Our guest today is Santiago Valdarrama, Director of Computer Vision at Levatas and founder of Bnomial.

In our conversation, we first talk about Santiago's transition from software engineering to the world of machine learning. He explains how he got into AI and why software engineering skills are important when working as a Data Scientist / ML engineer.

We then dig into computer vision at Levatas, a company which develops solutions for automating and scaling visual inspection. Santiago walks us through different machine learning projects that he worked on, summarises the latest advances in computer vision and shares his thoughts on the future of the field.

We finally talk about Bnomial, a website created by Santiago which posts one machine learning questions every day. Santiago explains the importance of building good habits and shares advice on how to progress in your career.

If you enjoyed the episode, please leave a 5 star review and subscribe to the AI Stories Youtube channel.

Follow Santiago on LinkedIn: https://www.linkedin.com/in/svpino/
Follow Santiago on Twitter: https://twitter.com/svpino
Subscribe to Santiago's Youtube channel: https://www.youtube.com/c/Santiagox0
Answer one machine learning question every day on Bnomial: https://today.bnomial.com/

Follow Neil on LinkedIn: https://www.linkedin.com/in/leiserneil/

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(00:00) : Intro. 

(03:45) : How did you get into the field ? 

(07:53) : Example of problems solved using AI. 

(12:47) : Software engineering skills for Data Scientists / ML engineers.

(20:08) : Computer vision at Levatas.

(28:16) : Management. 

(31:43) : Common mistakes made by Data Scientists / ML engineers.

(42:00) : Computer vision & the future of the field.

(47:55) : Bnomial.

(58:57) : One advice to progress in your career. 

Our guest today is Damien Benveniste, CTO at Motivee and Ex Tech Lead at Meta.

In our conversation, Damien first explains how he transitioned from a PhD in Physics to the world of AI and talks about the differences between machine learning in industry and doing a PhD. He also shares his experience getting laid off three times in a row and gives advice on how to do well in industry, how to choose the right company to work for and how to keep moving forward. 

We then discuss Machine learning for ads ranking and talk about deep learning architecture optimisation. Damien explains how he made Meta earn millions of dollars by improving the performance of machine learning models.

We finally talk about Motivee, a tech startup developing a smart decision insight tool powered by employee listening. We dig into some of the challenges when building a startup like the need to satisfy clients while keeping investors happy. Damien finally shares advice on how to progress in your career.

If you enjoyed the episode, please leave a 5 star review and subscribe to the AI Stories Youtube channel.

Follow Damien on LinkedIn: https://www.linkedin.com/in/damienbenveniste/
Follow Neil on LinkedIn: https://www.linkedin.com/in/leiserneil/

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(00:00) : Intro

(02:10) : How did Damien get into the field ? 

(07:28) : Why AI ?  

(13:51) : ML in industry vs PhD

(32:46) : Choosing the right company to work for

(38:36) : Getting laid off

(52:30) : Machine learning for ads ranking at Meta

(1:09:21) : $$ value of deep learning algorithms optimisation

(1:20:26) : ML at Meta vs ML in Startups

(1:26:03) : Founding a tech Startup (Motivee)

(1:47:28) : One advice to progress in your career

Our guest today is Marguerite Graveleau, Technical Product Manager at Iwoca; a Fintech company lending money to small and medium businesses; and Ex Data Scientist at Lyft, one of the main competitors of Uber.

In our conversation, Marguerite first explains how she got into the field. We talk about her Master at Stanford and give advice on how to get a first job after university. We discuss the importance of cold emails and building a portfolio of Data Science projects.

We then explore Data Science at Lyft where Marguerite explains how she used data and econometric models to maintain a market equilibrium between the demand for rides and the number of available drivers.

We finally talk about strategy and AI at Iwoca. We dive into the importance of monitoring, talk about A/B testing but also discuss what it feels like to work in a fast paced credit risk company.

If you enjoyed the episode, please leave a 5 star review and subscribe to the AI Stories Youtube channel.

Follow Marguerite on LinkedIn: https://www.linkedin.com/in/margueritegraveleau/
Follow Neil on LinkedIn: https://www.linkedin.com/in/leiserneil/

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(00:00) : Intro

(02:40) : How did you get into the field ?

(06:03) : How to get your first job ?

(12:29) : Data Science at Lyft.

(23:56) : Doing an MBA.

(29:09) : What is Iwoca ?

