The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)
Sam Charrington
Machine learning and artificial intelligence are dramatically changing the way businesses operate and people live. The TWIML AI Podcast brings the top minds and ideas from the world of ML and AI to a broad and influential community of ML/AI researchers, data scientists, engineers and tech-savvy business and IT leaders. Hosted by Sam Charrington, a sought after industry analyst, speaker, commentator and thought leader. Technologies covered include machine learning, artificial intelligence, deep learning, natural language processing, neural networks, analytics, computer science, data science and more.
Episodes
Mentioned books
9 snips
Dec 23, 2020 • 46min
Machine Learning as a Software Engineering Enterprise with Charles Isbell - #441
In this engaging discussion, Charles Isbell, Dean at Georgia Tech's College of Computing and an expert in interactive machine learning, dives into the transformative power of education in tech. He highlights the success of Georgia Tech's online Master's program, boasting over 11,000 students. The conversation explores the crucial need for diverse voices in AI and reflects on the systemic biases within machine learning. Isbell also emphasizes embedding ethics into engineering education, advocating for a balance between technological advances and human values.
Dec 21, 2020 • 58min
Natural Graph Networks with Taco Cohen - #440
Taco Cohen is a Machine Learning Researcher at Qualcomm Technologies, known for his work on equivariant networks and video compression. In this conversation, he introduces his paper on Natural Graph Networks and the concept of 'naturality,' which proposes that relaxed constraints can lead to more versatile architectures. Taco shares insights on the integration of symmetries from physics in AI, recent advances in efficient GCNNs for mobile, and innovative techniques in neural compression that significantly enhance data efficiency.
Dec 18, 2020 • 41min
Productionizing Time-Series Workloads at Siemens Energy with Edgar Bahilo Rodriguez - #439
Edgar Bahilo Rodriguez, Lead Data Scientist at Siemens Energy, dives into the complexities of productionizing time-series workloads. He shares insights from his journey moving from energy engineering to machine learning, discussing the blend of technologies used to enhance forecasting models. Edgar highlights industrial applications in wind and energy management, emphasizing sustainable solutions. The conversation also touches on the evolution of machine learning in operations, automation in monitoring, and the integration of diverse programming languages in robust AI workflows.
Dec 16, 2020 • 41min
ML Feature Store at Intuit with Srivathsan Canchi - #438
Srivathsan Canchi, Head of Engineering for Intuit's Machine Learning Platform, discusses the groundbreaking role Intuit played in developing the AWS SageMaker Feature Store. He explains how feature stores enhance machine learning by ensuring data consistency and addressing challenges like feature drift. The conversation also touches on the exploding interest in feature stores, their importance in scalable AI, and the implementation challenges faced during their establishment, including the benefits of GraphQL integration. Tune in for insights on the future of machine learning!
Dec 14, 2020 • 49min
re:Invent Roundup 2020 with Swami Sivasubramanian - #437
Swami Sivasubramanian, VP of Artificial Intelligence at AWS, shares insights from the recent re:Invent conference. He highlights the introduction of groundbreaking tools like Tranium for efficient machine learning training and SageMaker enhancements for better workflow management. The conversation dives into the significance of integrating DevOps with machine learning processes and showcases tailored AI solutions for industrial applications. With anecdotes reflecting the field's evolution, it underscores the push towards making machine learning accessible to all.
Dec 11, 2020 • 40min
Predictive Disease Risk Modeling at 23andMe with Subarna Sinha - #436
Subarna Sinha, a leader in Machine Learning Engineering at 23andMe, dives into the world of genomic data and disease prediction. She discusses the development of polygenic risk scores and the complexities involved in predicting disease likelihood from genetic variations. The conversation highlights the technological innovations behind their ML platform, including tools like AWS and Jenkins. Subarna also addresses the challenges of data drift and the importance of team dynamics in refining predictive models, ensuring more accurate health assessments.
Dec 9, 2020 • 40min
Scaling Video AI at RTL with Daan Odijk - #435
Daan Odijk, Data Science Manager at RTL, shares his expertise in AI and personalized content strategies. He delves into the MLOps journey at RTL, tackling the complexities of ad optimization and video understanding. Daan discusses the intricate challenges of processing large video files and the engineering feats of creating dynamic content selections. The conversation highlights the impact of custom platforms on efficiency and innovation in media, showcasing how AI transforms viewer experiences through tailored recommendations and innovative trailer creations.
Dec 7, 2020 • 46min
Benchmarking ML with MLCommons w/ Peter Mattson - #434
Peter Mattson, President of MLCommons and a Staff Engineer at Google, discusses the vital role of MLPerf in standardizing machine learning benchmarks. He emphasizes the need for ethical guidelines in AI, particularly through initiatives like the People's Speech dataset, which addresses fairness and representation in machine learning. Mattson also shares insights on streamlining model sharing with MLCube and the importance of robust performance metrics as the ML landscape evolves, aiming to democratize access and innovation in the field.
Dec 3, 2020 • 46min
Deep Learning for NLP: From the Trenches with Charlene Chambliss - #433
Charlene Chambliss, a Machine Learning Engineer at Primer AI with expertise in NLP, discusses her unique transition from psychology to data science. She shares insights on working with BERT models, detailing projects like her multilingual BERT initiative and a COVID-19 classifier. The conversation dives into challenges in data labeling, the use of innovative techniques for topic drift, and debugging NLP models. Charlene also offers advice for those looking to shift into tech from non-technical backgrounds, emphasizing the importance of mentorship.
Nov 30, 2020 • 56min
Feature Stores for Accelerating AI Development - #432
In this discussion, Kevin Stumpf, co-founder and CTO of Tecton; Willem Pienaar, engineering lead at Gojek and Feast Project founder; and Maxime Beauchemin, founder of Preset and creator of Apache Airflow, dive deep into feature stores. They explore how feature stores can accelerate AI development, streamline data management, and address operational challenges. The conversation highlights the evolution of these stores, their importance in automating workflows, and the collaboration needed between data engineers and scientists to maximize efficiency.
