
Models: From the Lab to the Factory
Deploying a model without a rigorous process in place has consequences. We go over techniques for successful deployment and management.
With years of experience working with cloud computing and distributed data architectures, Mauricio is passionate about creating value with technology. He is an industry-recognized leader in technical architecture for cloud-hosted data solutions.
Mauricio is a Senior Data Engineer at Silicon Valley Data Science. He has experience working with distributed storage and processing systems such as Hadoop, Spark, Cassandra and related tools in the ecosystem; application and web services development in Spring Java and Python; and designed and built cloud technical architectures in AWS and NTT. Mauricio has deployed models into production built using Spark, R, Impala, Hive, and other tools and works to bridge the gap between model development and deployment. Prior to joining SVDS, Mauricio was a technical architecture manager working in Accenture’s R&D group and Big Data practice. He managed a team of data scientists and engineers to build a web-scale recommender and network analytics streaming services on cloud infrastructure and presented the work at Strata and DataStax NYC* conferences. He was also a main developer in Accenture’s Cloud Platform which is used in over 30 client solutions and over 1600 managed servers. He has experience working with clients in the retail, healthcare, and banking industries.
Mauricio holds a Masters of Science in Computer Engineering from the University of Florida.
Deploying a model without a rigorous process in place has consequences. We go over techniques for successful deployment and management.
This post will show architects and developers how to set up Hadoop to communicate with S3, use Hadoop commands directly against S3, use distcp to perform transfers between Hadoop and S3, and how distcp can be used to update on a regular basis based only on differences.
While on paper it should be a seamless transition to run Impala code in Hive, in reality it’s more like playing a relentless game of whack-a-mole. This post provides hints to make the transition easier.
We’ll be in Boston covering a variety of topics—from running agile data teams, to visual storytelling with data. Let us know if you’ll be there, or sign up to receive all our slides.