AWS Partner Network (APN) Blog

Category: Artificial Intelligence

Next-Caller-AWS-Partners

Building a Data Processing and Training Pipeline with Amazon SageMaker

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.

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Accelerating Machine Learning with Qubole and Amazon SageMaker Integration

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.

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How Slalom and WordStream Used MLOps to Unify Machine Learning and DevOps 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.

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How Slalom Uses AWS DeepRacer to Upskill its Workforce in Reinforcement Learning

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.

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How to Orchestrate a Data Pipeline on AWS with Control-M from BMC Software

In spite of the rich set of machine learning tools AWS provides, coordinating and monitoring workflows across an ML pipeline remains a complex task. Control-M by BMC Software that simplifies complex application, data, and file transfer workflows, whether on-premises, on the AWS Cloud, or across a hybrid cloud model. Walk through the architecture of a predictive maintenance system we developed to simplify the complex orchestration steps in a machine learning pipeline used to reduce downtime and costs for a trucking company.

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How to Use Amazon SageMaker to Improve Machine Learning Models for Data Analysis

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.

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How Steamhaus Used AWS Well-Architected to Improve Sperry Rail’s Artificial Intelligence System

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.

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Gathering Market Intelligence from the Web Using Cloud-Based AI and ML Techniques

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.

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Sponsoring Amazon re:MARS 2020 Can Help Grow Your Artificial Intelligence Business

Amazon re:MARS 2020 is our second annual artificial intelligence event, covering a diverse array of topics and themes related to machine learning, automation, robotics, and space (MARS). The event brings together innovative minds to share new ideas across these rapidly advancing domains. APN Partner are invited to join us as a sponsor at Amazon re:MARS and gain access to decision makers who are starting or accelerating MARS tech initiatives.

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Unlocking the Value of Your Contact Center Data with TrueVoice Speech Analytics from Deloitte

Voice data represents a rich and relatively untapped source of information that can help organizations gaining precious insights into their customers and operations. By leveraging a number of AWS services, Deloitte’s speech analytics solution, TrueVoice, can process voice data at scale, apply machine learning models to extract valuable information for this unstructured data, and continuously refine and enrich such models, tailoring them to specific industries and business needs.

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