-
Be the first to like this
Slideshare uses cookies to improve functionality and performance, and to provide you with relevant advertising. If you continue browsing the site, you agree to the use of cookies on this website. See our User Agreement and Privacy Policy.
Slideshare uses cookies to improve functionality and performance, and to provide you with relevant advertising. If you continue browsing the site, you agree to the use of cookies on this website. See our Privacy Policy and User Agreement for details.
We provide an update on developments in the intersection of the R and the broader machine learning ecosystems. These collections of packages enable R users to leverage the latest technologies for big data analytics and deep learning in their existing workflows, and also facilitate collaboration within multidisciplinary data science teams. Topics covered include – MLflow: managing the ML lifecycle with improved dependency management and more deployment targets – TensorFlow: TF 2.0 update and probabilistic (deep) machine learning with TensorFlow Probability – Spark: latest improvements and extensions, including text processing at scale with SparkNLP
Be the first to like this
Be the first to comment