Since 2019, Pie & AI ambassadors have hosted over 700 events. Each year we celebrate their efforts.

Dr. Benjamin Harvey, CEO of AI Squaredhas used machine learning to investigate environmental problems, aid cancer research, and probe the damage caused by national security leaks

Jared Webb, chief data scientist at BlueConduit, discusses his transition from academia to business, how he navigates the challenges of working with government data, and what he looks for in new hires. 

In the mid-2000s, AI was still just a curiosity to the world at large. At Stanford University, however, one of the most popular classes on campus was Andrew Ng’s CS229 machine learning course. Enrollment was frequently too large to fit in the classroom, yet he wanted even more people to be able to master machine learning.

So, working with a few students, he created an online Machine Learning course that could be taken by anyone with an internet connection and a desire to learn. The rest is history. Coursera launched in 2012 with Machine Learning as its flagship title. It was also the platform’s most popular, with almost 5 million enrollments.

This year, to celebrate the course’s 10 year anniversary, DeepLearning.AI and Stanford Online released a successor — the Machine Learning Specialization. Andrew spoke with us about how the new Specialization improves on the original, who should take it, and how it fits into the modern AI builder’s career arc.

Kulsoom Abdullah pivoted from network security into a data science role after taking Andrew Ng’s original Introduction to Machine Learning course from Stanford in 2013. A self-described learning addict, she has taken more than 10 online courses, most recently the Deep Learning Specialization. Outside of work, she’s a fitness fanatic, and was the first competitive weightlifter to compete professionally for Pakistan — after winning a fight to change the dress code to allow religious head coverings. On top of all that, she’s a travel

Lorenzo Ostano first encountered machine learning and data science as a business analyst shortly after graduating from college. He dove into learning, and soon landed work as a machine learning engineer. After working several years for a variety of consulting companies, he recently took a job as a software engineer. He spoke to us about why he believes traditional computing skills are important to deploying enterprise machine learning applications.

Kai Saksela is the CEO of NL Acoustics, a Finnish technology startup that designs and manufactures AI products to analyze sounds. He took the Deep Learning Specialization primarily because he loves learning new skills and has been fascinated by the field for a long time. He also had a hunch that neural networks would help his company solve a core problem: providing customers guidance on what they should do when their equipment starts making strange noises. He spoke with us about how his hunch paid off and why AI plays a central role in his company’s growth.

Matt Struble is an engineer at a sportswear company, where he currently leads a team that’s developing a deep learning system for predicting shoe trends a year or two into the future. Before taking the Deep Learning Specialization, he was a computer programmer who watched data scientists from afar.

Olivier Moulin is an IT manager for a large, multi-national medical technology company who has been working in technology for over 20 years. Early in his career, he made a tough decision to take a high paying job instead of pursuing a Ph.D. He spoke with us about how the Deep Learning Specialization helped him build the confidence to go back

Apala Guha is a senior machine learning compiler engineer at Lightmatter, a Boston-area startup. Before that, she was a computer scientist who had always been interested in deep learning. When she quit her previous job at the beginning of 2020 to have a baby, she took advantage of the “time off” to take the Specialization.

Aleksandr Gontcharov is a software engineer at Microsoft. Early in his career, he moved from job to job, but none of them ever felt right. The Deep Learning Specialization helped him find his calling; he was hired for a machine learning role while still taking the courses. He spoke with us about why the Specialization was the spark that put his career in motion. 

Luciano Darriba is an AI developer living in Buenos Aires, Argentina. In his former life, he was an astrophysicist. This wasn’t fulfilling him, so he took the Deep Learning Specialization in hopes of kickstarting a new trajectory. Less than a year later, he had a new job working at Baufest, a software services company.

In this blog post, Synaptic-AnN, one of the winners of the Data-Centric AI Competition, describes techniques and strategies that led to victory. Participants received a fixed model architecture and a dataset of 1,500 handwritten Roman numerals. Their task was to optimize model performance solely by improving the dataset and dividing it into training and validation sets. The dataset size was capped at 10,000.

In this blog post, Innotescus, one of the winners of the Data-Centric AI Competition, describes techniques and strategies that led to victory. Participants received a fixed model architecture and a dataset of 1,500 handwritten Roman numerals. Their task was to optimize model performance solely by improving the dataset and dividing it into training and validation sets. The dataset size was capped at 10,000.

In this blog post, KAIST-AIPRLab, one of the winners of the Data-Centric AI Competition, describes techniques and strategies that led to victory. Participants received a fixed model architecture and a dataset of 1,500 handwritten Roman numerals. Their task was to optimize model performance solely by improving the dataset and dividing it into training and validation sets. The dataset size was capped at 10,000.

In this blog post, Johnson Kuan, one of the winners of the Data-Centric AI Competition, describes techniques and strategies that led to victory. Participants received a fixed model architecture and a dataset of 1,500 handwritten Roman numerals. Their task was to optimize model performance solely by improving the dataset and dividing it into training and validation sets. The dataset size was capped at 10,000.

In this blog post, Mohammad Motamedi, one of the winners of the Data-Centric AI Competition, describes techniques and strategies that led to victory. Participants received a fixed model architecture and a dataset of 1,500 handwritten Roman numerals. Their task was to optimize model performance solely by improving the dataset and dividing it into training and validation sets. The dataset size was capped at 10,000.

In this blog post, Divakar Roy, one of the winners of the Data-Centric AI Competition, describes techniques and strategies that led to victory. Participants received a fixed model architecture and a dataset of 1,500 handwritten Roman numerals. Their task was to optimize model performance solely by improving the dataset and dividing it into training and validation sets. The dataset size was capped at 10,000.

In this blog post, Pierre-Louis Bescond, one of the winners of the Data-Centric AI Competition, describes techniques and strategies that led to victory. Participants received a fixed model architecture and a dataset of 1,500 handwritten Roman numerals. Their task was to optimize model performance solely by improving the dataset and dividing it into training and validation sets. The dataset size was capped at 10,000.

In this blog post, GoDataDriven, one of the winners of the Data-Centric AI Competition, describes techniques and strategies that led to victory. Participants received a fixed model architecture and a dataset of 1,500 handwritten Roman numerals. Their task was to optimize model performance solely by improving the dataset and dividing it into training and validation sets. The dataset size was capped at 10,000.