Category: Amazon Machine Learning

Spark is a distributed computing framework that added new features like Pandas UDF by using PyArrow. You can leverage Spark for distributed and advanced machine learning model lifecycle capabilities to build massive-scale products with a bunch of models in production. Learn how Perion Network implemented a model lifecycle capability to distribute the training and testing stages with few lines of PySpark code. This capability improved the performance and accuracy of Perion’s ML models.

Amazon Fraud Detector is a fully managed service that makes it easy to identify potentially fraudulent online activities, such as online payment fraud and the creation of fake accounts. Learn how APN Premier Consulting Partner TCS has been integrating Amazon Fraud Detector to detect spam calls and route them efficiently using Amazon Connect. Used together, these AWS services can distinguish your genuine customers from spam or fraudulent callers.

With rapid advancements in machine learning techniques over the past decade, intelligent decision-making and prediction systems are poised to transform productivity and lead to significant economic gains. KNIME provides visual data science tools to help data science teams rapidly build and deploy data-driven solutions that integrate with AWS decision support tools and services. Learn about the barriers to adoption of AI and the ways in which the KNIME tools remove those barriers.

Next Caller uses machine learning on AWS to drive data analysis and the processing pipeline. Amazon SageMaker helps Next Caller understand call pathways through the telephone network, rendering analysis in approximately 125 milliseconds with the VeriCall analysis engine. VeriCall verifies that a phone call is coming from the physical device that owns the phone number, and flags spoofed calls and other suspicious interactions in real-time.

Data scientists creating enterprise machine learning models to process large volumes of data spend a significant portion of their time managing the infrastructure required to process the data, rather than exploring the data and building ML models. You can reduce this overhead by running Qubole data processing tools and Amazon SageMaker. An open data lake platform, Qubole automates the administration and management of your resources on AWS.

Deploying AI solutions with ML models into production introduces new challenges. Machine Learning Operations (MLOps) has been evolving rapidly as the industry learns to marry new ML technologies and practices with incumbent software delivery systems and processes. WordStream is a SaaS company using ML capabilities to help small and mid-sized businesses get the most out of their online advertising. Learn how Slalom developed ML architecture to help WordStream productionize their machine learning efforts.

AWS DeepRacer allows developers of all skill levels to get started with reinforcement learning, which is an advanced machine learning technique that learns very complex behaviors without requiring any labeled training data, and can make short-term decisions while optimizing for a longer term goal. Learn how Slalom created AWS DeepRacer experiences for its own workforce. The cars and tracks now regularly appear in at Slalom locations across the world as valuable internal learning events.

Amazon SageMaker provides all the components needed for machine learning in a single toolset. This allows ML models to get to production faster with much less effort and at lower cost. Learn about the data modeling process used by BizCloud Experts and the results they achieved for Neiman Marcus. Amazon SageMaker was employed to help develop and train ML algorithms for recommendation, personalization, and forecasting models that Neiman Marcus uses for data analysis and customer insights.

Over two days, Steamhaus conducted an AWS Well-Architected Review on-site with the team who designed, built, and currently manage Elmer at Sperry Rail. Elmer uses machine intelligence to inspect thousands of miles of ultrasound scans collected by Sperry’s inspection vehicles, searching for evidence of cracks in the rail. This partnership allowed quick improvements in efficiency, while ensuring the requirements of running the business day-to-day did not get in the way of improving Elmer.

Many organizations face the challenge of gathering market intelligence on new product and platform announcements made by their partners and competitors—and doing so in a timely fashion. Harnessing these insights quickly can help businesses react to specific industry trends and fuel innovative products and offerings inside their own company.Learn how Accenture helped a customer use AWS to gather critical insights along with key signals and trends from the web using AI and ML techniques.