If you think that knowing Tensorflow and Scikit-learn is enough, think again.
MLOps is one of those trendy terms today.
What is MLOps and why is it important?
In this episode I speak about the undeniable evolution of the data scientist in the last 5-10 years.
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If building software is your passion, you’ll love ThoughtWorks Technology Podcast. It’s a podcast for techies by techies. Their team of experienced technologists take a deep dive into a tech topic that’s piqued their interest — it could be how machine learning is being used in astrophysics or maybe how to succeed at continuous delivery.
Amethix use advanced Artificial Intelligence and Machine Learning to build data platforms and predictive engines in domain like finance, healthcare, pharmaceuticals, logistics, energy. Amethix provide solutions to collect and secure data with higher transparency and disintermediation, and build the statistical models that will support your business.
Rust and deep learning with Daniel McKenna (Ep. 135)
Scaling machine learning with clusters and GPUs (Ep. 134)
What is data ethics? (Ep. 133)
A Standard for the Python Array API (Ep. 132)
What happens to data transfer after Schrems II? (Ep. 131)
Test-First Machine Learning [RB] (Ep. 130)
Similarity in Machine Learning (Ep. 129)
Distill data and train faster, better, cheaper (Ep. 128)
Machine Learning in Rust: Amadeus with Alec Mocatta [RB] (ep. 127)
Top-3 ways to put machine learning models into production (Ep. 126)
Remove noise from data with deep learning (Ep.125)
What is contrastive learning and why it is so powerful? (Ep. 124)
Neural search (Ep. 123)
Let's talk about federated learning (Ep. 122)
How to test machine learning in production (Ep. 121)
Why synthetic data cannot boost machine learning (Ep. 120)
Machine learning in production: best practices [LIVE from twitch.tv] (Ep. 119)
Testing in machine learning: checking deeplearning models (Ep. 118)
Testing in machine learning: generating tests and data (Ep. 117)
Why you care about homomorphic encryption (Ep. 116)
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