Outputs Distributing Workloads 2. Reproducibility Data Versioning Reproducibility For Developers Reproducibility For Developers For the Team Reproducibility For Developers For the Team For Production Back Models 3. Clarity / Organizational Trust 4. Automation (CI/CD) Summary 1. Autonomy 2. Reproducibility 3. Data Provenance 4. Automation Demo Demo Demo Demo Demo Contact Me Twitter: @samkreter

Works github.com/GoogleContainerTools/jib Pure Java Speed What benefits do we get from Jib Reproducibility github.com/GoogleContainerTools/jib Pure Java github.com/GoogleContainerTools/jib A container Docker github.com/GoogleContainerTools/jib Jib vs Docker github.com/GoogleContainerTools/jib Reproducibility github.com/GoogleContainerTools/jib Why reproducible ? Version Control Reduce variation

In-Terminal workflows ● Frequently-repeated tasks ● Scripting & automation ● Sharing workflows / reproducibility ● Customization Onboarding new K8s users https://unsplash.com/ Over 50% of survey takers

build service… MUST prevent network access while running the build steps.” With regards to reproducibility of builds, Ada Logics did not find evidence of any declaration of whether the build script is

https://stackoverflow.com/questions/42022950/which-seeds-have-to-be-set-where-to-realize-100-reproducibility-of-training-res session_conf = tf.ConfigProto(intra_op_parallelism_threads=1, inter_op_parallelism_threads=1)

axis=None) Return a random sample of items from an axis of object. You can use random_state for reproducibility. Parameters n [int, optional] Number of items from axis to return. Cannot be used with frac random elements from the Series df['num_legs']: Note that we use random_state to ensure the reproducibility of the examples. >>> df['num_legs'].sample(n=3, random_state=1) fish 0 spider 8 falcon 2 axis=None) Return a random sample of items from an axis of object. You can use random_state for reproducibility. Parameters n [int, optional] Number of items from axis to return. Cannot be used with frac

axis=None) Return a random sample of items from an axis of object. You can use random_state for reproducibility. Parameters n [int, optional] Number of items from axis to return. Cannot be used with frac random elements from the Series df['num_legs']: Note that we use random_state to ensure the reproducibility of the examples. >>> df['num_legs'].sample(n=3, random_state=1) fish 0 spider 8 falcon 2 axis=None) Return a random sample of items from an axis of object. You can use random_state for reproducibility. Parameters n [int, optional] Number of items from axis to return. Cannot be used with frac

axis=None) Return a random sample of items from an axis of object. You can use random_state for reproducibility. Parameters n [int, optional] Number of items from axis to return. Cannot be used with frac random elements from the Series df['num_legs']: Note that we use random_state to ensure the reproducibility of the examples. >>> df['num_legs'].sample(n=3, random_state=1) fish 0 spider 8 falcon 2 axis=None) Return a random sample of items from an axis of object. You can use random_state for reproducibility. Parameters n [int, optional] Number of items from axis to return. Cannot be used with frac

axis=None) Return a random sample of items from an axis of object. You can use random_state for reproducibility. Parameters n [int, optional] Number of items from axis to return. Cannot be used with frac random elements from the Series df['num_legs']: Note that we use random_state to ensure the reproducibility of the examples. >>> df['num_legs'].sample(n=3, random_state=1) fish 0 spider 8 falcon 2 axis=None) Return a random sample of items from an axis of object. You can use random_state for reproducibility. Parameters n [int, optional] Number of items from axis to return. Cannot be used with frac

ignore_index=False) Return a random sample of items from an axis of object. You can use random_state for reproducibility. Parameters n [int, optional] Number of items from axis to return. Cannot be used with frac random elements from the Series df['num_legs']: Note that we use random_state to ensure the reproducibility of the examples. >>> df['num_legs'].sample(n=3, random_state=1) fish 0 spider 8 falcon 2 ignore_index=False) Return a random sample of items from an axis of object. You can use random_state for reproducibility. Parameters n [int, optional] Number of items from axis to return. Cannot be used with frac