During this session, we will talk about Azure Databricks' key features, and typical scenarios where Spark can fit, will see a lot of demos and I will share my top list of Azure Databricks best practices.
The video is not available to view online.
Machine Learning, Data Science, Artificial Intelligence. These are all big words we hear coming into our businesses lately - but what does it all mean?! Microsoft has created a set of simple and scalable tools that any developer can use and integrate into their applications super quickly! This session will focus on the various Cognitive Service offerings, where we can understand why and when we should use Pre-Build AI. Come and learn how to take advantage of these awesome services for your everyday work!
The video is not available to view online.
Delta Live Tables is a new framework available in Databricks that aims to accelerate building data pipelines by providing out of the box scheduling, dependency resolution, data validation and logging. We'll cover the basics, and then get into the demo's to show how we can: - Setup a notebook to hold our code and queries - Ingest quickly and easily into bronze tables using Auto Loader - Create views and tables on top of the ingested data using SQL and/or python to build our silver and gold layers - Create a pipeline to run the notebook - See how we can run the pipeline as either a batch job, or as a continuous job for low latency updates - Use APPLY CHANGES INTO to upsert changed data into a live table - Apply data validation rules to our live table definition queries, and get detailed logging info on how many records caused problems on each execution. By the end of the session you should have a good view of whether this can help you build our your next data project faster, and make it more reliable.
The video is not available to view online.
In this session, we will cut through the marketing buzzwords to share experiences, tips, and tricks on how to be successful with Data Science and Analytics in the real world. Tune in to hear the team share real-world experience and get takeaways from industry insiders on real projects with impact. We will also discuss the ethics and fairness of Data Science and Analytics projects and how we can be more inclusive from a technology, people, and process standpoint.
The video is not available to view online.
Only the curious will learn and only the resolute overcome the obstacles to learning.
Building a Data Quality implementation in Purview using the Glossary and Databricks
This session will discuss a custom solution we have built to solve the problem of row level encryption in a highly complex data lake.
The art of choosing the perfect set of actions to reach the ultimate value that can be achieved.
Curious what Azure Synapse Analytics brings to the table? Bring your scuba gear, as we take a dive and explore everything it has to offer!
An introduction to becoming a Data Engineer, Anna, Mikey and Ust will introduce the technology stack, tools and development skills needed for data engineering and show you how and where to go to learn them. We'll also show you how the skills you already have can kickstart your journey to becoming a Data Engineer.
This session is designed to explore some of the powerful features that made Databricks the leader in the Gartner 2021 Quadrant for Data Science and Machine Learning.
The video is not available to view online.
Tinkered with Powershell but not with Python yet? Let's get you started, and we'll do that with some practical examples focussed around talking to SQL Server and importing and exporting some data.
DBA and Data scientists should work together! Analyzing data gathered with XE and Query Store data using R or Python for better database insight and discovering hidden patterns.
The video is not available to view online.
The most challenging area of machine learning are Data acquisition, Feature extraction, Feature Selection. Almost in all data science project, 80% of time people spend in Data acquisition and Feature engineering.
AI and data is at the center of the digital feedback loops. We have invested in a comprehensive portfolio of AI tools, infrastructure and services. Come to this session to get an update of Azure AI with demos.
Taken from the 20+ years of field experiences, many common statistical and data science mistakes have been detected. Session will tackle couple of them.
The video is not available to view online.
Learn how Azure supports interactive, exploratory notebooks (e.g. Jupyter) for data processing and experimentation across a range of scales from simple single-computer work up to massively parallel Databricks clusters.
Learn to make better analytic solutions by following current thoughts on data modeling.
The video is not available to view online.
Databricks, Lakes & Parquet are a match made in heaven, but explode with extra power when using Delta Lake. This session will dive into the details of how Databricks Delta works and how to make the most of it.
In this session, we will discover how to utilize common machine learning approaches for daily SQL Server DBA tasks.
This short session will cover some scenarios of cleansing data in Power Query using M, Python, or both languages.
In this session we focus on how Spark implements Machine Learning at Scale with Spark ML.
This session is for all developers who want to learn about the new Dev features and enhancements of SQL Server 2017 and 2019
This session provides an end-to-end walk through of how to use Azure Synapse for cloud-hosted advanced analytics, based around a real-world predictive maintenance use case.
In this session, we will introduce the Advancing Analytics Machine Learning Canvas and how it can be used to capture requirements for Machine Learning Projects.
Learn how Tailwind Traders data science team uses Azure Machine Learning features and services to create bespoke open source NLP models and optimise them. Includes Automated ML, Azure ML SDK and Hyperparameter tuning
Azure now has two slick, platform-as-a-service spark offerings, but which one should you choose? A separate specialist tools or a one-size-fits-all solution? Join Simon as he compares and contrasts the spark offerings.
Within Azure we have a rich ecosystem of AI services that can be leveraged to gain new insights into your data. This session will give you an easy to digest breakdown of the key services that matter and how to approach each one. Cognitive Services, Bot Framework, Azure Machine Learning Studio, Databricks, Notebooks, the Azure ML SDK for Python and the Azure ML Service
The video is not available to view online.
We will review this new feature of ADFv2, do deep dive to understand the mentioned techniques, compare them to SSIS and/or T-SQL and learn how modelled data flow runs Scala behind the scenes.
In this session, we’ll look at the different options within the Cognitive Services suite, show you how to connect to the APIs using Python code, walk through a live bot demo, and build an Azure Cognitive Search index. You should leave this session feeling like you’ve had a jump start to further your AI developer skill set.
We’ll take a look at how to approach making an Azure Databricks based ETL solution from start to finish. Along the way it will become clear how Azure Databricks works and we will use our SSIS knowledge to see if it can handle common use-cases
The video is not available to view online.
An end-to-end solution covering many Azure features
Azure DataBricks is a PaaS offering of Apache Spark, which allows for blazing fast data processing! How can data engineers harness the in-memory processing power? Azure DataBricks can be your data ingestion, transformation and curation tool of choice
The video is not available to view online.
Learn how to make Azure do the work for you by developing smart responses for Azure SQL DB alerts using Automation and Logic Apps.
Organisations need to know how to get started with Artificial Intelligence. This practical session offers organizations, small and large, with a helping hand in practical advice and demos using Microsoft Azure with Open Source technologies.
With a strong focus on the algorithms used in Machine Learning, we will explore the maths involved to gain a deeper understanding. Using practical examples, with Databricks to consume a dataset and Python scripts to execute the models.
The video is not available to view online.
Ever wondered how you can add the power of ML to your existing SQL estate without the need to invest in new services? Come to this session to learn about running Python and R workloads in SQL, the PREDICT function, and how to operationalise models in Azure SQL Database
The video is not available to view online.
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