When you build a machine learning model, the first step is always to load your data. Typically this means downloading files from object storage, or querying a database. To speed up the process, why not build the model inside the database so that you don’t have to move the information? In this episode Paige Roberts explains the benefits of pushing the machine learning processing into the database layer and the approach that Vertica has taken for their implementation. If you are looking for a way to speed up your experimentation, or an easy way to apply AutoML then this conversation is for you.
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- Hello and welcome to the Data Engineering Podcast, the show about modern data management
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- We’ve all been asked to help with an ad-hoc request for data by the sales and marketing team. Then it becomes a critical report that they need updated every week or every day. Then what do you do? Send a CSV via email? Write some Python scripts to automate it? But what about incremental sync, API quotas, error handling, and all of the other details that eat up your time? Today, there is a better way. With Census, just write SQL or plug in your dbt models and start syncing your cloud warehouse to SaaS applications like Salesforce, Marketo, Hubspot, and many more. Go to dataengineeringpodcast.com/census today to get a free 14-day trial.
- Your host is Tobias Macey and today I’m interviewing Paige Roberts about machine learning workflows inside the database
- How did you get involved in the area of data management?
- Can you start by giving an overview of the current state of the market for databases that support in-process machine learning?
- What are the motivating factors for running a machine learning workflow inside the database?
- What styles of ML are feasible to do inside the database? (e.g. bayesian inference, deep learning, etc.)
- What are the performance implications of running a model training pipeline within the database runtime? (both in terms of training performance boosts, and database performance impacts)
- Can you describe the architecture of how the machine learning process is managed by the database engine?
- How do you manage interacting with Python/R/Jupyter/etc. when working within the database?
- What is the impact on data pipeline and MLOps architectures when using the database to manage the machine learning workflow?
- What are the most interesting, innovative, or unexpected ways that you have seen in-database ML used?
- What are the most interesting, unexpected, or challenging lessons that you have learned while working on machine learning inside the database?
- When is in-database ML the wrong choice?
- What are the recent trends/changes in machine learning for the database that you are excited for?
- From your perspective, what is the biggest gap in the tooling or technology for data management today?
- Thank you for listening! Don’t forget to check out our other show, Podcast.__init__ to learn about the Python language, its community, and the innovative ways it is being used.
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- Infoworld – 8 databases supporting in-database machine learning
- Power BI
- K-Means Clustering
- MPP == Massively Parallel Processing
- Random Forest
- PMML == Predictive Model Markup Language
- SVM == Support Vector Machine
- Naive Bayes
- Neural Magic
- Tensorflow Frozen Graph
- CNCF == Cloud Native Computing Foundation
- Hotel California
- Jupyter Notebook
- Unifying Analytics Presentation
- Holden Karau
- Vertica Academy