AWS Partner Network (APN) Blog
Tag: Artificial Intelligence
Building the Business Case for Machine Learning in the Real World
Many organizations feel that AI will be the biggest disruptor to their industry in the next five years, and many leaders are asking if machine learning is right for their business. We offer an approach to identifying real business value using ML and discuss how to identify and quantify which use cases are the best fit for your industry and how to derive business value with the help of AWS Machine Learning Competency Partners.
Artificial Intelligence and Machine Learning: Going Beyond the Hype to Drive Better Business Outcomes
Do you want to become more familiar with how your company can use artificial intelligence (AI) and machine learning (ML) but feel a bit lost amongst the buzzwords and hype? Driving business outcomes with AI doesn’t need to be overwhelming. It’s all about exploring which business problems you want to solve, how good predictions can help you achieve those outcomes, and then taking practical steps to get there while implementing an organization-wide AI strategy.
Understanding the Data Science Life Cycle to Drive Competitive Advantage
Companies struggling with data science don’t understand the data science life cycle. As a result, they fall into the trap of the model myth. This is the mistake of thinking that because data scientists work in code, the same processes that works for building software will work for building models. Models are different, and the wrong approach leads to trouble. Domino Data Lab shares that organizations excelling at data science are those that understand it as a unique endeavor, requiring a new approach.
An Executive’s Guide to Delivering Business Value Through Data-Driven Innovation and AI
Fostering a data-driven culture within your organization isn’t only about technology. It’s also about enabling stakeholders to make better decisions and realizing new opportunities by embracing an AI-driven mentality for solving business problems. In this post, AWS Machine Learning Competency Partner Crayon discusses some of the first steps you should take and the essential questions to ask yourself as you thoughtfully develop your company’s relationship with data.
The Curse of Big Data Labeling and Three Ways to Solve It
The nature of data has changed dramatically. Just a decade back, the majority of our data was structured (residing in relational databases) or textual. Now, with the advent of self-driving vehicles, drones, and the Internet of Things (IoT), images and video data are taking the lion’s share of the data storage zoo. As we create more and more data on more and more devices, however, this problem is not going away. In fact, we have reached a point where there aren’t enough people on the planet to label all the data we’re creating.
AWS Analytics Services Explained: From Data Lakes to Machine Learning
AWS provides a broad set of managed services for data analytics that, along with a strong APN Partner community, can help you build a scalable, secure, and cost-effective data lake. Customers and APN Partners want to know how to put all these pieces so we created a new poster and video explaining the overall flow of data—from data collection, storage, and processing all the way to analytics and machine learning.
Training Machine Learning Models in Pharma and Biotech Manufacturing with Aizon
While market adoption of machine learning varies across industry segments, healthcare and life sciences have lots of opportunity to explore. Pharmaceutical and biotech industries, specifically, have low utilization of the large volumes of data they collect and store for regulatory purposes. Aizon is an AWS Partner that offers a number of ML solutions to enhance utilization and management of such data in a way that adheres to Good Manufacturing Practice (GMP) in the cloud.
4 Steps to Train and Deploy Machine Learning Models on AWS Using H2O
H2O is an open source data machine learning platform that provides a flexible, user-friendly tool to help data scientists and machine learning practitioners. It was created by H2O.ai, an APN Advanced Partner with the AWS Machine Learning Competency. In this post, we look at setting up an H2O cluster, import data from Amazon S3, create an AWS Lambda deployment package from the model, and finally deploy a RESTful endpoint. Following these steps, you can migrate your H2O Flows to AWS in about 10 minutes.
Customer Success Stories from AWS Machine Learning Competency Partners
At the AWS London Summit, we announced the new AWS Machine Learning Competency Program for APN Consulting Partners. Launch Partners in this program have deep expertise and proven customer success in Machine Learning on AWS, and we are excited to showcase some of their stories. The AWS Competency Program helps customers identify and choose the top APN Partner for their AWS projects and workloads.
Integrating with Amazon SageMaker: Using Built-In Algorithms from External Applications
We are often asked how to integrate software with Amazon SageMaker and use the service’s built-in machine learning algorithms. In this post, we discuss how to use the training capabilities of Amazon SageMaker to leverage its built-in algorithms. The types of applications that can integrate with Amazon SageMaker are data science platforms, business intelligence tools, or any application that needs to use machine learning behind the scenes.