Azure Machine Learning [ML] is a great tool for providing deep analytical data analysis and can provide a great learning environment to those people who are just getting started with learning machine learning concepts as well as those who want to deploy complex models. This session show how Azure ML can assist in picking the best algorithm in with AutoML as well has how Azure ML can deploy models created from any source and then monitor the ongoing performance of the model over time.
Ginger Grant is a Data Platform MVP who provides consulting services in advanced analytic solutions, including machine learning, data warehousing, and Power BI. She is an author of articles, books, and at DesertIsleSQL.com and uses her MCT to provide data platform training in topics such as Python, R and Azure Machine Learning.
We seek to provide a respectful, friendly, professional experience for everyone, regardless of gender, sexual orientation, physical appearance, disability, age, race or religion. We do not tolerate any behavior that is harassing or degrading to any individual, in any form. The Code of Conduct will be enforced.
All live stream organizers using the Global Azure brand and Global Azure speakers are responsible for knowing and abiding by these standards. We encourage every organizer and attendee to assist in creating a welcoming and safe environment. Live stream organizers are required to inform and enforce the Code of Conduct if they accept community content to their stream.
If you are being harassed, notice that someone else is being harassed, or have any other concerns, report it. Please report any concerns, suspicious or disruptive activity or behavior directly to any of the live stream organizers, or directly to the Global Azure admins at email@example.com. All reports to the Global admin team will remain confidential.
We encourage local organizers to set up and enforce a Code of Conduct for all Global Azure live stream. A good template can be found at https://confcodeofconduct.com/, including internationalized versions at https://github.com/confcodeofconduct/confcodeofconduct.com. An excellent version of a Code of Conduct, not a template, is built by the DDD Europe conference at https://dddeurope.com/2020/coc/.