When developing the in-database solution and creating it for continuous development (and also deployment), several aspects should be taken into consideration. First of all, the environment where data scientists will be working. You might give them a powerful, standalone server or even allocate proper seats in the cloud. They will need it, especially when training the model. This is extremely important, as you don't want to have your highly-paid statisticians and mathematicians wait for the models to compute and generate. So, enabling the route to a highly scalable CPU and RAM powerful computations is a must. Second to this, you have to get the data there. Whether it's on cloud or on premises, getting data there (and later also, back) should not be overlooked, as this might also be the point where you will lose precious time. And...
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You're reading from SQL Server 2017 Machine Learning Services with R.
Julie Koesmarno is a senior program manager in the Database Systems Business Analytics team, at Microsoft. Currently, she leads big data analytics initiatives, driving business growth and customer success for SQL Server and Azure Data businesses. She has over 10 years of experience in data management, data warehousing, and analytics for multimillion-dollar businesses as a SQL Server developer, a system analyst, and a consultant prior to joining Microsoft. She is passionate about empowering data professionals to drive impacts for customer success and business through insights.
Read more about Julie Koesmarno
Toma Katrun is a SQL Server developer and data scientist with more than 15 years of experience in the fields of business warehousing, development, ETL, database administration, and query tuning. He holds over 15 years of experience in data analysis, data mining, statistical research, and machine learning. He is a Microsoft SQL Server MVP for data platform and has been working with Microsoft SQL Server since version 2000. He is a blogger, author of many articles, a frequent speaker at the community and Microsoft events. He is an avid coffee drinker who is passionate about fixed gear bikes.
Read more about Tomaž Kaštrun
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Julie Koesmarno is a senior program manager in the Database Systems Business Analytics team, at Microsoft. Currently, she leads big data analytics initiatives, driving business growth and customer success for SQL Server and Azure Data businesses. She has over 10 years of experience in data management, data warehousing, and analytics for multimillion-dollar businesses as a SQL Server developer, a system analyst, and a consultant prior to joining Microsoft. She is passionate about empowering data professionals to drive impacts for customer success and business through insights.
Read more about Julie Koesmarno
Toma Katrun is a SQL Server developer and data scientist with more than 15 years of experience in the fields of business warehousing, development, ETL, database administration, and query tuning. He holds over 15 years of experience in data analysis, data mining, statistical research, and machine learning. He is a Microsoft SQL Server MVP for data platform and has been working with Microsoft SQL Server since version 2000. He is a blogger, author of many articles, a frequent speaker at the community and Microsoft events. He is an avid coffee drinker who is passionate about fixed gear bikes.
Read more about Tomaž Kaštrun