(32:22) : Operation Strategy at Iwoca. 

(37:54) : The importance of monitoring.

(40:50) : Challenges of working in a credit risk company.

(44:56) : A / B tests.

(49:10) : Trying multiple different jobs.

(54:16) : What is a good Data Scientist ?

(58:36) : One advice to progress in your career.

Our guest today is Aleksa Gordic, Research Engineer at Deepmind and founder of the AI Epiphany Youtube channel. 

In our conversation, we first talk about Aleksa’s Bachelor in Electronics and Computer Science at the University of Belgrade and then discuss his experience as a Software Engineer at Microsoft. 

He then explains how he transitioned to the world of AI by doing courses, bootcamps and learning subfields like GANs, Neural Style Transfer or Reinforcement Learning on his own. He shares his learning strategy and gives advice on how to get into the field. 

We finally talk about Deepmind, how Aleksa managed to get an offer and his latest work on Flamingo, an impressive visual language model which sets state-of-the-art results in few shots learning on a wide range of tasks.

If you enjoyed the episode, please leave a 5 star review and subscribe to the AI Stories Youtube channel.

Follow Aleksa on LinkedIn: https://www.linkedin.com/in/aleksagordic/
Follow Neil on LinkedIn: https://www.linkedin.com/in/leiserneil/

Useful resources:
- Getting started in AI by Aleksa Gordic
- Learning to Learn Coursera course
- Flamingo blog post

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(00:00) : Intro

(02:50) : Bachelor in Electronics and Computer Science

(05:51) : Transitioning to Software Engineering

(07:40) : Fighting the imposter syndrome

(12:49) : Software Engineer at Microsoft

(21:07) : How Aleksa got into AI and ML

(25:04) : Intelligence

(28:36) : ML courses and Hackathons

(33:37) : Productivity

(38:04) : Learning subfields of AI in depth

(46:36) : Advice to get started in the field

(50:18) : Getting into Deepmind

(55:27) : Working at Deepmind

(58:06) : Flamingo and other algorithms

(01:08:19) : Challenges and mistakes faced

(01:13:10): What is a good ML engineer ? 

(01:21:23) : One advice to progress in your career

Our guest today is Smriti Mishra, applied AI and Data Science Researcher at the KTH Royal Institute of Technology in Stockholm and ex Head of AI at Earthbanc.

In our conversation, we first talk about Smriti’s career, how she transitioned from electrical engineering to the world of Data Science and AI and how she became passionate about healthcare and climate change.

Smriti then describes her experience in tackling climate change with AI. She introduces the concept of carbon sequestration and explains how she estimates it using deep learning algorithms, satellite images and additional data sources.

We finally talk about her current research, AI for diversity but also share advice on how to get into the field and how to become a better Data Scientist.

If you enjoyed the episode, please leave a 5 star review and subscribe to the AI Stories Youtube channel.

Follow Smriti on LinkedIn: https://www.linkedin.com/in/smritimishra/
Follow Neil on LinkedIn: https://www.linkedin.com/in/leiserneil/


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(00:00) : Intro

(02:51) : Smriti’s bachelor in electronics

(05:58) : 2 events which made Smriti get into Data science

(11:43) : Switching from AI in healthcare to AI for climate change

(16:34) : How Earthbanc tackles climate change

(19:38) : The concept of carbon sequestration

(25:09) : Earthbanc

(26:53) : Predicting carbon sequestration using deep learning

(37:44) : AI in research vs AI in industry

(40:23) : AI for diversity

(46:27) : Getting into Data Science

(49:10) : What is a good Data Scientist

(51:00) : One advice to progress in your career

Our guest today is Joshua Starmer, Founder and CEO of StatQuest, a Youtube channel with over 700k subscribers which clearly explains complex statistics and machine learning methods.

In our conversation, Josh first talks about his career: his PhD in Bioinformatics, how he got introduced to the concepts of Statistics and variation and how he learned more about it.

We then explore StatQuest, how the channel started and discuss the difficulties behind leaving a full time job to start your own project. We also explain what the channel is about and why it became so successful.

We finally talk about Josh's new book "The StatQuest Illustrated Guide to Machine Learning" and give advice on how to make progress in Data Science.

If you enjoyed the episode, please leave a 5 star review and subscribe to the AI Stories Youtube channel.

