data science lifecycle)  2012-2018 IBM Research – Almaden, USA  Declarative large-scale machine learning  Optimizer and runtime of Apache SystemML  2011 PhD TU Dresden, Germany  Cost-based optimization Algebra for Large-Scale Machine Learning. PVLDB 2016] [Ahmed Elgohary, Matthias Boehm, Peter J. Haas, Frederick R. Reiss, Berthold Reinwald: Scaling Machine Learning via Compressed Linear Algebra. SIGMOD Large-Scale Machine Learning. VLDB Journal 2018 27(5)] [Ahmed Elgohary, Matthias Boehm, Peter J. Haas, Frederick R. Reiss, Berthold Reinwald: Compressed Linear Algebra for Large-Scale Machine Learning. Commun.

calling them through the usual function call mechanism. Inlining a function can generate more efficient object code, as long as the inlined function is small. Feel free to inline accessors and mutators { Foo f; // My ctor and dtor get called 1000000 times each. f.DoSomething(i); } It may be more efficient to declare such a variable used in a loop outside that loop: Foo f; // My ctor and dtor get called and ensure that all data members are copied. Copy and move constructors are also generally more efficient, because they don’t require heap allocation or separate initialization and assignment steps, and

#2 “Big Data”  MR/Spark: BigBench, HiBench, SparkBench  Array Databases: GenBase  #3 Machine Learning Systems  SLAB, DAWNBench, MLPerf, MLBench, AutoML Bench, Meta Worlds, TPCx-AI Experiments and text Experiments and Result Presentation [Matthias Boehm et al: SystemDS: A Declarative Machine Learning System for the End-to-End Data Science Lifecycle. CIDR 2020] 17 706.015 Introduction to Scientific Interpretation [Matthias Boehm et al: On Optimizing Operator Fusion Plans for Large-Scale Machine Learning in SystemML. PVLDB 11(12) 2018] 19 706.015 Introduction to Scientific Writing – 03 Experiments

feedback and recommendations, widen own horizon  Lots of similarities to code reviews in OSS  Learning by What NOT to Do  Accept if no time to review  The Goldilocks Method (examples, proofs, theoretical

simple cases. Definition: List comprehensions and generator expressions provide a concise and efficient way to create lists and iterators without resorting to the use of map(), filter(), or lambda. Pros: can be clearer and simpler than other list creation techniques. Generator expressions can be very efficient, since they avoid the creation of a list entirely. Cons: Complicated list comprehensions or generator membership test operators (“in” and “not in”). Pros: The default iterators and operators are simple and efficient. They express the operation directly, without extra method calls. A function that uses default operators

together (no underscores). For example, com.example.deepspace, not com.example.deepSpace or com.example.deep_space. 5.2.2 Class names Class names are written in UpperCamelCase. Class names are typically nouns