In this post, I describe how SVM works to make you understand strengths / weaknesses, the parameter meanings, so on and so forth in the practical use.
These ideas behind SVM are based on mathematics (statistics). However, for building your intuitions, I’ll try to explain with a lot of samples and visualizations. This post will also be helpful for you to learn kernel methods.
Monitor Performance in Apache Spark – Identify Bottlenecks by sparkMeasure
When you want to see the bottlenecks in your code on Apache Spark, you can use the detailed logs with Spark event logs or REST API.
By the integration with your notebooks and your programming code, sparkMeasure simplifies your works for these logging and analyzing in Apache Spark.
In this post, I briefly show you how to use sparkMeasure.
Optimized Read-Throughput by Azure Cosmos DB Spark Connector
In this post, we see how it efficiently works behind Azure Cosmos DB Spark Connector (azure-cosmosdb-spark). By knowing the mechanism of connector, please optimize the read throughput with Apache Spark and Cosmos DB.
Spark ML Serving with Azure Machine Learning
Using Azure Machine Learning service, you can train the model on the Spark-based distributed platform (Azure Databricks) and serve your trained model (pipeline) on Azure Container Instance (ACI) or Azure Kubernetes Service (AKS).
In this post, I show you this step and background using AML Python SDK.
MXNet Distributed Training with Azure ML (Custom Configuration Sample)
In this post, I proceed to more advanced topics by showing you how to set up (customize) your Azure Machine Learning Compute (AmlCompute) for the practical training. In the last part of this post, I’ll show you Apache MXNet distributed training example with Azure Machine Learning service.
Azure Machine Learning : Walkthrough of Key Features
In this post, I’ll show you how Azure ML helps your ML/AI workloads with overall features and code examples.
Speed up Inference by TensorRT (Step-by-Step on Azure)
In this post, I’ll show you how to optimize models in TensorFlow by using TensorRT for ONNX on Microsoft Azure.
Run FPGA Accelerated Serving (“Project Brainwave”)
Azure Machine Learning Hardware Accelerated Models (Project Brainwave) provides hardware accelerated machine learning with FPGA.
In Github tutorial, there are several useful helper classes and functions (with python) which encapsulate boilerplate code to achieve provisioning steps. In this post I show you the same steps without these helpers. With these steps I hope it helps you to understand new FPGA-enabled services and how it’s working.
Azure Databricks tutorial for TensorFlow developers (TensorFlowOnSpark)
Here I show you TensorFlowOnSpark on Azure Databricks. With this tutorial, you can learn how to use Azure Databricks through lifecycle, such as – cluster management, analytics by notebook, working with external libraries, working with surrounding Azure services, submitting a job for production, etc.
Run deep learning workloads on SQL Server Machine Learning Services (rxNeuralNet)
In this post, we quick view how to run the workloads with neural networks (deep learning workloads) in SQL Server.
tsmatz