Speed, Trust, Evolution and Scale in MLOps
Speed, Trust, Evolution and Scale in MLOps
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44 More difficult transition for data scientists to become ML engineers
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46 Deeper thinking from data scientists around platform blackholes
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47 Checkpointing, metadata, and confidence in your data
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48 Adjacent usecases and multistep feature engineering
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49 Standardization of Machine Learning tools like in Software Engineering with Venkata Pingali
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50 Reproducability flaws in end to end Machine Learning debugging
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51 3rd wave of data scientists
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52 MLOps meetup #7 Alex Spanos // TrueLayer 's MLOps Pipeline
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53 MLOps Meetup #8 Optimizing Your ML Workflow with Kubeflow 1.0
MLOps Meetup #8 Optimizing Your ML Workflow with Kubeflow 1.0
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54 Are Kubeflow and Airflow complementary?
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55 Why Kubeflow gained so much traction=open community
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