Terry McCann's Speaker Profile @ Sessionize

Using AI to write conference session submissions

Deep learning has been used to write new Shakespearean sonnets, to imagine new delicious recipes, write hilarious Harry Potter novels and even come up with new names for beer! In this session we will understand, what is deep learning, what are neural nets, what are the steps required to build a deep learning model and look at some of the great examples mentioned.

We will then turn our new skills to the problem most speakers have! Writing session abstracts. Together we will develop a recursive neural net designed to generate new session abstracts, entirely based on previously submitted sessions to SQL Server conferences. Will we be able to produce a session you would have attended? Come along and fine out.

Production Python Machine Learning in SQL Server

SQL Server 2017 introduced support for Python. In this session we will look at creating a model in Python, from there we will look at how Python integrates in to SQL Server. We will build a model and then serialise in inside SQL Server, ready to be scored.

This session is more than an introduction to Python or SQL Server. In this session we will make a model and deploy it - that is a big deal.

Machine Learning in Azure: Everything you need to know

Getting started with Data Science in Azure is often quite daunting. There are a huge variety of different options to select and knowing which one to use and when depends a lot on your current skills and what you are trying to achieve. Then there is the added complication that the tools we are using are constantly evolving and improving. It is understandable that getting started is difficult.

In this full day session we will explore everything you need to know to get started with Machine Learning in Azure. We will start the day getting you up to speed with Machine Learning. We will explore the Machine Learning flow and get to know the data we will be working with. From there we will five in to Azure Machine Learning Services. We will spend most of our time here, looking in to working with data, mounting data and creating datasets and diving deep on Azure Machine Learning's workspace.

We will then define the term "Citizen Data Scientist" and build a series of models together, initially in AutoML and then using the Machine Learning designer (a GUI interface for building models). Which should you use and when? We will look at which is best under which circumstances and how to get the best out of each type.

Once we have mastered the skills of the "Citizen data scientist" we will look at how an experienced Data Scientist develops models. We will initially look at compute backed notebooks in Azure ML before moving on to look at how to build models at scale in Azure Databricks. Throughout the day we will also look at scenarios for tracking with either the tracking services in Azure ML or MLFlow in Databricks.

Join Terry McCann, Director of AI and Microsoft MVP for AI as we tackle a day full of demos and labs looking at everything you need to know to get started with Machine Learning in Azure

Machine Learning in Azure Synapse

Revised for 2022! There is a lot of content available on Synapse for Data Engineering, but what about Machine Learning? In this session we will look at how to train models in Azure Synapse with SparkML, AutoML, and cogntive services.

Getting started with Machine learning in Python

Everyone needs to start learning machine learning, by 2020 80% of all applications will be powered by a form Artificial Intelligence (Machine learning - don’t worry the robots are not rising). Machine learning is no longer just for data scientists, everyone working with data need to have a basic awareness of machine learning. If you work with data, you should be investing in machine learning.

In this session we will look at Python as a language and explore its packages for interactive machine learning. Terms like SkLearn, Pandas, SciPy, Pickle will become familiar to you by the end of this session. You won't be an expert in machine learning but you will know how to get started with Python. This session will touch on Python for SQL Server, but our focus will be developing models using Python.

Get there faster with Machine Learning in Azure Synapse

Azure Synapse is Microsoft's unified data analytics platform. You will see a lot about the great problems it solves in data Engineering and with Data Lakehouse Architectures. But this is never the end of the story. A data lake needs to be distilled and refined in to its predictive potential. This is where Machine Learning comes in to play. Each month there are new and interesting features added to Azure Synapse. One area which is getting more and more attention is Machine Learning. In this full day session we go from zero to hero in not only Azure Synapse but most importantly how you build models for production in Azure Synapse.

This session is a lab heavy session. You will need to bring a laptop with you, with an Azure subscription. We start the day looking at an overview of Azure Synapse, what it is and how it works. From there we move to explore how Synapse implements Machine Learning. The morning will focus on the integrations with AutoML and cognitive services before we move to train models from scratch in Python and PySpark. Along the way we will talk about model management and an increasingly important topic, MLOps.

Learn from industry experts and Microsoft MVPs Advancing Analytics in this full day session. This session is great for those who are new to Synapse, new to Machine learning but know some Synapse or new to both.

From idea to production: Data Science end-to-end with Databricks

Have you ever wondered what happens end-to-end to take a Machine Learning model from an idea in to production, then this is the session for you. Already building Machine Learning models but not used Databricks, well then this session is for you too. This session is designed to demonstrate all the steps to take a model in to production.

This session is a lab heavy session. We will cover the basics of Spark and Databricks, before looking at how to train a model using both Spark and Python. We will look at how Databricks supports both the pro-code and the low-code and when to use one over the other. We will look at common techniques to explore our data and get the most from it, from there we will move on to producitonise our model using MLOps with MLFlow.

This session was created based on feedback and engagements with our customers at Advancing Analytics. This course is an applied course which is designed to not only teach you how to implement a technology, but also why and most importantly why not. This session is delivered by Microsoft Gold Partners, Microsoft MVPs and also award winning Databricks partners.

Exploring Graph structures with the CosmosDB Gremlin API

Analysing highly connected data using SQL is hard! Relational databases were simply not designed to handle this, but graph databases were. Built from the ground up to understand highly connected data, graph databases enable a flexible performant way to interrogate and traverse highly connected data.

If you have looked at graph processing in SQL Server then you will know it has limitations. Well Microsoft developer has a few alternatives, one of those being the Gremlin API in CosmosDB. In this session, we seek to explore, what is a graph database, why you should be interested, what query patterns does they solve and how to use Gremiln's fantastic language.

