90. Spotkanie Data Community Śląsk - w Future Processing
- 17:45 - 18:00 — Powitanie, rozpoczęcie spotkania
- 18:00 - 19:15 — Brent Ozar - Getting Better Query Plans by Improving SQL's Estimates [sesja online w języku angielskim]
- 19:15 - 19:45 — Przerwa integracyjna, networking
- 19:45 - 20:45 — Marcin Szeliga - AutoML at scale
- 20:45 - 21:00 — Zakończenie spotkania.
Wakacje, wakacje i po wakacjach. W związku z tym zapraszamy na 90 spotkanie naszego oddziału Data Community Poland. Tym razem mamy w agendzie dwóch naprawdę wyjątkowych prelegentów!
Spotykamy się w czwartek 5 września 2019 r. o 17:45 w siedzibie Future Processing - Progress Bar, Gliwice, ul. Bojkowska 37A. Miejsce jest dobrze oznakowane, nie będzie problemu trafić. Lokalizacja na mapie: https://goo.gl/maps/4vjAwhjr1oB2
Rejestracja — wstęp wolny, ale bardzo prosimy o rejestrację (i rezygnację gdy z jakichś powodów nie możesz przybyć).
Brent Ozar - Getting Better Query Plans by Improving SQL's Estimates
You've been writing T-SQL queries for a few years now, and when you have performance issues, you've been updating stats and using OPTION (RECOMPILE). It's served you well, but every now and then, you hit a problem you can't solve. Your data's been growing larger, your queries are taking longer to run, and you're starting to wonder: how can I start getting better query plans?
The secret is often comparing the query plan's estimated number of rows to actual number of rows. If they're different, it's up to you – not the SQL Server engine – to figure out why the guesses are wrong. To improve 'em, you can change your T-SQL, the way the data's structured and stored, or how SQL Server thinks about the data.
This session won't fix every query – but it'll give you a starting point to understand what you're looking at, and where to go next as you learn about the Cardinality Estimator.
I make Microsoft SQL Server go faster. I got my start in the late 1990s – first as a developer and systems administrator, then as a full time DBA. I’ve managed performance and reliability for truly tough servers: tens of terabytes, thousands of databases, thousands of queries per second.
I’m one of the rare Microsoft Certified Masters, and I’ve headlined conferences around the world including the PASS Summit, Microsoft TechEd, SQLbits, SQL Intersections, and Microsoft Ignite. My clients have included Stack Overflow, Google, and other companies whose SQL Servers you rely on every day.
Marcin Szeliga - AutoML at scale
Automating the construction and tuning of machine learning (ML) models has long been one of the goals of the ML community. This is due to several factors, most notably a sharp increase in the demand for tailored AI solutions, a relative scarcity of trained ML scientists, and the development of deep learning models with complex architectures requiring accurate design and fine-tuning. Existing automated machine learning (AutoML) techniques have been remarkably successful in identifying good parameters for a given model, sometimes even outperforming humans. However, these options either take too long to train or they work for only a handful of parameters. That’s why Azure Machine Learning uses probabilistic latent variable model to work with DNNs without needing to fully train them. Azure Machine Learning Services (AML) provides a cloud-based environment you can use to develop, train, test, deploy, manage, and track machine learning models. AutoML in the cloud will soon become mainstream. Join now.
Machine learning practitioner working with SQL Server and Azure. Since 2006 invariably awarded Microsoft Most Valuable Professional title, one of two Polish AI MVPs. Speaker at many European conferences, such as Machine Learning Prague, Data Science Summit, SQLDay, 4 Developers, SQL Nexus, SQL Saturday and Microsoft Technology Summit. Author of books and articles about Microsoft data platform, including the bestseller “Data Science and Machine Learning” and upcoming “Practical Machine Learning”.