In this episode, we dive into the world of data analytics and artificial intelligence with Ryan, the CEO, and Paul, the CTO of Zenlytic. Having graduated from Harvard and with extensive backgrounds in venture capital, consulting, and data engineering, Ryan and Paul provide valuable insights into their journey of building Zenlytic, a cutting-edge analytics platform.
Join us as we explore how Zenlytic's natural language interface enhances user experiences, enabling seamless access and analysis of analytics data. Discover how their self-service platform empowers teams to leverage business intelligence effectively, and learn about the unique features that set Zenlytic apart from other analytics platforms in the market. Delve into the crucial aspects of data security and privacy while granting team access, and find out how Zenlytic's analytics capabilities have transformed companies into data-driven decision-makers, ultimately improving their performance.
Test-First machine learning (Ep. 115)
GPT-3 cannot code (and never will) (Ep. 114)
Make Stochastic Gradient Descent Fast Again (Ep. 113)
What data transformation library should I use? Pandas vs Dask vs Ray vs Modin vs Rapids (Ep. 112)
[RB] It’s cold outside. Let’s speak about AI winter (Ep. 111)
Rust and machine learning #4: practical tools (Ep. 110)
Rust and machine learning #3 with Alec Mocatta (Ep. 109)
Rust and machine learning #2 with Luca Palmieri (Ep. 108)
Rust and machine learning #1 (Ep. 107)
Protecting workers with artificial intelligence (with Sandeep Pandya CEO Everguard.ai)(Ep. 106)
Compressing deep learning models: rewinding (Ep.105)
Compressing deep learning models: distillation (Ep.104)
Pandemics and the risks of collecting data (Ep. 103)
Why average can get your predictions very wrong (ep. 102)
Activate deep learning neurons faster with Dynamic RELU (ep. 101)
WARNING!! Neural networks can memorize secrets (ep. 100)
Attacks to machine learning model: inferring ownership of training data (Ep. 99)
Don't be naive with data anonymization (Ep. 98)
Why sharing real data is dangerous (Ep. 97)
Building reproducible machine learning in production (Ep. 96)
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