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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.
How do you deploy multiple machine learning models in production to solve your challenge? How do enable canary releases and A/B testing? How do you make sure a user is always served by the same version of the model?
Together Power BI and Azure Synapse Analytics can handle huge datasets. This session details how the composite model and aggregation capabilities of Power BI can be used to fully exploit the scale provided by Synapse.
Taken from the 20+ years of field experiences, many common statistical and data science mistakes have been detected. Session will tackle couple of them.
SQL Server 2019 comes with Python as an installation option. Let’s use this to gather data from various sources to create your test and development data.
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
Power BI brings the concept of self-service BI, and gives a chance to incorporate Data Science models into analysis using R and Python. It also analyse data with a feature called Dataflows.
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
Session shows how to prepare data and develop models to predict the future based on time series data.
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