Notes about Cutting-Edge Technologies and Everything

The critical need for information governance continues to drive a diversified market for information stewardship solutions that support it. Data and analytics leaders must assess the capabilities these solutions offer to select vendors that will best suit their needs. Key Findings Policy setting in information governance programs is still so different and inconsistent that no market of offerings is forming as yet. Furthermore, policy enforcement in information stewardship initiatives is conforming to a market, but now across a wider set of use cases. Information stewardship applications available in the market do not yet fully support the information steward's wider role and tasks. Growth in the market for information stewardship applications is being disrupted by new technology capabilities in adjacent markets, such as data quality and metadata management, and new regulatory requirements, such as GDPR. Recommendations For data and analytics leaders working with dat

Comparing Top Deep Learning Frameworks: Deeplearning4j, PyTorch, TensorFlow, Caffe, Keras, MxNet, Gluon & CNTK Skymind bundles Deeplearning4j and Python deep learning libraries such as Tensorflow and Keras (using a managed Conda environment) in the Skymind Intelligence Layer (SKIL), which offers ETL, training and one-click deployment on a managed GPU cluster. The SKIL Community Edition is free and downloadable here . Eclipse Deeplearning4j is distinguished from other frameworks in its API languages, intent and integrations. DL4J is a JVM-based, industry-focused, commercially supported, distributed deep-learning framework that solves problems involving massive amounts of data in a reasonable amount of time. It integrates with Kafka, Hadoop and Spark using an arbitrary number of GPUs or CPUs , and it has a number you can call if anything breaks. DL4J is portable and platform neutral, rather than being optimized on a specific cloud service such as AWS, Azure or Goog

Machine learning platforms are not the wave of the future. It's happening now. Developers need to know how and when to harness their power. Working within the ML landscape while using the right tools like Filestack can make it easier for developers to create a productive algorithm that taps into its power. The following machine learning platforms and tools — listed in no certain order — are available now as resources to seamlessly integrate the power of ML into daily tasks. 1.  H2O H2O was designed for the Python, R, and Java programming languages by H2O.ai. By using these familiar languages, this open source software makes it easy for developers to apply both predictive analytics and machine learning to a variety of situations. Available on Mac, Windows, and Linux operating systems, H2O provides developers with the tools they need to analyze data sets in the Apache Hadoop file systems as well as those in the cloud. 2.  Apache PredictionIO Developers who

What are some open datasets for machine learning? We at Gengo decided to create the ultimate cheat sheet for high quality datasets. These range from the vast (looking at you, Kaggle) or the highly specific (data for self-driving cars). First, a couple of pointers to keep in mind when searching for datasets. According to Dataquest : A dataset shouldn’t be messy, because you don’t want to spend a lot of time cleaning data. A dataset shouldn’t have too many rows or columns, so it’s easy to work with. The cleaner the data, the better — cleaning a large data set can be very time consuming. There should be an interesting question that can be answered with the data. Let’s get to it! Dataset Finders Kaggle : A data science site that contains a variety of externally-contributed interesting datasets. You can find all kinds of niche datasets in its master list , from ramen ratings to basketball data to and even seattle pet licenses . UCI Machine Learning Repository : One of