AI News, Building a scalable Data Science Platform ( Luigi, Apache Spark, Pandas, Flask)

Building a scalable Data Science Platform ( Luigi, Apache Spark, Pandas, Flask)

Typically, a data science platform consists of: Over time the amount of data stored that needs to be processed increases and it necessitates

We will be covering the following topics for data engineering: Building a robust and scalable machine learning platform is a hard job.

We will be covering the following topics for API: We use Vagrant along with Virtual Box to make our job easier By the end of these 2 steps, you should have the vagrant executable in your path.

Once this is done, clone the repository into a location of your choice Then cd into the repository directory From here, you want to bring up the vagrant box.

This step might change based on your installation Create a .exports file in your home dir to be sourced Set the JAVA_HOME to where the JDK is installed Add pyspark's bin directory to PATH Set the SPARK_HOME variable Set PYTHONPATH according to the SPARK_HOME variable Note: The \ is for escaping the $ so that the $SPARK_HOME variable isn't evaluated when being added into the file You also need to add the repository location into PYTHONPATH Finally we tell Spark to use Python 3 over 2 We need the ~/.exports file to be sourced when the shell starts up, so lets do that Next, install all the packages from requirements.txt in the repository.

In order to do this, first login via the postgres OS user and set the password using psql Back as your regular user, Edit the pg_hba.conf and set change the following line To And restart PostgreSQL Now running psql should ask you for the password Enter postgres at the prompt and you should see the psql prompt.

Setting up your machine for data science in Python

If you'll be using the programming language Python and its related libraries for loading data, exploring what it contains, visualizing that data, and creating statistical models this is what you need.

Anaconda puts nearly all of the tools that we're going to need into a neat little package: the Python core language, an improved REPL environment called Jupyter, numeric computing libraries (NumPy, pandas), plotting libraries (seaborn, matplotlib), and statistics and machine learning libraries (SciPy, scikit-learn, statsmodels).

* In your command prompt with the tutorial environment activated (Note: you'll be able to tell because your command prompt will say (tutorial) at the start of it.)

Provision the Data Science Virtual Machine for Linux (Ubuntu)

The Data Science Virtual Machine for Linux is an Ubuntu-based virtual machine image that makes it easy to get started with machine learning, including deep learning, on Azure.

The Data Science Virtual Machine for Linux also contains popular tools for data science and development activities, including: Doing data science involves iterating on a sequence of tasks: Data scientists use various tools to complete these tasks.

It can be quite time consuming to find the appropriate versions of the software, and then to download, compile, and install these versions.

You pay only the Azure hardware usage fees that are assessed based on the size of the virtual machine that you provision.

To connect to the Linux VM graphical desktop, complete the following procedure on your client: After you sign in to the VM by using either the SSH client or XFCE graphical desktop through the X2Go client, you are ready to start using the tools that are installed and configured on the VM.

To connect, browse to https://your-vm-ip:8000 on your laptop or desktop, enter the username and password that you used to create the VM, and log in.

You can set JupyterLab as the default notebook server by adding this line to /etc/jupyterhub/jupyterhub_config.py: The Microsoft Cognitive Toolkit is an open source, deep learning toolkit.

To run a basic sample at the command-line, execute the following commands in the shell: For more information, see the CNTK section of GitHub, and the CNTK wiki.

The NVIDIA Deep Learning GPU Training System, known as DIGITS, is a system to simplify common deep learning tasks like managing data, designing and training neural networks on GPU systems, and monitoring performance in real time with advanced visualization.

It is an open source software library for numerical computation using data flow graphs.

It is available in /dsvm/tools/torch, and the th interactive session and luarocks package manager are available at the command line.

This distribution contains the base Python along with about 300 of the most popular math, engineering, and data analytics packages.

To activate the root (2.7) environment: To activate the py35 environment again: To invoke a Python interactive session, just type python in the shell.

For pip, activate the correct environment first if you do not want the default: Or specify the full path to pip: For conda, you should always specify the environment name (py35 or root): If you are on a graphical interface or have X11 forwarding set up, you can type pycharm to launch the PyCharm Python IDE.

A shortcut to Spyder is provided in the graphical desktop.s The Anaconda distribution also comes with a Jupyter notebook, an environment to share code and analysis.

You can see the link to the samples on the notebook home page after you authenticate to the Jupyter notebook by using your local Linux user name and password.

standalone instance of Apache Spark is preinstalled on the Linux DSVM to help you develop Spark applications locally first before testing and deploying on large clusters.

