Machine Learning with SQL Server

SQL Masters Consulting is a Brisbane based SQL Server Consultancy business focusing primarily on SQL Server which allows for an in-depth knowledge of the product stack. SQL Masters Consulting can undertake a range of activities from the Architectural Design of SQL Server High Availability solutions, Performance Tuning, Troubleshooting, Upgrades and Migrations, Data Integration, Reporting, DevOPs for the Database and Training.SQL Masters Consulting gives back to the SQL Server Community by participating as conference speakers as well as speaking at local SQL Server user groups and SQLSaturday events across Australia, New Zealand, India, Singapore, USA & Canada.

SQL Masters Consulting

Thinking about getting involved with Machine Learning(ML) and are unsure what it can do? This session provides answers to the question: Where can ML provide a solution? As it is important to understand the limitations of ML, the course also reviews when other analytic processes would work better. Learn the steps to ensure that your data is suited for ML analysis and what you can do to fix it. Once attendees understand the workflow, the course will cover the processes used in algorithm selection. We will implement ML solutions in both R and Python to show the strengths and limitations of each in SQL Server 2017.
<H2> </H2>
In this session attendees will learn:
<UL>
<LI>The machine learning workflow</LI>
<LI>The techniques and algorithms used in creating solutions</LI>
<LI>The implementation techniques in R and Python and deploy solutions in SQL Server. </LI>
</UL>
Since the release of SQL Server 2017, Microsoft now supports both R and Python. In this course we will cover the strengths and weaknesses of each language so that you can make an informed choice which is the right tool for the job. During this class I will also provide the answer to a question commonly asked by people starting to learn data science, if you only are going to learn one language, R or Python, which one should you choose? 
There are some steps to follow to help simplify which algorithm to use, which can speed up the design process. Once you have created your machine learning solution, you will learn how to implement it in SQL Server, monitor the ongoing performance of SQL Server and configure the server to optimally configure the Machine Learning Services of SQL Server 2017, without adversely impacting the overall performance of SQL Server.
<H2>Attendee Requirements</H2>
No prior knowledge of Machine Learning, Data Science, R or Python is required of attendees. Please bring your laptop to be able to work through developing your own solutions in this hands-on interactive session. Ideally, your laptop should have SQL Server 2017 installed with Machine Learning Services. Links to all applications and setup guides will be provided during the course.
<H2 CLASS="MsoNormal">Course Agenda</H2>
<H3>Machine Learning Workflow</H3>
The development process for data science and machine learning is different from other development which attendees may be familiar. We will cover the steps, the order and the methods that need to be performed to find a solution. The types of questions that can be answered with data, and what needs to be done to the data to provide answers will be discussed to provide context for the hands on learning.
<H3>Introduction to the R language</H3>
The history of the language and the basic language constructs are reviewed. Hands on exercises are included to build familiarity with the concepts covered.
<H3>Application of Machine Learning with R</H3>
This section will discuss some algorithms which can be used for problem solving and walk through R code which implements a solution with one of the algorithms reviewed.
<H3>Algorithms for Decision Making</H3>
<H3>Introduction to the Python Language</H3>
The history of Python, versions and the basic language constructs are reviewed. Hands on exercises are included to build familiarity with the concepts covered.
<H3>Application of Machine Learning with Python</H3>
Additional algorithm classifications are reviewed and implemented in Python.
<H3>Machine Learning Services in SQL Server 2017</H3>
Microsoft has implemented a series of executables and Dlls which interact with R, Python and SQL Server. The internal workings of how the parts all fit together are reviewed to understand the impact of running code on SQL Server. Ongoing maintenance is reviewed to ensure that the process of running over time is considered.
<H3>Implementing a Python Solution in SQL Server</H3>
Taking one of the algorithms we discussed earlier, we will craft a solution for it, test it, then migrate the code to SQL Server. This session will cover the entire process of from beginning to end, and the code and datasets used are provided so that attendees can review the process later.
<H3>Configuration and Monitoring Performance in SQL Server</H3>
Microsoft has recommended techniques for monitoring performance of Machine Learning Services over time. Learn how to implement these configurations in SQL Server. Monitoring reports will also be provided for ongoing monitoring.
<H3>Data Visualization Solutions and their Implementation</H3>
While SQL Server is not a visual tool, there are ways to provide visual results with R and Python and in this section we will review the methods visualizations can be provided.
 
<H2>About the Speaker</H2>
Ginger Grant provides consulting services in advanced analytic solutions, including machine learning, data warehousing, ETL, reporting and cube development, Power BI, Excel Automation, Data Visualization and training. Ginger started working with data to solutions across a wide range of industries including insurance, education, healthcare, finance and transportation. She is a prolific blogger at http://www.desertislesql.com and frequent speaker at conferences and events worldwide to introduce more people to current developments and future trends in data. 
She is proficient in creating solutions using the entire Microsoft Data Stack, which includes SQL Server, T-SQL, SSIS, SSAS, Power BI and Azure Data Cloud components including Data Factory, Data Lakes, Data Analytics, IOT, Event Hubs, Machine Learning and Machine Learning Workbench and as a Microsoft Certified Trainer offers training on technical topics as well. Microsoft has recognized her technical contributions by awarding her a MVP in Data Platform.

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Machine Learning with SQL Server

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