DataXDay 2020
| title | author | length |
|---|---|---|
| Python SDK to train, package and deploy models | Paul Péton | 42m |
Shows an end-to-end MLOps workflow using the Azure ML Python SDK: it connects a workspace to a dataset, runs experiments on a compute target, uses HyperDrive… | ||
| The whole truth about using deep learning in medical imaging | Pierre-Antoine Ganaye | 47m |
The talk presents replacing legacy patch-based segmenters with a U-Net to cut brain MRI inference time from about 10 minutes to 1 second while maintaining… | ||
| Delta Lake has competition | Alban Phélip | 25m |
Compares Delta Lake, Apache Hudi and Apache Iceberg using runnable Spark examples on small datasets to show what works in practice: Delta Lake provides a DSL… | ||
| Unveiling the secrets of a machine learning model | Johan Jublanc, Giulia Bianchi | 48m |
Describes a GAN-based model inversion method that starts from a name vector and optimizes a latent code by backpropagating through a frozen generator and… | ||
| Let’s Build a Streaming Data Pipeline! | Robin Moffatt | 49m |
Walks through a working streaming pipeline built with Apache Kafka and ksqlDB, showing Kafka Connect for CDC from MySQL, Avro + Schema Registry for… | ||
| Back to fundamentals: from databases to event-driven platforms | Florent Ramière | 43m |
Describes converting a monolithic SQL database into a distributed commit log by using change-data-capture (CDC) to publish events to Kafka so multiple teams… | ||
| What is DataOps? Becoming a Truly Data-Driven Organization | Samia Drappeau | 37m |
DataOps presents making data a product through culture, processes and tools, with a core emphasis on "tests first" for data and transformations to restore user… | ||
| DevOps for Machine Learning - why is it different? | Ryan Dawson | 44m |
Claim: machine learning fails in ops because 80–90% never go live and the bulk of effort is data, training and monitoring not code. Demonstrated tools and… | ||
| Semi-supervised deep learning with GANs | Olga Petrova | 36m |
Claims that semi‑supervised GANs turn 100 labeled images plus ~13,000 unlabeled ones into a practical classifier: baseline ResNet18 on 100 labels hits ~60% but… | ||
| Automatic retraining of TensorFlow models | Sarah, Julien Cheillan | 38m |
An automated retraining pipeline uses an Airflow DAG to trigger AWS SageMaker jobs that read feature CSVs from S3, run TensorFlow Docker images stored in ECR… | ||
| From On-Premise to Cloud | Franck Cussac | 24m |
A team migrated a legacy on-prem Hadoop setup running about 10 daily Spark batch jobs (HDFS, Hive metastore, Zeppelin) to AWS, evaluating S3+Glue against EMR… | ||
| Open Source Reliability for Data Lakes | Seifeddine Saafi | 19m |
Delta Lake is an open source Parquet-based layer that adds a transactional log and ACID semantics so writes are atomic and updates or failed jobs do not leave… | ||
| AI for every language | Daniel Whitenack | 51m |
Claims a beta language‑ID API that covers over 1,300 languages and demos it against existing Python packages, showing many regional tongues are missed… | ||
| Internals of Speeding up PySpark with Arrow | Ruben Berenguel | 46m |
Explains exactly what changes: PySpark uses Apache Arrow record batches to avoid pickling, enabling record batches / zero-copy conversion between JVM and… | ||
| Future of Software Testing: Machine Learning Assistance | Mesut Durukal | 25m |
Claims that machine learning helps every testing stage and shows concrete prototypes: an API-driven test generator learns input–output patterns and emits… | ||
| Which Serverless Prediction Platforms on Google Cloud Platform? | Guillaume Blaquiere | 37m |
Compares Google Cloud options for serving TensorFlow models: recommends Cloud Run for online prediction because it provides container portability… | ||
| Parallelising pandas with | Niall Turbitt | 45m |
Koalas implements the pandas API on Apache Spark, so one line change import databricks.koalas as ks gives pandas-like code but runs distributed. Project claims… | ||
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