Demetrios Brinkmann is one of the main organizers of the MLOps Community and currently resides in a small town outside Frankfurt, Germany. He is an avid traveler who taught English as a second language to see the world and learn about new cultures. He fell into the Machine Learning Operations world, and since, has interviewed the leading names around MLOps, Data Science, and Machine Learning. Since diving into the nitty-gritty of ML Operations he felt a strong calling to explore the ethical issues surrounding AI/ML. When he is not conducting interviews, you can find him making stone stackings with his daughter in the woods or playing the ukulele by the campfire.

Vishnu Rachakonda is a data scientist at Firsthand, where he helps build data-intensive systems that identify and connect individuals living with serious mental illness to firsthand’s peer-based recovery model. Vishnu is also the Head of Operations for the MLOps Community, the world’s largest online hub for production ML practitioners, and co-hosts the community’s podcast "MLOps Coffee Sessions", whose past guests include Jeremy Howard, D. Sculley, and other industry luminaries. Prior to this, he was the first machine learning hire at Tesseract Health, a 4Catalyzer company focused on ophthalmic imaging, and a teaching assistant for the spring 2021 edition of Full Stack Deep Learning. He obtained a BS and MS in bioengineering from the University of Pennsylvania.

In this episode, we cover a range of topics including:
- Journey into machine learning
- How they started the MLOps community and grew it into the world's largest community of its kind
- Framework for building online communities and getting the first few members to join
- What tools can be used to manage online communities
- ML tooling landscape
- What signals can we use to identify ML tools that are gaining traction before it's obvious
- Why should an ML tool be opinionated

In this episode, the host Prateek Joshi covers the latest developments in Machine Learning including:
- Brain computer interface enters human trials
- Nanomagnetic computing for low-energy AI
- Speeding up the process of counting microplastics using Machine Learning
- Quantum tunneling memory to boost AI energy efficiency
- Wine and beer reviews written by AI
- Spotting cavity using an AI system
- FDA approves the world's first automated and wearable 3D breast ultrasound
- Intel announces new chips for AI processing

Thom Ives is the founder of Integrated Machine Learning & AI providing services and instruction in the data science community. And he is the lead data scientist at AI Strategy Corporation. He is the recipient of more than 25 US Patents for many different types of devices. He has a PhD in mechanical engineering and has over 47,000 followers on LinkedIn. He has built an amazing career over the last 30 years.

In this episode, we cover a range of topics including:
- His AI journey
- Creating educational content
- His upcoming book
- Building communities
- "You must first BE to DO to HAVE". What does this mean in our context of data science?
- Building a portfolio of projects as a data scientist
- Growth happens through cycles and maintenance is integral to cycling. How should a machine learning professional think about it?
- Establishing your online presence as a data scientist
- Preventing burnout and the importance of recovery periods
- Storytelling for data scientists
- Why he loves Python

In this episode, the host Prateek Joshi covers a range of topics including:
- Generative AI and OpenAI's DALL-E 2
- ML based enzyme that can break down plastic really quickly
- Predicting the performance of plasma for nuclear fusion
- Creating low-carbon concrete with ML
- Building ML models that suggest molecular structures that can be synthesized in the lab
- Google launches Pix2Seq, a new language interface for object detection.
- DeepMind introduces Flamingo, a single visual language model (VLM) for few-shot learning.
- Anaconda announces PyScript. It's an in-browser, single-include way to run Python scripts in HTML pages as easily as JavaScript.

Tushar Gupta is a Senior Product Manager at Packt. He produces books in the Machine Learning and AI portfolio. He's been in tech publishing for close to 8 years. And has produced over 300 books to date. 

