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SQL Operations Studio, Amazon’s support for ONNX, and Google’s Chatbase among today’s trending stories in data science news.

Announcing SQL Operations Studio for preview

Microsoft announced that SQL Operations Studio is now available in preview. Users can install SQL Operations Studio (preview) as following:

For Windows

  1. Download SQL Operations Studio (preview) for Windows.
  2. Browse to the downloaded file and extract it.
  3. Run sqlops-windowssqlops.exe

For macOS

  1. Download SQL Operations Studio (preview) for macOS.
  2. To expand the contents of the zip, double-click it.
  3. To make SQL Operations Studio (preview) available in the Launchpad, drag sqlops.app to the Applications folder.

For Linux

  1. Download SQL Operations Studio (preview) for Linux.
  2. To extract the file and launch SQL Operations Studio (preview), open a new Terminal window and type the following commands:

cd ~

cp ~/Downloads/sqlops-linux-<version string>.tar.gz ~

tar -xvf ~/sqlops-linux-<version string>.tar.gz

echo ‘export PATH=”$PATH:~/sqlops-linux-x64″‘ >> ~/.bashrc

source ~/.bashrc


Microsoft added a note that on Ubuntu and Redhat, users may have a missing dependency for libXScrnSaver. To install this dependency, following commands can be used:

Ubuntu: sudo apt-get install libxss1

Redhat: yum install libXScrnSaver

New announcements from Amazon Web services (AWS)

AWS announces ONNX support for Apache MXNet

The Open Neural Network Exchange (ONNX) deep-learning format, introduced in September by Microsoft and Facebook, has a new backer following Amazon Web Services’ decision to embrace the framework with a new open-source project. The AWS has released ONNX-MXNet, a method for allowing deep learning models built around the ONNX format to run on the Apache MXNet framework. MXNet is a fully featured and scalable deep learning framework that offers APIs across popular languages such as Python, Scala, and R. With ONNX format support for MXNet, developers can build and train models with other frameworks, such as PyTorch, Microsoft Cognitive Toolkit, or Caffe2, and import these models into MXNet to run them for inference using the MXNet highly optimized and scalable engine.

AWS unveils two new deep learning AMIs for machine learning practitioners

Amazon Web Services has announced the availability of two new versions of the AWS Deep Learning AMI: Conda-based AMI and Base AMI. The Conda-based AMI comes pre-installed with separate Python environments for deep learning frameworks created using Conda, while the Base AMI comes pre-installed with the foundational building blocks for deep learning. “Think of the Conda-based AMI as a fully baked virtual environment ready to run your deep learning code, for example, to train a neural network model. Think of the Base AMI as a clean slate to deploy your customized deep learning set up,” Amazon said in its release. The Conda-based AMI is packaged with latest official releases of the following deep learning frameworks: Apache MXNet 0.12 with Gluon, TensorFlow 1.4, Caffe2 0.8.1, PyTorch 0.2, CNTK 2.2, Theano 0.9, Keras 1.2.2, and Keras 2.0.9. The Base AMI comes with the CUDA 9 environment installed by default, but users can switch to a CUDA 8 environment. The Base AMI provides following GPU drivers and libraries: CUDA 8 and 9, CuBLAS 8 and 9, CuDNN 6 and 7, glibc 2.18, OpenCV 3.2.0, NVIDIA driver 384.81, NCCL 2.0.5, Python 2 and 3.

Google’s Chatbase and BigQuery

Google’s bot analytics platform “Chatbase” now open to everyone

More than six months after it quietly announced “Chatbase” during the I/O developer conference, Google has made the chatbot analytics platform open for public use. Chatbase helps developers analyze and optimize their bots better so that they can improve conversion rates and accuracy. Anyone can use Google’s Chatbase for free, similar to Google Analytics, and it’ll work across any platform, including Facebook Messenger, Kik, Slack, Viber, and Skype.

Google’s BigQuery data transfer service is now generally available

Google’s BigQuery Data Transfer Service is now generally available, offering users a way to easily transfer data from supported SaaS applications in an automated fashion. So far, the service supports transfer from apps like AdWords, DoubleClick Campaign Manager, DoubleClick for Publishers, and YouTube Content and Channel Owner Reports. The service has some new features, including customer-managed scheduling, which lets customers set their own data delivery schedules. It also now offers a data delivery service-level agreement (SLA). Companies like Trivago and Zenith have already begun using the service, Google said. Pricing information can be found here.


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