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Read the complete blog below for a more detailed description of the vendors and their capabilities. Because it is such a new category, both overly narrow and overly broad definitions of DataOps abound. Reflow enables scientists and engineers to compose existing tools (packaged in Docker images) using ordinary programming constructs.
64% of the respondents took part in training or obtained certifications in the past year, and 31% reported spending over 100 hours in training programs, ranging from formal graduate degrees to reading blog posts. The tools category includes tools for building and maintaining data pipelines, like Kafka.
AWS Deep Learning Containers are docker images which are preconfigured for deep learning tasks. This blog post acts more like a step-by-step tutorial. It walks through classifying Yahoo Answers into different categories. It is the days between Christmas and New Years, so the there is not much news to share. Education/Courses.
In this blog, we’ll highlight the key CDP aspects that provide data governance and lineage and show how they can be extended to incorporate metadata for non-CDP systems from across the enterprise. The example 1_typedef-server.json describes the server typedef used in this blog. . docker run -d -p 21000:21000 --name atlas.
For a good overview of what DevOps entails and how to transition, check out this blog post. DevOps Engineers must be both good software developers and infrastructure experts and, as such, they are expected to know Linux infrastructure, networking, algorithms, and DevOps tools such as Terraform, Docker, and Kubernetes.
Build a scalable, online recommender with Keras, Docker, GCP, and GKE TL;DR Online retailers such as Wayfair and Amazon offer endless arrays of products, chosen by smart AI product recommender systems. Multiple such index libraries are generated for different furniture categories.
In this blog, I’ll describe how to solve this with Contextual Topic Identification, leveraging machine learning methods to identify semantically similar groups and surface relevant category tags of the reviews. Once we come up with a final model, we may scale it to be a production model using Docker, AWS, or a similar cloud service.
And my favorite topic: what are some of the best books, blogs, podcasts, etc., I’ll amend that point to say that the big news was Jupyter + [some manner of lightweight virtualization, ranging from VMs to Docker to virtualenv to serverless]. for beginning study in data science? Learning Data Science. Wes McKinney (2017).
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