Published inAI³ | Theory, Practice, BusinessHow to Build Machine Learning Pipelines with Airflow & PapermillLearn to scale your machine learning workflows at will.Apr 7, 20201Apr 7, 20201
Published inAI³ | Theory, Practice, BusinessAnomaly Detection Part 1: AutoencoderWhat is an anomaly?Feb 27, 2020Feb 27, 2020
Published inAI³ | Theory, Practice, BusinessHow to Ship Machine Learning Models into Production with TensorFlow Serving and KubernetesLearn how to ship in this 5-minute read on TensorFlow ServingDec 23, 2019Dec 23, 2019
Published inAI³ | Theory, Practice, BusinessTensorFlow 1.0 vs 2.0, Part 4: TensorBoardWhy TensorBoard?Dec 5, 20191Dec 5, 20191
Published inAI³ | Theory, Practice, BusinessTensorFlow 1.0 vs 2.0, Part 3: tf.kerastf.keras, one ring to rule them all!Nov 22, 2019Nov 22, 2019
Published inAI³ | Theory, Practice, BusinessTensorFlow 1.0 vs 2.0, Part 2: Eager Execution and AutoGraphIn my previous post, I covered computational graphs with TensorFlow 1.0. As we’ve learned, these graphs are stable and performant but the…Nov 12, 20192Nov 12, 20192
Published inAI³ | Theory, Practice, BusinessTensorFlow 1.0 vs 2.0, Part 1: Computational GraphsTensorFlow, a machine learning library created by Google, is not known for being easy to use. In response, TensorFlow 2.0 addressed a lot…Nov 4, 2019Nov 4, 2019
Published inAI³ | Theory, Practice, BusinessDebunking Myths about Python Names and VariablesAn introduction to the anatomy and core concepts of a python execution modelOct 22, 2019Oct 22, 2019
Published inAI³ | Theory, Practice, BusinesswhoamiI have been developing backend applications for the last few years. After reading Hackers & Painters I was captivated by the Lisp, to this…Sep 27, 2019Sep 27, 2019
