Nieuwe, volledige managed dienst maakt bewezen machine learning technologie om predictive apps op Amazon te ondersteunen beschikbaar voor alle ontwikkelaars - zonder dat hier machine learning ervaring voor nodig is
Amazon Web Services, Inc. (AWS), een bedrijf van Amazon.com (NASDAQ: AMZN), heeft gisteren in Seattle Machine Learning aangekondigd, een volledige managed dienst waarmee het voor elke ontwikkelaar makkelijk wordt om historische data te gebruiken om hiermee voorspellende modellen uit te voeren. Deze modellen kunnen voor een breed scala aan doelen worden ingezet, zoals het detecteren van problematische transacties, het voorkomen van klantverloop en het verbeteren van klantondersteuning. De dienst is gebaseerd op de bewezen, schaalbare machine learning technologie die nu al door ontwikkelaars bij Amazon wordt gebruikt om meer dan 50 miljard voorspellingen per week te doen. Een API en een simpele handleiding helpt gebruikers van Amazon Machine Learning bij het creatieproces en het fine tunen van machine learning modellen zodat de dienst makkelijk gebruikt kan worden om voorspellingen naar boven te halen. Amazon Machine Learning is geïntegreerd met Amazon Simple Storage Service (Amazon S3), Amazon Redshift en Amazon Relational Database Service (Amazon RDS), waardoor het makkelijk wordt voor de klant om te werken met de data die ze al in de AWS cloud hebben opgeslagen.
Lees hieronder het volledige persbericht in het Engels:
Amazon Web Services, Inc. (AWS), an Amazon.com company (NASDAQ: AMZN), yesterday announced Amazon Machine Learning, a fully managed service that makes it easy for any developer to use historical data to build and deploy predictive models. These models can be used for a broad array of purposes, including detecting problematic transactions, preventing customer churn, and improving customer support. Based on the same proven, highly scalable machine learning technology used by developers across Amazon to generate more than 50 billion predictions a week, Amazon Machine Learning’s APIs and wizards guide developers through the process of creating and tuning machine learning models that can be easily deployed and scale to support billions of predictions. Amazon Machine Learning is integrated with Amazon Simple Storage Service (Amazon S3), Amazon Redshift and Amazon Relational Database Service (Amazon RDS), making it easy for customers to work with the data they’ve already stored in the AWS Cloud. To get started with Amazon Machine Learning, visit http://aws.amazon.com/machine-learning.
Until now, very few developers have been able to build applications with machine learning capabilities because doing so required expertise in statistics, data analysis, and machine learning. In addition, the traditional process for applying machine learning involves many manual, repetitive, and error-prone tasks such as computing summary statistics, performing data analysis, using machine learning algorithms to train a model based on data, evaluating and fine tuning the model, and then generating predictions using the model. Amazon Machine Learning makes machine learning broadly accessible to all software developers by abstracting away this complexity and automating these steps. With Amazon Machine Learning, developers can use the AWS Management Console or APIs to quickly create as many models as they need, and generate predictions from them with high throughput without worrying about provisioning hardware, distributing and scaling the computational load, managing dependencies, or monitoring and troubleshooting the infrastructure. There is no setup cost, and developers pay as they go so they can start small and scale as an application grows.
“Amazon has a long legacy in machine learning. It powers the product recommendations customers receive on Amazon.com, it is what makes Amazon Echo able to respond to your voice, and it is what allows us to unload an entire truck full of products and make them available for purchase in as little as 30 minutes,” said Jeff Bilger, Senior Manager, Amazon Machine Learning. “Early on, we recognized that the potential of machine learning could only be realized if we made it accessible to every developer across Amazon. Amazon Machine Learning is the result of everything we’ve learned in the process of enabling thousands of Amazon developers to quickly build models, experiment, and then scale to power planet-scale predictive applications.”
Because high-quality data is critical to building accurate models, Amazon Machine Learning allows developers to visualize the statistical properties of the datasets that will be used to “train” the model to find patterns in the data. This saves time by allowing developers to understand data distributions and identify missing or invalid values prior to model training. Amazon Machine Learning then automatically transforms the training data and optimizes the machine learning algorithms so that developers don’t need a deep understanding of machine learning algorithms or tuning parameters to create the best possible model. Using the Amazon Machine Learning technology, a single Amazon developer was able in 20 minutes to solve a problem that had previously taken two developers 45 days to solve – none of these developers had prior experience in machine learning, and both models achieved the same accuracy of 92 percent. Once a model is created, developers can then easily generate batch or real time predictions directly from Amazon Machine Learning without having to develop and manage their own infrastructure to do so.
Comcast Corporation is a global media and technology company with two primary businesses, Comcast Cable and NBCUniversal. “We evaluated Amazon Machine Learning and found it to be a compelling offering for our data science analytics. We particularly liked the ability to visually explore the tradeoff between parameter settings and classification performance during the evaluation,” said Jan Neumann, Manager of a Data Science Research team at Comcast. “With Amazon Machine Learning it was quite simple to prepare and clean the input data and train a model on large data sets in short order.”
The Amazon sustainable packaging team provides Amazon shipments in smaller, more environmentally friendly packages while still protecting the delivered items. “We use Amazon Machine Learning to analyze customer feedback on packaging and create predictions to identify products that are suited for our Frustration Free and eCommerce ready packaging standards,” said Kara Hurst, Director of Amazon Global Sustainability. “Amazon Machine Learning has really helped us improve our ability to identify products with packaging that is wasteful and frustrating for our customers. We’ve been able to use our existing data and very quickly develop predictive models that we can deploy in production within weeks. As a result, we have a more environmentally friendly product and packaging that is better for our customers.”
Space Ape Games is an award-winning mobile and tablet gaming startup that delivers games such as Rival Kingdoms and Samurai Siege. “A key part of keeping our customers engaged in our games is to predict the types of content, such as live events and tournaments, that they’ll enjoy the most and let the game adapt to their play styles,” said Toby Moore, CTO and co-founder at Space Ape Games. “By using a service like Amazon Machine Learning, we’re able to more easily and precisely make decisions about how to keep our customers excited about and enjoying playing games like Rival Kingdoms and Samurai Siege. We’ve been very impressed with Amazon Machine Learning so far, and plan to deploy Amazon Machine Learning across multiple departments in our organization to help us build and deploy predictive models for our current and future games. This is an exciting day for our business.”