My superpower toolkit: TFRecorder, TensorFlow Cloud, AI Platform Predictions and Weights & Biases. Lightweight yet scalable TensorFlow workflow on Google Cloud
I am just going to say it. I am absolutely overwhelmed and intimidated with the growing breadth and depth of machine learning (ML) today.
This does not even include the algorithm and modeling space which also makes me feel like an imposter without a research background. There has got to be an easier way!
I spent the last few weeks thinking about this dilemma and what I would recommend to a data scientist with a similar mindset as me. Many of those topics above are important to learn, especially if you want to focus on the new field of MLOps, but are there tools and technologies that can allow you to stand on the shoulder of giants?
Below are 4 such tools that abstract away much of the complexity and can allow you to more efficiently develop, track and scale your ML workflows.
Train and Deploy TensorFlow Models using Google Cloud AI Platform. A practical workflow of TensorFlow model training and deploying
Google is much more than a search company. Learn about all the tools they are developing to help turn your ideas into reality through Google AI.
In this guide, we learn how to develop a TensorFlow model and serve it on the Google Cloud Platform (GCP). We consider a regression ...
Most popular Data Science and Machine Learning courses — August 2020. This list was last updated in August 2020 — and will be updated regularly so as to keep it relevant
Google Cloud’s data analytics and AI and machine learning solutions can help SAP customers store, analyze, and derive insights from all their data in the cloud. You can shift your SAP applications to the cloud to take full advantage of a flexible, scalable solution that eliminates ongoing infrastructure maintenance costs; leverage BigQuery for your enterprise data to unlock new business value; integrate machine learning into business processes; or mix and match solutions to suit your needs now and in the future.