Infinite Curiosity Pod with Prateek Joshi

Harpreet Sahota is the host of The Artists of Data Science podcast. He's a data science generalist with a strong business acumen. He currently works on Developer Relations at Pachyderm where he is defining and executing strategies that demonstrate the value of data. In this episode, we cover a range of topics including:- Building bottom up ML products- Driving product adoption- Building the DevRel function at a startup- What makes an online community great- How to create useful content- Understanding the MLOps ecosystem- Building a personal brand

Brent Dykes is the founder of Analytics Hero where he offers workshops on data storytelling. As a Forbes contributor, he has authored more than 45 articles and spoken at some of the largest industry conferences around the globe. After publishing two books on digital analytics, his most recent book focuses on the importance of effective data storytelling.In this episode, we cover a range of topics including:- Why do we need data storytelling- What does storytelling entail- Data, narrative, and visuals- How to distill down complex topics- Step by step process of building a practical data story

Joe Reis is a business-minded data engineer who’s worked in the industry for 20 years. He is the CEO and Co-Founder of Ternary Data, a data engineering and architecture consulting firm based in Salt Lake City, Utah. He volunteers with several technology groups and teaches at the University of Utah. In his spare time, he likes to rock climb, produce electronic music, and take his kids on crazy adventures.In this episode, we cover a range of topics including:- What does a data engineer do?- Relationship between data engineering and MLOps- What are the components of the data engineering lifecycle?- What should a data engineer know at a fundamental level to be successful at this?- His book Fundamentals of Data Engineering - Why writing a book is one of the few activities that exposes your weaknesses and very deeply refines your thinking- The three pillars of a solid data foundation: data architecture, data engineering, and DataOps. - How do you structure your first call with a potential client? - Why is there a mismatch between expectations and reality when it comes to using data science within a business?- Why he puts a lot of the responsibility on the student to get good at researching problems and solutions- How to think about architecture as a data professional

In this episode, Prateek Joshi talks about the latest developments in:- Using AI to build digital twins for nuclear fusion reactors- Photonic chips for fast image recognition- Electronic skin for touch sensitive robots- Delivering brain MRI in 1 minute- Using AI for energy grid management- Analyzing satellite imagery using machine learning

Vin Vashishta is a globally recognized expert on AI Strategy and Data Science. He has been a LinkedIn Top Voice in Data Science and has been featured on dozens of Top 10 Lists over the last 7 years. His client list includes the likes of Walmart, JPMC, Siemens, and Airbus. He delivers products with recurring revenue streams in the 100s of millions and build Data Science teams from the ground up. He advises startups and teaches founders how to launch their first ML based product. He founded V-Squared in 2012 and built it into a successful AI Strategy consulting practice. 4 years ago, he started teaching Business Strategy For Data Scientists. In this episode, we cover a range of topics including:- Teaching a strategy class for data scientists- How to measure the success of data science projects- How to identify the highest value opportunities- You learn a lot by building ML models, but you learn more my maintaining them for 6 months. Why is that?- What are transferable capabilities for a professional who wants to transition into data science?- How to inject reality into ML products using domain knowledge?- Advantages of rapid prototyping- How to structure AI strategy sessions with potential new collaborators?- What is career coaching?- How to hire great data scientists?

In this AMA episode, the host Prateek Joshi answers the following questions:- How is machine learning being used in the space industry?- How is synthetic data being used to train AI systems?- Are foundation models going to become more prevalent in production ML systems?- What do you think about AI-infused coding tools?

Serg Masis is a Data Scientist in agriculture with a background in entrepreneurship and web/app development. He's the author of the book "Interpretable Machine Learning with Python". In addition to ML interpretability, he's passionate about explainable AI, behavioral economics, and ethical AI.In this episode, we cover a range of topics including:- How did he get into machine learning?- What is interpretable ML?- What is post hoc interpretability?- Process fairness vs statistical fairness- How an algorithm creates the model?- How a model makes predictions?- What makes an ML model interpretable?- How do you measure the interpretability of a model?- How do parts of the model affect predictions?- Does the method of interpretation depend on the model? Or can we apply a given method to a number of models?- Can you explain a specific prediction from a model?- What techniques can we use to interpret neural networks?- What techniques are available to increase the interpretability of a model? 

In this AMA episode, the host Prateek Joshi answers the following questions received from listeners and readers:- When it comes to experimentation in machine learning, does Python have an advantage over Matlab?- I have many years of experience in database technology and I'd like to switch to machine learning. What advice would you give me?- What is the most difficult part of building a machine learning startup? What is your advice if someone wants to do it?- I just graduated from college and want to be the AI domain. What roles are available in this field?

Zach Keller leads the data science team at Trove, a white label platform that supports resale as a channel for the world's most beloved brands. He has worked as a data scientist and machine learning engineer, building machine learning systems that operate at scale. He's been in industries such as manufacturing, finance, and re-commerce. At Trove, he focuses on growing and mentoring the data science team. He lives in Dallas with his wife and dog.In this episode, we cover a range of topics including:- His journey into data science- What is Recommerce- How does circular shopping work- Carbon footprint in ecommerce- How is machine learning used in ecommerce- How should new entrants evaluate what you like within data science- The role of mathematics in learning ML- The role of economics in ecommerce and marketplaces- How incentives work in marketplaces- Push bucket vs pull bucket- How to build good data science culture

In this episode, the host Prateek Joshi covers the latest developments in Machine Learning including:- Predicting battery lifetimes- Fighting wildfires- Rainfall mapping- Avoiding idling at traffic lights- General purpose AI system that can perform 604 tasks- Google releases world's largest publicly available machine learning hub- A plant nutrient detected by AI comes to market- European Union's recently proposed AI Act- ML startups that are gaining traction