In this talk, we present: trends and challenges in the data ecosystem in cloud era; Data engineering in the cloud with data orchestration; Use cases of using tech stacks (Presto or Tensorflow) with Alluxio on S3.
Problem It becomes increasingly more popular among data scientists to train models based on frameworks like TensorFlow on a local server or cluster while using remote shared storages like S3 or Google Cloud Storage to store a massive amount of the input data. This stack provides high flexibility and cost efficiency, especially requires no dev-ops … Continued
TensorFlow is an open source machine learning platform used to build applications like deep neural networks. It consists of an ecosystem of tools, libraries, and community resources for machine learning, artificial intelligence and data science applications. S3 is an object storage service that was created originally by Amazon. It has a rich set of API’s … Continued
Speeding Up Machine Learning in the Cloud with Alluxio
This presentation focuses on how Alluxio helps the big data analytics stack to be cloud-native. The trending Cloud object storage systems provide more cost-effective and scalable storage solutions but also different semantics and performance implications compared to HDFS. Applications like Spark or Presto will not benefit from the node-level locality or cross-job caching when retrieving data from the cloud object storage. Deploying Alluxio to access cloud solves these problems because data will be retrieved and cached in Alluxio instead of the underlying cloud or object storage repeatedly.
The Alluxio POSIX API enables data engineers to access any distributed file system or cloud storage as if accessing a local file system with an added performance improvement. This reduces the effort and complexity for data engineers to run their machine learning or legacy workloads on new data storage without data migration or data duplication.
This webinar reviews: The observation and analysis of trends of separation of Storage and Compute in Big Data ecosystem; Why and how to build a new data access layer between compute and storage in this data stack; Alluxio open source: history, overview, design, and architecture; Production Use case with Spark, Presto, Tensorflow and etc; A demo of running Presto on Alluxio on S3
In the age of growing datasets and increased computing power, deep learning has become a popular technique for AI. Deep learning models continue to improve their performance across a variety of domains, with access to more and more data, and the processing power to train larger neural networks. This rise of deep learning advances the state-of-the-art for AI, but also exposes some challenges for the access to data and storage systems. In this article, we further describe the storage challenges for deep learning workloads and how Alluxio can help to solve them.