There’s an extensive amount of literature about how to use the various available machine and deep learning frameworks to train, save, and optimize models, most of which is extremely detailed and very focused on the programming and math aspects of the process—making it relatively easy to get up and running. However, it’s also important to consider deployment alternatives so models may be used in staging and production environments using performant, secure solutions.
Greg Werner walks you through using MXNet and TensorFlow to train deep learning models and deploy them using the leading serverless compute services in the market: AWS Lambda, Google Cloud Functions, and Azure Functions. You’ll also learn how to monitor and iterate upon trained models for continued success using standard development and operations tools. Examples will demonstrate alternatives for automating the data science pipeline using continuous integration and continuous deployment (CI/CD) tools to reduce human error, leverage battle-tested cloud infrastructure solutions, and reduce opportunity costs by quickly pushing the most effective models to production quickly and securely.
Greg Werner is the founder and CEO of 3Blades. 3Blades develops and maintains IllumiDesk (https://illumidesk.com) and Cup of Data (https://cupofdata.com). Greg has built information technology businesses his entire career. Previously, he cofounded Certsuperior, currently one of the largest web security companies by sales in Latin America, and Reachcore, a leading business-to-business supplier of document exchange services for the oil and gas, insurance, telco, and financial verticals. Greg is a co-organizer of the PyData Meetup group in Atlanta. He frequently contributes to open source projects that help the scientific community, particularly those within the Python ecosystem. Greg holds a BA in economics from Emory University, an MBA in international management from Thunderbird, and a master’s degree in computer science from the University of Illinois.
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