Establishing a Machine Learning capability in 2021

Neglecting Data Science will be the biggest cause for companies ceasing trading over the next 5 years. To complete in today's market, you need to innovate, or risk being left behind.

The challenge for most enterprises is how do I get started? What roles should I hire? Where could Machine Learning help me? What project should we tackle first? How do I convert theory into ROI driven applications?

In this session we tackle all of the above. You will leave this session with the high-level objectives to start machine learning in your organisation.

Driving engagement with Recommendation Systems

In this session we look at some of the approaches to building an AI driven recommendation system and address how recommendations will drive better engagement in your platform.

We will explore the options for recommendations in Azure and look at a worked example.

Docker & Kubernetes for the Data Scientist

Deploying Machine Learning models is known as the hardest problem in Data Science. Too many models live and die on a developers machine. We need a way to deploy our models in a repeatable way. In this session we will look at the basics and the history of Docker. We will build a Machine Learning model in Python, serialise it and containerise it.

Docker is great for packaging our applications, but we need somewhere to run it. For this we will use Kubernetes. Again we will look at the basics and history of K8s (how the kool kids write Kubernetes). We will then get our docker container running our model live and in to production.

Too few machine learning developers can deploy models, lets change this by running through all the examples together in this session.

DevOps for Data Science

As data scientists we are great at machine learning, statistical modelling, visualising data and using data to tell a story. What are we not so good at? A lot of the core skills required from traditional software development. If you answer no to any of the following you need to attend this session. Do you source control your models? Do you test your models? Is the % of models deployed in production less than 10%? In this session I will show you how to apply DevOps practices to speed up your development cycle and ensure that you have robust deployable models.

Deploy models faster with Data Science DevOps

As data scientists we are great at machine learning, statistical modelling, visualising data and using data to tell a story. What are we not so good at? A lot of the core skills required from traditional software development. Whether you're an expert or new to machine learning, this session will help you deploy models faster!

In this session I will show you how to apply DevOps practices to speed up your development cycle and ensure that you have robust deployable models. It's time to get you code source controlled, tested and deployed!

Azure Machine learning beyond the basics with Jupyter notebooks

Since the launch of Azure Machine Learning, there have been a lots published on how to get started with AML looking at basic regression, while regression is great, it only skims the surface of what machine learning is good at. In this session we go beyond the basics of regression and look at how we can clean and tune our model to boost its predictive performance. We will look at what you model is actually telling you and investigate how we can improve your accuracy. We will look at what algorithm works for what type of scenario, whether you're looking to predict, classify, recommend, cluster or segment. We will also look at why they work and what they are doing and how we can tweak their parameters to boost performance of our model. This is not Machine learning out of the box, this is applied machine learning.

Is this session for you? Terms like supervised, unsupervised learning, confusion matrix, area under the curve should be familiar to you, however you might not be familiar with how these values are calculated. That is ok. We will look at each of these.

Azure Databricks: Engineering Vs Data Science

Have you looked at Azure DataBricks yet? No! Then you need to. Why you ask, there are many reasons. The number 1, knowing how to use apache Spark will earn you more money. It is that simple. Data Engineers and Data Scientists who know apache Spark are in-demand! This workshop is designed to introduce you to the skills required to do both.

In the morning we will introduce Azure DataBricks then discuss how to develop in-memory elastic scale data engineering pipelines. We will talk about shaping and cleaning data, the languages, notebooks, ways of working, design patterns and how to get the best performance. You will build an engineering pipeline with Python (Or possibly some other stuff we are not allowed to tell you about yet). The Engineering element will be delivered by UK MVP Simon Whiteley. Simon has been deploying engineering projects with Azure DataBricks since it was announced. He has real world experience in multiple environments.

Then we will shift gears, we will take the data we moved and cleansed and apply distributed machine learning at scale. We will train a model and productionise it. We will then enrich our data with our newly predicted values. The Data Science element will be led by UK MVP Terry McCann. Terry holds an MSc in Data Science and has been working with apache Spark for the last 5 years. He is dedicated to applying engineering practices to data science to make model development, training and scoring as easy an as automated as possible

By the end of the day, you will understand how Azure Databricks supports both data engineering and data science, levering apache Spark to deliver blisteringly fast data pipelines and distributed machine learning models. Bring your laptop as this will be hands on.

Pre-requisites
An understanding of ETL processing either ETL or ELT on either on-premises or in a big data environment. A basic level of Machine Learning would also be beneficial, but not critical.
Laptop Required:Yes

Software: In the session we will be using Azure Databricks. We will have labs and demos that you can follow if you want to. If you do want to then you will need the following: - An Azure Subscription - Money on the Azure Subscription - Enough access on the subscription to make service principals. - Azure Storage explorer- PowerShell
Subscriptions: Azure

An introduction to Deep Learning in Azure

In this session we will take a gentle introduction to Deep Learning.

If you have attended a session or read a book on machine learning that did not mention Deep Learning, AI or Neural Networks then it was most likely a shallow machine learning session.

Shallow machine learning is fantastic when you need to have accountability and auditing of your machine learning models. It is great for a lot of problems, but it does require a lot of work up front. That work is feature engineering. Deep Learning is not a silver bullet by any means, but it is quite different to shallow learning and does not require the same degree of feature engineering. Neural nets, the magic behind deep learning can be shaped to work for all sorts of problems, text generation, image processing, dynamic generation, you name it, there is a neural network trying to solve it.

In this session we will look at the basics of Deep Learning. What is it, why is it deep, what problems does is solve, how do you get started and more. There is an assumption that you know a bit about machine learning, but you will still enjoy the session even if this is your first exposure to machine learning.

AI in Production

Abstract missing, due to last-minute change

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Terry McCann

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