Amazon announces new machine learning services and analytic solutions at AWS re:Invent
Amazon is tackling everything from analytics to machine learning and time series forecasting at its AWS re:Invent 2018 conference this week. The company made a number of new announcements to help developers to get started with non-virtual environments, artificial intelligence, and gain deeper insights.
Amazon Firecracker allows for microVMs to be launched in non-virtual environments
It launched Firecracker, an open-source solution that will allow lightweight micro-virtual machines to be launched in non-virtual environments. Firecracker combines the security and workload isolation of traditional VMs with the resource efficiency of containers.
According to AWS, it uses multiple levels of isolation, resulting in minimal attack surface. It can also be used to power high-volume AWS services, such as AWS Lambda and AWS Fargate.
CloudWatch Logs Insights
The company also announced CloudWatch Logs Insights to provide better insight into service log data. The solution is able to work through massive logs in a short period of time and provide interactive queries and visualizations.
According to the company, it uses a sophisticated query language that features commands that can fetch specific event fields, filter based on conditions, calculate aggregate statistics, sort on a desired file, and limit the number of events that a query returns.
Support for Java in Amazon Kinesis Data Analytics
The company also announced that Amazon Kinesis Data Analytics now supports Java, enabling developers to write their own Java code to create applications for processing streaming data.
There are two open-source libraries that can be used to build these applications: Apache Flink and AWS SDK for Java.
According to AWS, developers will be able to use their chosen IDE and integrate with AWS services such as Amazon Kinesis Data Streams, Amazon S3, Amazon DynamoDB, and Amazon Kinesis Data Firehose.
Amazon DynamoDB transactions
Transactions have been added to DynamoDB, providing developers with atomicity, consistency, isolation, and durability (ACID).
Transactions in DynamoDB will enable use cases such as processing financial transactions, fulfilling and managing orders, building multiplayer game engines, and coordinating actions across distributed components and services.
Amazon has added two new DynamoDB operations to handle these transactions. TransactWriteItems is a batch operation with a write set that can check for prerequisite conditions that need to be satisfied before updates are made. TransactGetItems is a batch operation with a read set.
AWS launches Private Marketplace to improve governance over the AWS Marketplace
In order to improve governance of software procurement, AWS is launching Private Marketplace. Private Marketplace allows the creation of a custom digital catalog of pre-approved solutions from the AWS Marketplace.
Administrators will be able to select products that meet procurement policies, as well as customize Private Marketplace with company branding. Controls for Private Marketplace will apply to the entire organization, and further controls can be set up for certain roles using Identity and Access Management.
Amazon SageMaker Neo improves machine learning processes
The company is also introducing a new solution to improve machine learning processes. Amazon SageMaker Neo allows machine learning models to train once and then run anywhere in the cloud or at the edge.
Amazon SageMaker Neo optimizes machine learning models deployed on Amazon EC2 instances, Amazon SageMaker endpoints, and devices managed by AWS Greengrass.
“We want to help all of our customers embrace machine learning, no matter their size, budget, experience, or skill level,” said Swami Sivasubramanian, Vice President, Amazon Machine Learning. “Today’s announcements remove significant barriers to the successful adoption of machine learning, by reducing the cost of machine learning training and inference, introducing new SageMaker capabilities that make it easier for developers to build, train, and deploy machine learning models in the cloud and at the edge, and delivering new AI services based on our years of experience at Amazon.”
Amazon Forecast improves time series forecasting
Finally, Amazon announced a new solution that will make time series forecasting easier. Amazon Forecast is a fully managed deep learning service that can be used to generate predictions on time series data.
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