Check out my reading below!
Companies are spending billions on machine learning projects, but it’s money wasted if the models can’t be deployed effectively. In this practical guide, Hannes Hapke and Catherine Nelson walk you through the steps of automating a machine learning pipeline using the TensorFlow ecosystem.
Aug 1, 2024
The low cost of getting started with cloud services can easily evolve into a significant expense down the road. That’s challenging for teams developing data pipelines, particularly when rapid changes in technology and workload require a constant cycle of redesign.
Aug 1, 2024
Designing Data-Intensive Applications by Martin Kleppmann is a comprehensive guide to understanding the architecture of data-driven systems. It breaks down complex topics like data modeling, distributed systems, and database design in a highly accessible way.
Aug 1, 2024
Data pipelines are the foundation for success in data analytics. Moving data from numerous diverse sources and transforming it to provide context is the difference between having data and actually gaining value from it.
Aug 1, 2024
Database Internals by Alex Petrov is a deep dive into the inner workings of modern databases, offering a detailed look at topics like storage engines, indexing, and query processing. The book covers a wide range of database types, from relational to NoSQL, and explains complex concepts like consensus algorithms, transaction management, and data replication.
Aug 1, 2024
Database Internals by Alex Petrov is a deep dive into the inner workings of modern databases, offering a detailed look at topics like storage engines, indexing, and query processing. The book covers a wide range of database types, from relational to NoSQL, and explains complex concepts like consensus algorithms, transaction management, and data replication.
Aug 1, 2024
Data fabric, data lakehouse, and data mesh have recently appeared as viable alternatives to the modern data warehouse. These new architectures have solid benefits, but they’re also surrounded by a lot of hyperbole and confusion.
Aug 1, 2024
Database Internals by Alex Petrov is a deep dive into the inner workings of modern databases, offering a detailed look at topics like storage engines, indexing, and query processing. The book covers a wide range of database types, from relational to NoSQL, and explains complex concepts like consensus algorithms, transaction management, and data replication.
Aug 1, 2024
If you’re training a machine learning model but aren’t sure how to put it into production, this book will get you there. Kubeflow provides a collection of cloud native tools for different stages of a model’s lifecycle, from data exploration, feature preparation, and model training to model serving.
Aug 1, 2024
Streaming data is a big deal in big data these days. As more and more businesses seek to tame the massive unbounded data sets that pervade our world, streaming systems have finally reached a level of maturity sufficient for mainstream adoption.
Aug 1, 2024
Streaming data is a big deal in big data these days. As more and more businesses seek to tame the massive unbounded data sets that pervade our world, streaming systems have finally reached a level of maturity sufficient for mainstream adoption.
Aug 1, 2024