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LlamaTune: Sample-Efficient DBMS Configuration Tuning., , , , , and . Proc. VLDB Endow., 15 (11): 2953-2965 (2022)Cloudy with high chance of DBMS: A 10-year prediction for Enterprise-Grade ML., , , , , , , , , and 12 other author(s). CoRR, (2019)Data Science Through the Looking Glass: Analysis of Millions of GitHub Notebooks and ML.NET Pipelines., , , , , , , , , and 4 other author(s). SIGMOD Rec., 51 (2): 30-37 (2022)VIP Hashing - Adapting to Skew in Popularity of Data on the Fly., , , and . Proc. VLDB Endow., 15 (10): 1978-1990 (2022)Lessons learned from the early performance evaluation of Intel optane DC persistent memory in DBMS., , , , and . DaMoN, page 14:1-14:3. ACM, (2020)MLOS: An Infrastructure for Automated Software Performance Engineering., , , , , , , , , and 2 other author(s). DEEM@SIGMOD, page 3:1-3:5. ACM, (2020)Optimizing Databases by Learning Hidden Parameters of Solid State Drives., , , and . Proc. VLDB Endow., 13 (4): 519-532 (2019)Towards Building Autonomous Data Services on Azure., , , , , , , , , and 28 other author(s). SIGMOD Conference Companion, page 217-224. ACM, (2023)Cloudy with high chance of DBMS: a 10-year prediction for Enterprise-Grade ML., , , , , , , , , and 12 other author(s). CIDR, www.cidrdb.org, (2020)From WiscKey to Bourbon: A Learned Index for Log-Structured Merge Trees., , , , , , and . OSDI, page 155-171. USENIX Association, (2020)