Before running in Spark context in Microsoft R Server, you need to do a one time setup step to enable a local single node Hadoop HDFS and Yarn instance.

In order to enable it, you need to run the following commands as root the first time: You can stop the Hadoop related services when you dont need them by running systemctl stop hadoop-namenode hadoop-datanode hadoop-yarn A

sample demonstrating how to develop and test MRS in remote Spark context (which is the standalone Spark instance on the DSVM) is provided and available in the /dsvm/samples/MRS directory.

The ODBC driver package for SQL Server also comes with two command-line tools: bcp: The bcp utility bulk copies data between an instance of Microsoft SQL Server and a data file in a user-specified format.

The bcp utility can be used to import large numbers of new rows into SQL Server tables, or to export data out of tables into data files.

To import data into a table, you must either use a format file created for that table, or understand the structure of the table and the types of data that are valid for its columns.

sqlcmd: You can enter Transact-SQL statements with the sqlcmd utility, as well as system procedures, and script files at the command prompt.

Azure Machine Learning is a fully managed cloud service that enables you to build, deploy, and share predictive analytics solutions.

Vowpal Wabbit is a machine learning system that uses techniques such as online, hashing, allreduce, reductions, learning2search, active, and interactive learning.

The objective of this library is to push the computation limits of machines to the extremes needed to provide large-scale tree boosting that is scalable, portable, and accurate.

Here is a simple example you can run in R prompt: To run the xgboost command line, here are the commands to execute in the shell: A

It presents statistical and visual summaries of data, transforms data that can be readily modeled, builds both unsupervised and supervised models from the data, presents the performance of models graphically, and scores new data sets.

It also generates R code, replicating the operations in the UI that can be run directly in R or used as a starting point for further analysis.

Especially for beginners in R, this is an easy way to quickly do analysis and machine learning in a simple graphical interface, while automatically generating code in R to modify and/or learn.

Create an Azure Machine Learning Web Service with Python and Azure DSVM

Now our project folder should contain the following files: Now that we have everything setup, we will now create our web service and deploy the model by doing the following: In order for us to be able to create our environment and model management account using azure cli we will first need to authenticate with this command: This will then show a message that you need to open up your web browser and go to https://aka.ms/devicelogin and enter the code provided in your terminal.

You can check the Provisioning State with the following command: Once you’re able to successfully set your Model Management Account and environment, we will now create the web service by: Once the web service is successfully created, you can use the following command to get all the important details that you can use to test the web service (like the sample CLI command, Swagger URL, Authorization Bearer Key, and etc): You can get your Service Id with the following command: It should look like this when your run in your terminal: Here’s an example on how you can test the web service via Scoring URL And we’re done, so what do you think about the Azure Machine Learning Operationalization?

Provision a Linux CentOS Data Science Virtual Machine on Azure

The Linux Data Science Virtual Machine is a CentOS-based Azure virtual machine that comes with a collection of pre-installed tools.

The key software components included are: Doing data science involves iterating on a sequence of tasks: Data scientists use various tools to complete these tasks.

It can be quite time consuming to find the appropriate versions of the software, and then to download, compile, and install these versions.

You pay only the Azure hardware usage fees that are assessed based on the size of the virtual machine that you provision with the VM image.

To connect to the Linux VM graphical desktop, do the following on your client: After you sign in to the VM by using either the SSH client or XFCE graphical desktop through the X2Go client, you are ready to start using the tools that are installed and configured on the VM.

If you are using the Emacs editor, note that the Emacs package ESS (Emacs Speaks Statistics), which simplifies working with R files within the Emacs editor, has been pre-installed.

This distribution contains the base Python along with about 300 of the most popular math, engineering, and data analytics packages.

Since we have both Python 2.7 and 3.5, you need to specifically activate the desired Python version (conda environment) you want to work on in the current session.

To activate the Python 2.7 conda environment, run the following command from the shell: Python 2.7 is installed at /anaconda/bin.

To install additional Python libraries, you need to run conda or pip command under sudo and provide full path of the Python package manager (conda or pip) to install to the correct Python environment.

You can see the link to the samples on the notebook home page after you authenticate to the Jupyter notebook by using your local Linux user name and password.

standalone instance of Apache Spark is preinstalled on the Linux DSVM to help you develop Spark applications locally first before testing and deploying on large clusters.

