What is an end-to-end #MachineLearning learning #pipeline? What benefits does it bring to a data scientist’s workflow? The latest instalment on the #MLOps series, introducing the notion of ML pipelines.
#projectalesia#DataScience
https://t.co/xqaA7gAFAD
Machine Learning Operations (#MLOps ), is a budding career specialization with a promising future. In our latest article, we explain what #ML Ops is, and discuss its role in modern data teams. More on this topic coming soon on #ProjectAlesia
https://t.co/XLF9HDtJzi
Our latest podcast is up! This time we had the privilege of interviewing #cybersecurity expert Omer Aziz Khan over a deep and insightful session about the state of modern cybersecurity, and its relationship with the growing big #data industry.
https://t.co/xj7t0bNahZ
What does digital sovereignty mean and why does it matter? Learn about digital sovereignty, how open infrastructure is helping to achieve it and the goals of the GAIA-X initiative: https://t.co/T3f2JGbyrO
"Even the most sophisticated models, the latest #algorithms, and highly experienced #AI experts cannot make AI a practical success unless it is connected to a meaningful #business goal", writes @Ellen_Friedman. Read her latest blog to learn more: https://t.co/nKCmz4s6SQ
We are actively working on Neural Architecture Search (NSA) and develop AI that builds AI
Fundamentally, this is what makes no code/low code machine learning possible
We will be presenting at #AAAI2021 tmrw at 8:45am and 4:45pm PT!
Paper here - https://t.co/3bp33neqeA
For all @streamlit lovers: I made a list of the best Streamlit apps, ranked by Github stars 🏆 88 projects & counting. If you built sth awesome and want to add it, plz reach out!
🎈👉 https://t.co/9ASKmNNl0u
In the Machine Learning Engineering newsletter this week we cover Misinformation Tech & Solutions, Metadata Management Systems, Papers With Code Datasets, AI & ML Platforms in 2021, AI Regulatory Proposals, ML Libraries, AI Guidelines, + more 🚀 https://t.co/kVHkvdeqnB
Google's iOS apps release cycle before & after Apple asks to disclose privacy labels.
Thie pattern is probably just a coincidence. We all know "transparency forms the bedrock of [their] commitment to users"...