Tony McGovern
Founder and Data Scientist at emdata.ai
Washington, Washington, D.C., United States
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Hacker. Artist. Storyteller. Data lover.
Using machine learning models and data mining techniques, I solve hard, analytical problems for my clients to create meaningful insight out of data. I find the whole process of discovering, cleaning, analyzing, and visualizing data extremely rewarding. I have both an unending love for cutting-edge technologies and a dogged determination to solve the difficult problems others run away from.
Area of Expertise
Topics
Building Robust ETL Pipelines with Power BI Dataflows
Power BI dataflows gives us the ability to run data transformation activities at scale while providing a single view for scheduling, monitoring, and managing complex ETL pipelines. In this session, see how simple it is to build robust, low-code ETL pipelines with Power BI dataflows. We'll walk through standard data transformation activities using APIs as our source data and landing that data as fact and dimension tables on the Power BI Service.
Choosing the Right Data Science Services in Azure: Scale, Automate, and Deploy with Ease
Microsoft Azure offers a multitude of cloud-based products that simplify creating solutions that solve your analytical problems with machine learning and AI. Together these products help your team with all aspects of the data science process.
With Choosing the Right Data Science Services in Azure: Scale, Automate, and Deploy with Ease, we unpack the challenge in determining which Azure data science product is best suited to solve your analytical problems. We explore the most popular machine learning and AI products and describe how each fit into a typical machine learning workflow.
Our machine learning workflow covers the full lifecycle of the data science process from data acquisition to scaling and serving finished models on production systems. Within each of the tasks in the process, we explore the right Azure products that help us easily scale our solutions.
We also explore how to right-size your data science team to best utilize each product so you’re solving problems as quickly and cheaply as possible.
The materials here will help you understand how to choose the set of Azure machine learning and AI products that fit your data science team within a typical machine learning workflow, allowing you to scale, automate, and deploy analytical solutions with ease.
For more information, check out our GitHub repo:
https://github.com/emdata-design/azure-data-science
Building Robust ETL Pipelines with Data Flows in Azure Data Factory
Azure Data Factory automates data workflows in the cloud, giving us the ability to run data transformation activities at scale while providing a single view for scheduling, monitoring, and managing complex ETL pipelines.
In this session, see how simple it is to build robust, low-code ETL pipelines on Azure with Data Flows in Azure Data Factory. We'll walk through standard data transformation activities for a modern data warehouse using APIs as our source data and landing that data as fact and dimension tables on an Azure SQL database.
Power BI Days DC 2020-02 Sessionize Event
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