In my previous blog, End-to-End Machine Learning Using Containerization, I covered the advantages of doing machine learning using microservices and how containerization can improve every step of the workflow by providing:
The Secret Behind the New AI Spring: Transfer Learning
Transfer learning has democratized artificial intelligence. A real-world example shows how.
As enterprises strive to find competitive advantages, artificial intelligence stands out as a "new" technology that can bring benefits to their organization. Model building is a big part of AI, but it is a time-consuming chore, so anything an enterprise can do to make faster progress is a plus. That includes finding ways to avoid reinventing the wheel when it comes to building AI models.
If you missed the live session, you can catch me and Ralf Klinkenberg -- Co-founder and chief of data science research at RapidMiner -- discussing the how's and why's of predictive maintenance below:
Lately, we've been talking a lot about containerization and how Kubernetes and MapR can pair up to enhance the productivity of your data science teams and increase the time to insights. In this multi-part blog series, I will start with a high-level overview of why Kubernetes and containerization are appealing for data science environments. In a later iteration, I will provide an example of a framework that enables Kubernetized data science on your MapR cluster.
Predictive maintenance (PdM) has emerged as a primary advanced analytics use case as manufacturers have sought increased operational efficiency and productivity and as a response to technological innovations like the Internet of Things (IoT) and edge computing.
Some people spend their weekends doing housework, socializing, or running errands. I spent last weekend playing with image classification and having a blast with it - so much so that I wrote a blog about the experience.
Read it here!
Python has become the darling language of the data science and data engineering world. It's versatile and powerful, yet easy enough for beginners to use. While we encounter Python developers in every area of IT from web development to network management, we're really seeing the boom right now in machine learning and deep learning application development.
But there's a problem where data science and big data intersect as Hadoop does not have native support for Python. On a filesystem like MapR-XD, this is less of an issue since any library that supports parallel computation can use MapR-XD as a Direct NFS storage layer. If you want to leverage Apache Hadoop YARN for distributed computation, however, you are limited to the Spark Python API (PySpark).
Executive Briefing: A new taxonomy of machine learning
Rachel Silver shares a new taxonomy of machine learning approaches that distinguishes between those that are providing enormous competitive advantage and those that represent merely small, incremental improvements on existing analytical tools and details a framework for evaluating ML approaches on several dimensions of complexity, including:
The amount of data required (such as for training)
The computational complexity of the training algorithm
Real-time streaming requirements (versus just batch computing)
Data throughput for the deployed model to process
Rachel explores examples of how to apply this framework to real-world machine learning approaches and highlights the technical requirements of supporting the most disruptive examples of ML solutions.
The MapR Data Science Refinery container includes a FUSE-based MapR POSIX Client, optimized for containers, that allows deep learning libraries to read and write data directly to MapR-FS.
So, when you run TensorFlow, the compute occurs on the host where the container resides, but each container has full access to the persistent storage provided by the MapR Converged Data Platform. When you kill the container off, the data remains.
Complete Steps to deploy this can be found here.
Rachel Silver is the Product Management Lead for Machine Learning & AI @ MapR Data Technologies.