of CPU core and network capacity allocated to a function proportionally Is your code CPU, network or memory-bound? If so, it could be cheaper to choose more memory > Memory, > Cores, > Network
needed SDK modules Put your dependency (e.g. jar files) in a separate directory Improve dependency injection with smaller and simpler IoC frameworks that load quickly on startup, like Dagger2 Leverage smaller and faster frameworks like jackson-jr for Java data binding Use environment variables to modify operational behavior Secure secrets/tokens/passwords with Parameter Store and AWS Secrets Manager
simplify serverless apps New serverless resource types: functions, APIs, and tables Local testing with SAM CLI github.com/awslabs/serverless-application-model
concurrent invocations of Lambda function Batch size sets maximum # of records per invocation (min 1, max 10K) Data Stream Processor Function Streaming source Other AWS services
STREAM "device_id", STEP("SOURCE_SQL_STREAM_001".ROWTIME BY INTERVAL '10' MINUTE) as "window_ts", SUM("measurement") as "sample_sum", COUNT(*) AS "sample_count" FROM "SOURCE_SQL_STREAM_001" GROUP BY "device_id", STEP("SOURCE_SQL_STREAM_001".ROWTIME BY INTERVAL '10' MINUTE); Kinesis Data Analytics Aggregation 10-minute tumbling window Kinesis Data Analytics: Time window aggregation Source stream Destination stream(s)
data Structured, semi-structured, and unstructured data Decoupled compute and storage Fast automated ingestion Schema on-read Complementary to data warehouses
scanned: 169.53GB Cost*: $0.85 * $5/TB or $0.005/GB SELECT gram, year, sum(count) FROM ngram WHERE gram = 'just say no' GROUP BY gram, year ORDER BY year ASC;
California, Berkeley) Up to 40 TFLOPS of peak compute power Over 700 GB/sec of read and 500 GB/sec of write performance using S3 “numpywren: Serverless Linear Algebra” https://arxiv.org/pdf/1810.09679.pdf
K S & I N F R A S T R U C T U R E The Amazon ML Stack: Broadest & Deepest Set of Capabilities A I S E R V I C E S R E K O G N I T I O N I M A G E P O L L Y T R A N S C R I B E T R A N S L A T E C O M P R E H E N D C O M P R E H E N D M E D I C A L L E X R E K O G N I T I O N V I D E O Vision Speech Chatbots A M A Z O N S A G E M A K E R B U I L D T R A I N F O R E C A S T T E X T R A C T P E R S O N A L I Z E D E P L O Y Pre-built algorithms & notebooks Data labeling (G R O U N D T R U T H ) One-click model training & tuning Optimization ( N E O ) One-click deployment & hosting M L S E R V I C E S F r a m e w o r k s I n t e r f a c e s I n f r a s t r u c t u r e E C 2 P 3 & P 3 d n E C 2 C 5 F P G A s G R E E N G R A S S E L A S T I C I N F E R E N C E Models without training data (REINFORCEMENT LEARNING) Algorithms & models ( A W S M A R K E T P L A C E ) Language Forecasting Recommendations NEW NEW NEW NEW NEW NEW NEW NEW NEW
Chatbot Processing DynamoDB: Customer Data SNS: SMS Messaging Customer calls Connect to reschedule an appointment Connect calls Lex chatbot Lex chatbot calls Lambda function to get customer preferences and fulfil Intents Lambda function sends text message confirmation via SNS Customer receives appointment confirmation text message Lambda function writes updates to DynamoDB
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