10 June 2019
Data scientists have a dynamic role. They need environments that are fast and flexible while upholding their organization’s security and compliance policies. Notebook Virtual Machine (VM), announced in May 2019, resolves these conflicting requirements while simplifying the overall experience for data scientists.
04 June 2019
The automated machine learning capability in Azure Machine Learning service allows data scientists, analysts, and developers to build machine learning models with high scalability, efficiency, and productivity all while sustaining model quality.
14 May 2019
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.
09 May 2019
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.
06 May 2019
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.
01 May 2019
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 what you’ve previously watched or purchased, you’ve interacted with a recommendation system.
29 April 2019
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.
09 April 2019
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.
01 April 2019
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.
20 March 2019
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).