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 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.
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.
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.
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.
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
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
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
Learn how to make Azure do the work for you by developing smart responses for Azure SQL DB alerts using Automation and Logic Apps.