AI in Java and Scala on AWS
Download as PPTX, PDF2 likes1,462 views
The document discusses various artificial intelligence and machine learning services offered by Amazon Web Services (AWS), including Amazon Polly for text-to-speech, Amazon Rekognition for visual content analysis, and Amazon Machine Learning for building predictive models. It highlights the capabilities of these services, such as real-time processing, ease of use, and their applications in fraud detection and data analysis. Additionally, it mentions the integration of AWS tools like Amazon EMR and Apache MXNet for deep learning and big data processing.
AI in Java and Scala on AWS
- 1. © 2016, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Julien Simon AI/ML Evangelist, EMEA @julsimon AI in Java and Scala on AWS
- 2. Agenda • Artificial Intelligence At Amazon • Text-to-speech: Amazon Polly • Object and face detection: Amazon Rekognition • Machine Learning as a service: Amazon Machine Learning • Spark MLlib on Amazon EMR • Apache MXNet: Deep Learning • Resources
- 3. • Artificial Intelligence: design software applications which exhibit human-like behavior, e.g. speech, natural language processing, reasoning or intuition • Machine Learning: teach machines to learn without being explicitly programmed • Deep Learning: using neural networks, teach machines to learn from complex data where features cannot be explicitly expressed
- 4. Artificial Intelligence At Amazon Thousands Of Employees Across The Company Focused on AI Discovery & Search Fulfilment & Logistics Enhance Existing Products Define New Categories Of Products Bring Machine Learning To All
- 5. Selected customers running AI on AWS
- 6. Amazon AI for every developer
- 7. Polly: Life-like Speech Service Converts text to life-like speech 50 voices 24 languages Low latency, real time Fully managed
- 9. Polly: plain text & SSML text
- 10. Rekognition: Search & Understand Visual Content Real-time & batch image analysis Object & Scene Detection Facial Detection Face SearchFacial Analysis Visual Similarity Search Find similar faces Celebrity Detection Sports, music, movies, etc. Content Moderation Explicit, suggestive, etc.
- 15. Amazon AI for every developer
- 16. Amazon Machine Learning Easy-to-use, managed machine learning service built for developers Robust, powerful technology based on Amazon’s internal systems Create regression and classification models using your data already stored in the AWS Cloud Deploy models to production in seconds
- 17. ” “ Fraud.net Uses AWS to Quickly, Easily Detect Online Fraud Fraud.net is the world’s leading crowdsourced fraud prevention platform. Amazon Machine Learning helps us reduce complexity and make sense of emerging fraud patterns. • Needed to build and train a larger number of more targeted machine-learning models • Uses Amazon Machine Learning to provide more than 20 models • Easily builds and trains models to effectively detect online payment fraud • Reduces complexity and makes sense of emerging fraud patterns • Saves clients $1 million weekly by helping them detect and prevent fraud Oliver Clark CTO, Fraud.net ” “
- 18. Amazon Machine Learning: real-time prediction
- 19. Amazon AI for every developer
- 20. Amazon Elastic Map Reduce (EMR) • Map Reduce, Apache Spark, Presto, etc. • Launch a cluster in minutes • Open source distribution or MapR distribution • Elasticity of the cloud • Built in security features • Pay by the hour and save with Spot instances • Flexibility to customize
- 21. Integration with AWS backends Amazon DynamoDB EMR-DynamoDB connector Amazon RDS Amazon Kinesis Streaming data connectorsJDBC Data Source w/ Spark SQL ElasticSearch connector Amazon Redshift Spark-Redshift connector EMR File System (EMRFS) Amazon S3 Amazon EMR Amazon ES
- 22. Running Spark jobs on EMR Amazon EMR Step API Submit a Spark application Amazon EMR AWS Data Pipeline Airflow, Luigi, or other schedulers on EC2 Create a pipeline to schedule job submission or create complex workflows AWS Lambda Use AWS Lambda to submit applications to EMR Step API or directly to Spark on your cluster AWS Glue
- 23. Spark ML on Amazon EMR: spam detector Adapted from https://github.com/databricks/learning-spark/blob/master/src/main/scala/com/oreilly/learningsparkexamples/scala/MLlib.scala
- 24. Amazon AI for every developer
- 25. Apache MXNet: Open Source library for Deep Learning Programmable Portable High Performance Near linear scaling across hundreds of GPUs Highly efficient models for mobile and IoT Simple syntax, multiple languages Most Open Best On AWS Optimized for Deep Learning on AWS Accepted into the Apache Incubator https://mxnet.incubator.apache.org/
- 26. Apache MXNet demo: learning MNIST https://mxnet.incubator.apache.org/tutorials/scala/ https://github.com/apache/incubator-mxnet/blob/master/scala-package
- 27. Running MXNet in Spark • Perform Data processing and Deep Learning on the same cluster • Amazon EMR support GPU instances (g2 family) • You can use them for distributed training • This is still experimental • More information: https://github.com/apache/incubator-mxnet/tree/master/scala- package/spark
- 28. Amazon AI for every developer
- 29. Resources https://aws.amazon.com/ai https://aws.amazon.com/machine-learning https://aws.amazon.com/emr https://aws.amazon.com/blogs/big-data/ https://aws.amazon.com/blogs/ai/ https://github.com/aws/{aws-sdk-java, aws-scala-sdk} Code samples: https://github.com/juliensimon/aws/tree/master/ML
- 30. Thank you! Thank you! Julien Simon AI/ML Evangelist, EMEA @julsimon https://aws.amazon.com/evangelists/julien-simon/
Editor's Notes
- #12: Using AWS, C-SPAN can sample a frame every six seconds for recognition against indexed faces in a database of 97,000 people. Previously, this was done manually: Indexers scrolled through screen captures to identify who was speaking at any given point and select an image to represent each individual in each video. C-SPAN expects to save 8,000 to 9,000 hours a year in labor by automating that process using Rekognition, and will be able to index 100% of its incoming footage and archives.
- #17: Today, we have announced Amazon ML, the newest addition to the Amazon Web Services family. Amazon ML is easy to use, and intended for developers – people who are already most connected and familiar with data instrumentation, pipelines and storage/ Amazon ML is based on the same robust ML technology that is already used within Amazon’s internal systems, generating billions of predictions weekly Amazon ML is built to make it simple and reliable to use the data that you are already storing in the AWS cloud, in products like Amazon S3, Amazon Redshift and Amazon RD And lastly, Amazon ML is built to eliminate the gap between having models and using these models to build smart applications. Production deployment is only a click away – and sometimes you won’t even need that one click.
- #18: STORY BACKGROUND Fraud.net uses Amazon Machine Learning to support its machine-learning models. The company uses Amazon DynamoDB and AWS Lambda to run code without provisioning and managing servers. Uses Amazon Redshift for data analysis. SOLUTION & BENEFITS Launches and trains machine-learning models in almost half the time it took on other platforms. Reduces complexity and makes sense of emerging fraud patterns. Saves customers $1 million each week. CONTENT TAGS Main use case: Big Data, Analytics, & Business Intelligence (BI) Keywords: online fraud, fraud detection, machine learning, big data, Amazon Machine Learning, business intelligence AWS Services used: Amazon DynamoDB, Amazon Redshift, Amazon Machine Learning, Amazon S3, AWS Lambda Benefits Realized: Agility, Better Performance, Ease of Use, Lower Cost, Reliability, Scalability/Elasticity, Speed