2019년 5월 14일 화요일
In Craig Kerstiens‘s latest blog post, “A health check playbook for your Postgres database” he emphasizes the need for periodic checks for your Postgres databases to ensure it’s healthy and performing well.
2019년 5월 9일 목요일
At Microsoft Build 2019 we announced MLOps capabilities in Azure Machine Learning service. MLOps, also known as DevOps for machine learning, is the practice of collaboration and communication between data scientists and DevOps professionals to help manage the production of the machine learning (ML) lifecycle.
2019년 5월 6일 월요일
Digital disruption has created unlimited potential for companies to embrace data as a competitive advantage for their business. As a result, analytics continues to be a key priority for enterprises. When it comes to analytics, customers tell us that they need a solution that provides them with the best price, performance, security, and privacy, as well as a system that can easily deliver powerful insights across the organization. Azure has them covered.
2019년 5월 1일 수요일
Recommendation systems are used in a variety of industries, from retail to news and media. If you’ve ever used a streaming service or ecommerce site that has surfaced recommendations for you based on…
2019년 4월 29일 월요일
The Jupyter Notebook on HDInsight Spark clusters is useful when you need to quickly explore data sets, perform trend analysis, or try different machine learning models. Not being able to track the status of Spark jobs and intermediate data can make it difficult for data scientists to monitor and optimize what they are doing inside the Jupyter Notebook.
2019년 4월 9일 화요일
DevOps is the union of people, processes, and products to enable the continuous delivery of value to end users. DevOps for machine learning is about bringing the lifecycle management of DevOps to Machine Learning.
2019년 4월 1일 월요일
Everyone’s talking about machine learning (ML). Business decision makers are finding ways to deploy machine learning in their organizations. Data scientists are keeping up with all the advancements, tools, and frameworks available.
2019년 3월 20일 수요일
As data scientists, we are used to developing and training machine learning models in our favorite Python notebook or an integrated development environment (IDE), like Visual Studio Code (VSCode).
2019년 3월 20일 수요일
With ever-increasing data volume and latency requirements, GPUs have become an indispensable tool for doing machine learning (ML) at scale. This week, we are excited to announce two integrations that Microsoft and NVIDIA have built together to unlock industry-leading GPU acceleration for more developers and data scientists.
2019년 3월 18일 월요일
Azure Machine Learning service is the first major cloud ML service to support NVIDIA’s RAPIDS, a suite of software libraries for accelerating traditional machine learning pipelines with NVIDIA GPUs.
