5 Best Practices for Building Custom Machine Learning Models on AWS
Tensor Flow and H2O this tutorial is based on the 5 best practices we’ve used in our work at Data Robot with many of our customers facing problems like…
Building a Machine Learning Pipeline Using Amazon Elastic MapReduce (EMR) EMR gives you access to ready-to-use tools for creating Hadoop MapReduce and Spark clusters. Using a Hadoop cluster, you can integrate AWS services such as Amazon S3 with your machine learning algorithms to facilitate the fast processing of large datasets stored in the cloud.
Building Custom Machine Learning Models for Your Business In this post, I will cover some key points that I learned from experience about building custom machine learning models. …
Machine Learning & Big Data Analytics –
The Expert’s Voice in the AWS Community Machine Learning is at the forefront of how businesses are leveraging big data to gain valuable insights into their customers, products, and operations. … It also provides access to machine learning algorithms through Amazon Machine Learning’s Python API. This enables our customers to build models which can operate locally on your private data, or be deployed to the cloud for low-latency inferencing using Amazon EC2.
How to Build an End-To-End Predictive Customer Churn Model Using Amazon S3, DynamoDB, AWS Glue, and Amazon ML
This post details how we’ve used these components together in order to provide a solution for building predictive customer churn models. … There are lots of choices for building machine learning models, both open source and commercial. … Some examples of the most popular include the following: R, Python with sci-kit-learn, Spark MLLib, H2O, among others.
An introduction to using image classification with Amazon Rekognition This article walks through how to upload an image file, access its content using Amazon Rekognition ‘s powerful detection features, then create a Lambda function that runs when new images are detected. … This article walks through how to upload an image file, access its content using Amazon Rekognition ‘s powerful detection features, then create a Lambda function that runs when new images are detected. … Predict whether the image contains any adult or potentially offensive content using Machine Learning on AWS with the prebuilt deep learning classifiers in Amazon Rekognition.
Building Mobile Apps with Machine Learning on AWS Machine learning can be used to analyze mobile data captured from mobile phone sensors, such as the accelerometer. … For our demonstration, we are going to use Amazon Machine Learning which provides pre-packaged algorithms that you can simply call through an API instead of building machine learning models from scratch.
Deep Learning & NLP in Healthcare:
Transforming Diagnostics With Amazon Comprehend Medical experts can use natural language processing (NLP) to manually review medical records for EMR data correction, annotation of clinical concepts such as diagnosis, treatments, etc., and sentiment analysis. … To build our model, we used Amazon SageMaker, which provides end-to-end support including data preparation with Sparkling Water and deployment of TensorFlow deep learning models with Amazon SageMaker RL.
Serverless Machine Learning on AWS –
Analytics Vidhya We also explored the use of Amazon SageMaker to build, train and host machine learning models deployed as serverless APIs with Amazon API Gateway that can be easily called by mobile apps. … AWS Lambda is used to run Spark jobs periodically on the incoming Tweets from Twitter Streaming API. A pre-built library has been called inside the lambda function to perform sentiment analysis on tweets using Apache Spark MLlib model.
Using AWS Step Functions for Real-Time Data Processing
Real-time processing allows you to monitor data in near real-time, enabling collaborative efforts between teams across many different platforms. … By using AWS Step Functions, you are able to create state machines that handle the processing of your streaming data. … Depending on the complexity of what you need to process, you can also build machine learning algorithms and other complex ETL tasks with AWS Step Functions for highly versatile and scalable data processing.
Serverless Machine Learning:
5 use cases for business. In this tutorial, we will see how easy it is to save a trained machine-learning model as a web service using Amazon Web Services (AWS). We will be building a mobile app that uses an Amazon S3 bucket as storage which gets triggered by Amazon Cloud Watch events. Then, we will use AWS Lambda to implement serverless machine learning by calling our pre-trained model from the cloud.
Conclusion:
AWS has a large offering of tools and products to address many use cases for building scalable serverless architectures. … Many traditional businesses have been using these kinds of services due to the low cost and efficiency in scaling. AWS excels in providing a wide range of products to support this kind of architecture with a very high rate of innovation in their cloud offering.
