How a scientist would improve serverless functions
March 2, 2020
Join us if you're curious about how to reliably improve and refactor serverless applications or how to ensure you've covered all the unexpected edge cases that occur in production. Jochem Schulenklopper and Gero Vermaas demonstrate a scientific approach that enables you to release your refactored serverless applications to production with great confidence.
Releasing improved serverless functions with confidence
March 1, 2020
Jochem Schulenklopper and Gero Vermaas explain and practice an approach that enables you to improve and release serverless functions to production with confidence. You'll make changes in some sample serverless functions running in production, deploy the improved functions to production, and analyze your improvement against the originals.
Broken Fingers: A Deep Dive Into Open Source Fingerprint Authentication and its Security Issues
February 26, 2020
Biometric authentication provides distinguished advantages over other techniques such as password-based ones; Biometric information is always with and unique to an individual, and hardly forgeable. On …
Operating a global cloud native platform
February 26, 2020
Operating cloud native infrastructure is more than just spinning up a container orchestrator. Auxiliary services are required in order to operate effectively and provide developers with a true platform experience. Josh Michielsen explores how Cond Nast operates multiple Kubernetes clusters across the world, with a focus on observability, testing, app delivery, and developer experience.
The elephant in the Kubernetes room: Team interactions
February 25, 2020
Regardless of all the technical benefits that Kubernetes brings, team interactions are still key for successfully delivering and running services. Manuel Pais explores how team design affects the success of Kubernetes adoption.
Machine learning over real-time streaming data with TensorFlow
February 23, 2020
In many applications where data is generated continuously, combining machine learning with streaming data is imperative to discover useful information in real time. Yong Tang explores TensorFlow I/O, which can be used to easily build a data pipeline with TensorFlow and stream frameworks such as Apache Kafka, AWS Kinesis, or Google Cloud PubSub.
Implementing an AI multicloud broker
February 19, 2020
Holger Kyas details the AI multicloud broker, which is triggered by Amazon Alexa and mediates between AWS Comprehend (Amazon), Azure Text Analytics (Microsoft), GCP Natural Language (Google), and Watson Tone Analyzer (IBM) to compare and analyze sentiment. The extended AI part generates new sentences (e.g., marketing slogans) with a recurrent neural network (RNN).
Public policy and deep reinforcement learning on AWS
February 18, 2020
If you've ever wondered if you could use AI to inform public policy, join Emily Webber as she combines classic economic methods with AI techniques to train a reinforcement learning agent on decades of randomized control trials. You'll learn about classic philosophical foundations for public policy decision making and how these can be applied to solve the problems that impact the many.
Start your engines: Making deep reinforcement learning accessible to all developers (sponsored by AWS)
February 18, 2020
Reinforcement learning is an advanced machine learning technique that makes short-term decisions while optimizing for a longer-term goal through trial and error. Ian Massingham dives into state-of-the-art techniques in deep reinforcement learning for a variety of use cases.
Data science and the business of Major League Baseball
February 16, 2020
Using SAS, Python, and AWS SageMaker, Major League Baseball's (MLB's) data science team outlines how it predicts ticket purchasers likelihood to purchase again, evaluates prospective season schedules, estimates customer lifetime value, optimizes promotion schedules, quantifies the strength of fan avidity, and monitors the health of monthly subscriptions to its game-streaming service.
Interactive sports analytics
February 12, 2020
Imagine watching sports and being able to immediately find all plays that are similar to what just happened. Better still, imagine being able to draw a play with the Xs and Os on an interface like a coach draws on a chalkboard and instantaneously finding all the similar plays and conduct analytics on those plays. Join Patrick Lucey to see how this is possible.
Orchestrating data workflows using a fully serverless architecture
February 11, 2020
Use of data workflows is a fundamental functionality of any data engineering team. Nonetheless, designing an easy-to-use, scalable, and flexible data workflow platform is a complex undertaking. Tomer Levi walks you through how the data engineering team at Fundbox uses AWS serverless technologies to address this problem and how it enables data scientists, BI devs, and engineers move faster.
Create your Own MySQL-as-a-Service that Runs Anywhere Using Kubernetes Operators
February 6, 2020
Kubernetes offers a unique opportunity for organizations to build a production-grade RDS-like service running on their own infrastructure: either on-premises or in the public cloud. Using Kubernetes O …
Safe and smarter driving, powered by AI (sponsored by Amazon Web Services)
February 3, 2020
Lei Pan examines how Nauto uses Amazon SageMaker and other AWS services, including Amazon Simple Notification Service (SNS) and Amazon Simple Queue Service (SQS) to continually evolve smarter data for driver behavior.
Blockchain for good
January 31, 2020
ixo is the blockchain for impact, helping individuals and organizations around the world to achieve the UN Sustainable Development Goals by 2030. Herman Smith dives into how ixo can help you count what matters and value what counts using new Web 3.0 protocols and the ixo blockchain.
Building machine learning inference pipelines at scale
January 31, 2020
Real-life ML workloads require more than training and predicting: data often needs to be preprocessed and postprocessed. Developers and data scientists have to train and deploy a sequence of algorithms that collaborate in delivering predictions from raw data. Julien Simon outlines how to build machine learning inference pipelines using open source libraries and how to scale them on AWS.
Developing intelligent robots with AWS RoboMaker
January 30, 2020
Robots are becoming prevalent in our lives, helping us carry out tedious housework, distribute warehouse inventory, automate manufacturing, and research lunar landscapes. Thomas Moulard discusses AWS RoboMaker, a new cloud robotics service that makes it easy for developers to develop, test, and deploy robotics applications and build intelligent robotics functions using cloud services.
Observability for data pipelines: Monitoring, alerting, and tracing lineage
January 27, 2020
Data-intensive applications, with many layers of transformations and movement from different data sources, can often be challenging to maintain and iterate even after they are initially built and validated. Jiaqi Liu explores how to factor in monitoring, alerting, and tracing data lineage when building data applications that move and transform data across multiple dependencies.
Open source force multipliers
January 27, 2020
Businesses that are based on open source technology are leveraging communities to get ahead of their competition. Adrian Cockcroft explores how the most successful open source-based businesses have turned the end user developer community and their partner ecosystem into a force multiplier for their own marketing and engineering teams.
The San Francisco open source voting project
January 23, 2020
In 2016, the mayor and board of supervisors of the city and county of San Francisco approved a plan that would lead to the development of open source voting technology for San Franciscos elections. Tony Wasserman provides a progress report on the development of an open source voting system to replace San Francisco's existing proprietary paper ballot voting system.
Why Amazon cares about open source (sponsored by Amazon Web Services)
January 23, 2020
Arun Gupta walks you through how AWS starts with customers and works backwards to solve their problems. Customer use of and dependencies on open source technologies have been steadily increasing over the years; this is why AWS has long been committed to open source, and its commitment to open source projects and communities continues to accelerate.