Amazon Forecast is another ML service for an IT team’s toolbelt to help companies predict production demands, such as necessary inventory levels, along with other predictive uses. AWS users don’t need ML expertise to use the service, but official documentation is light so far, which could make this AWS time series forecasting service tough for beginners.
The managed predictive analysis service was initially unveiled at AWS re:Invent 2018. Unlike other predictive services, Forecast’s machine learning models use time as an additional dimension, which makes it particularly accurate to predict broad business trends, according to AWS. Early use cases are around resource and financial planning.
AWS positions Forecast as an easy to use, pay as you go service that doesn’t require machine learning experience. The user provides the relevant data sets, and the AWS time series forecasting service picks an appropriate machine learning algorithm to produce a forecasting model, which includes the model’s expected accuracy.
Users with machine learning experience can bring their own custom algorithm and will likely want to add more data and retrain the model to improve on its initial expected accuracy. Unfortunately, those who want to really dig into Forecast documentation won’t find much yet. The most detailed guides are currently on GitHub.
Rekognition adds “Fear” face analysis capability
AWS improved its facial analysis service Rekognition amid increased backlash over Amazon’s involvement with U.S. government agencies such as Immigration and Customs Enforcement. This month, AWS improved Rekognition’s accuracy with gender identification, age estimation and emotion detection. It also added a new emotion — fear.
It’s been less than a banner summer for Rekognition. The city of Orlando, Fla. ended its pilot program with the technology in July. A recent study even called into question the viability of technological emotional analysis. Amazon’s own employees urged the company not to collaborate with law enforcement agencies like ICE, and Amazon ultimately rejected staff and shareholder calls to halt facial recognition sales to government agencies.
Lake Formation now open to all
Organizations use data lakes to store, catalog, query and analyze massive amounts of raw data in one central repository. Building data lake architectures on AWS is a complicated process, where users string together several Amazon cloud services like S3, Amazon Elasticsearch, Amazon Athena and others. Lake Formation orchestrates all these services for you.
To get started, navigate to the Lake Formation console and register any existing S3 buckets that you want in your data lake. Create a database and grant permissions to Identity and Access Management users and roles that’ll need to access the data lake. Make sure the database is registered in the Glue Data Catalog for metadata analysis. To orchestrate data ingestion, select blueprints in the console that create different data lake workflows, such as an AWS Elastic Load Balancing (ELB) logs blueprint that loads data from ELB logs.
Follow the workflow progress in the AWS Glue console, and when it’s finished, you’ll find a new table in your data lake database. That centralization is a key benefit of Lake Formation.
Capital One hacker indicted
Former Amazon software engineer Paige Thomspon was indicted Wednesday, Aug. 28 on two counts in connection with the recent Capital One hack and her unauthorized intrusion into data from more than 30 different companies and institutions.
Thompson created scanning software that could identify if cloud computing customers misconfigured their firewalls, according to the indictment. She then allegedly used this access to steal data and channel stolen compute power into cryptojacking. Thompson faces up to 25 years in prison and will remain in custody until her arraignment Sept. 5.
Ahead of Thompson’s detention hearing earlier this month, federal prosecutors filed a memorandum that stated investigators searched Thompson’s servers and found multiple terabytes of additional stolen data from more than 30 different companies. With these additional allegations, along with a history of violent behavior, the court denied Thompson bail.
Capital One expects the affair to cost between $100 million and $150 million in 2019, according to Reuters.