In this episode, we cover a range of topics including:
- How did you get into the world of book publishing?
- How do you identify topics for books?
- What trends are you seeing in ML books?
- What does great writing look like to you?
- What is the learning journey of a reader?
- How to think about your book's target audience?
- How do you work with authors?
- What's the step by step process of writing a book?
- What advice do you have for people who want to write their first book?
- What areas of ML are gaining traction in 2022?
- What type of book tends to sell more? A text book, a book containing recipes, or a book that does a deep dive?
- What do book readers want?
- What's the role of communities in book publishing?

Richad Nieves-Becker is a self-taught data scientist with an eclectic background. He currently leads the data science function at Revantage, a real estate shared service organization in the Blackstone family. He got a BA in Neuroscience and Anthropology. And was on the PhD path until he realized it was not for him. He pivoted and earned a Masters in Commerce from the University of Virginia. He started at CoreLogic focusing on text mining, then moved to Greystone leading all things data in an innovation lab. He credits his career progress to focusing on impact and deeply understanding the business case. 

In this episode, we cover a range of topics including:
- His entry into data science
- Academic vs business work
- How he cold emailed his way to getting job interviews
- Why data scientists need sales skills
- How data scientists should think about building a portfolio
- Defining tractable problems in machine learning and data science
- Moving from mathematics to data science
- Framework for creating educational content
- Creating a course for data scientists
- How he interviews people
- The report he's writing for new data science leaders
- Building good culture by aligning an individual's desires to the company's goals
- The advantage of modular products
- Rise of MLOps and data versioning
- Monetization of models

Matt Kirk is a senior data scientist at Zeitworks specializing in harnessing data to empower humans to achieve their highest potential at work. He has contributed to open source, written books, spoken at conferences, and worked with startups ranging from three person upstarts to billion dollar unicorns. He got into data science by studying quantitative economics and working as a junior quant. He is a cancer survivor, a father of a firecracker, a voracious reader, a recovering musician, and a Zen student. 

In this episode, we cover a range of topics including:
- Quantitative economics 
- Writing books
- Surviving cancer and dealing with adversity
- Being a Zen student
- Using the pomodoro technique
- Building a strong culture
- Current and future trends in data

Che Sharma is the founder and CEO of Eppo. It's a next gen A/B experimentation platform that helps product teams run tests that are reliably informative. He was previously an early Data Scientist at Airbnb, where he spent 5 years working on fraud detection, logging integrity, and mobile product analytics. The data science team grew from 5 to more than 80 while he was there. He got his Bachelors and Masters degrees from Stanford with a focus on Electrical Engineering and Statistics.

In this episode, we cover a range of topics including:
- How he entered the world of data 
- The concept of experimentation
- How can data professionals setup a framework to run experiments
- Storytelling
- Reusable components
- Creating customer empathy
- The rise of data communities

Pedram Navid is the Head of Data at Hightouch where he leads advocacy for data practitioners, community-building, and the internal data stack. Prior to Hightouch, he has worked as a data engineer building up a modern data stack from the ground up and as a data scientist where he helped scale analytics at a major bank. He contributes to open-source packages and creates data memes on Twitter.

In this episode, we cover a range of topics including:
- How he entered the world of data science
- Reverse ETL
- How can new data science professionals evaluate various roles in data science
- How can interviewees evaluate team culture
- How data gets political within a company
- How should data practitioners forge alliances
- What is data activation
- How he manages data teams

Amir Feizpour is the cofounder and CEO of Aggregate Intellect. It's a platform to accelerate knowledge discovery for research and development teams, including but not limited to AI. You can visit ai.science to learn more about it. Previously he worked in the industry as a data scientist, a senior manager, and a product lead in NLP. He has a PhD in Physics from University of Toronto and he did his postdoc in quantum computing at University of Oxford. You can join the Aggregate Intellect Slack community here. You can book a free 20-min coaching session with him here.

In this episode, we cover a range of topics including:
- His journey into the world of data
- What is knowledge discovery
- How to build online communities
- What he's building at Aggregate Intellect
- What does great data science culture look like
- What product has impressed you the most
- Current and future trends in AI