Follow Neil on LinkedIn: https://www.linkedin.com/in/leiserneil/


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(00:00) : Intro

(01:57) : Bachelor Degree in Computer Science and Music

(05:59) : Why choosing Computer Science over Music ?

(09:55) : Why doing a PhD in Bioinformatics ?

(13:17) : Being your own boss

(18:31) : How Josh got introduced to the world of Data Science and Statistics

(24:50) : Caring about general concepts rather than focusing on the details

(27:36) : Working in a research lab

(30:55) : How StatQuest started

(38:47) : What is StatQuest ? 

(41:47) : Why is StatQuest so successful ?

(46:10) : How to build a successful Youtube channel

(51:41) : The StatQuest Illustrated Guide to Machine Learning (new book)

(56:37) : One advice for someone to progress in their career

Our guest today is Jan Xu, Senior Machine Learning Engineer at Deep Render, a startup developing the next generation of compression technology using Deep Learning.

This episode is a special one since Jan and I studied Civil Engineering at Imperial College London together between 2015 and 2019. In our conversation, we first talk about our bachelor degree at Imperial and how Jan made the transition from Engineering to Data Science and AI. He explains how he learned to code, the first machine learning projects that he worked on and how he managed to get a Job as a Machine Learning Engineer without any bachelor, master or even PhD in the field.

We then dig further into Deep Learning applied to computer vision where Jan describes how Deep Render uses an algorithm called Autoencoder which improves video compression and outperforms traditional approaches. He also clarifies why video compression is such an interesting problem and explains how AI can help tackle this issue.

If you enjoyed the episode, please subscribe to my Youtube channel: https://www.youtube.com/channel/UCWvn6k4aeceyYKjAleML9VA

Our guest today is Mark Freeman II, Senior Data Scientist at Humu and CEO of On the Mark Data. 

In our conversation, Mark first talks about his Bachelor in Sociology and his Master at Stanford. He explains how he managed to get into Stanford despite having bad grades and describes how he got into the world of Statistics and AI. 

We then discuss Data Science in Startups, the pros and cons of working in a Startup and the main differences between Data Science in small and large companies. Mark shares projects that he worked on and explains how he almost made his company lose $1 Million. 

We finally dive into the world of entrepreneurship and AI. We talk about On the Mark Data; Mark’s latest Startup where he works on consulting and content creation. Mark shares his experience, his failures and advice on how to do well as a Data Scientist.

If you enjoyed the episode, please subscribe to my Youtube channel: https://www.youtube.com/channel/UCWvn6k4aeceyYKjAleML9VA

Our guest today is Rishabh Mehrotra, ex Tech Lead at Spotify and Director of Machine Learning at ShareChat.

In our conversation, Rishabh first talks about his transition from working as an analyst at Goldman Sachs to his PhD at UCL where he worked on deep models for user understanding, knowledge discovery and decision optimization. We then explore Machine Learning at Spotify which Rishabh joined in 2017 as a founding member of the research lab. We dig into recommender systems and discuss how AI can be used to improve user experience by understanding their behaviours and recommending personalised songs / playlists.

Rishabh also talks about his new role at ShareChat, a leading Indian social media platform with over 300 million users. We discuss the impact of machine learning on the creator economy, the main challenges, the importance of metrics to train and monitor ML models but also share advice on how to become a good Data Scientist / ML Engineer and progress in your career.

If you enjoyed the episode, please subscribe to my Youtube channel: https://www.youtube.com/channel/UCWvn6k4aeceyYKjAleML9VA

Our guest today is Christina Stathopoulos, Analytical Lead at Waze and Adjunct Professor at IE University in Madrid.

In our conversation,  Christina first shares her opinion on her MSc in Business Analytics and Big Data at IE university and discusses the pros and cons of an in-person vs an online master. Christina then talks about her transition into teaching, the challenges that she faced as well as advice that she has on how to start learning more about Data Science and Machine Learning. We also talk about the #bookaweekchallenge and discuss the importance of building a personal brand.

We then focus on AI at Waze, a navigation app bought by Google in 2013 for around $1 billion.  Christina explains how she got into Google and why Machine Learning plays such an important role. We finally outline the importance of diversity in tech teams and share advice on what "tech minorities" such as women can do to break into the field and reject the "imposter syndrome". 

Follow Christina on LinkedIn: https://www.linkedin.com/in/christinastathopoulos/

Subscribe to my Youtube channel: https://www.youtube.com/channel/UCWvn6k4aeceyYKjAleML9VA