Before running in Spark context in Microsoft R Server, you need to do a one time setup step to enable a local single node Hadoop HDFS and Yarn instance.

In order to enable it, you need to run the following commands as root the first time: You can stop the Hadoop related services when you dont need them by running systemctl stop hadoop-namenode hadoop-datanode hadoop-yarn A

sample demonstrating how to develop and test MRS in remote Spark context (which is the standalone Spark instance on the DSVM) is provided and available in the /dsvm/samples/MRS directory.

It allows you to create, develop, test, and deploy Azure applications using the Eclipse development environment that supports languages like Java.

The open source database Postgres is available on the VM, with the services running and initdb already completed.

The ODBC driver package for SQL Server also comes with two command-line tools: bcp: The bcp utility bulk copies data between an instance of Microsoft SQL Server and a data file in a user-specified format.

The bcp utility can be used to import large numbers of new rows into SQL Server tables, or to export data out of tables into data files.

To import data into a table, you must either use a format file created for that table, or understand the structure of the table and the types of data that are valid for its columns.

sqlcmd: You can enter Transact-SQL statements with the sqlcmd utility, as well as system procedures, and script files at the command prompt.

Azure Machine Learning is a fully managed cloud service that enables you to build, deploy, and share predictive analytics solutions.

Vowpal Wabbit is a machine learning system that uses techniques such as online, hashing, allreduce, reductions, learning2search, active, and interactive learning.

The objective of this library is to push the computation limits of machines to the extremes needed to provide large-scale tree boosting that is scalable, portable, and accurate.

Here is a simple example you can run in R prompt: To run the xgboost command line, here are the commands to execute in the shell: A

It presents statistical and visual summaries of data, transforms data that can be readily modeled, builds both unsupervised and supervised models from the data, presents the performance of models graphically, and scores new data sets.

It also generates R code, replicating the operations in the UI that can be run directly in R or used as a starting point for further analysis.

In some of the steps below, you are prompted to automatically install and load some required R packages that are not already on the system.

Especially for beginners in R, this is an easy way to quickly do analysis and machine learning in a simple graphical interface, while automatically generating code in R to modify and/or learn.

19 Data Science and Machine Learning Tools for people who Don’t Know Programming

This article was originally published on 5 May, 2016 and updated with the latest tools on May 16, 2018.

Among other things, it is acknowledged that a person who understands programming logic, loops and functions has a higher chance of becoming a successful data scientist.

There are tools that typically obviate the programming aspect and provide user-friendly GUI (Graphical User Interface) so that anyone with minimal knowledge of algorithms can simply use them to build high quality machine learning models.

The tool is open-source for old version (below v6) but the latest versions come in a 14-day trial period and licensed after that.

RM covers the entire life-cycle of prediction modeling, starting from data preparation to model building and finally validation and deployment.

You just have to connect them in the right manner and a large variety of algorithms can be run without a single line of code.

There current product offerings include the following: RM is currently being used in various industries including automotive, banking, insurance, life Sciences, manufacturing, oil and gas, retail, telecommunication and utilities.

BigML provides a good GUI which takes the user through 6 steps as following: These processes will obviously iterate in different orders. The BigML platform provides nice visualizations of results and has algorithms for solving classification, regression, clustering, anomaly detection and association discovery problems.

Cloud AutoML is part of Google’s Machine Learning suite offerings that enables people with limited ML expertise to build high quality models. The first product, as part of the Cloud AutoML portfolio, is Cloud AutoML Vision.

This service makes it simpler to train image recognition models. It has a drag-and-drop interface that let’s the user upload images, train the model, and then deploy those models directly on Google Cloud.

It also provides visual guidance making it easy to bring together data, find and fix dirty or missing data, and share and re-use data projects across teams.

Also, for each column it automatically recommends some transformations which can be selected using a single click. Various transformations can be performed on the data using some pre-defined functions which can be called easily in the interface.

Trifacta platform uses the following steps of data preparation: Trifacta is primarily used in the financial, life sciences and telecommunication industries.

The core idea behind this is to provide an easy solution for applying machine learning to large scale problems.

All you have to do is using simple dropdowns select the files for train, test and mention the metric using which you want to track model performance.

Sit back and watch as the platform with an intuitive interface trains on your dataset to give excellent results at par with a good solution an experienced data scientist can come up with.

It also comes with built-in integration with the Amazon Web Services (AWS) platform. Amazon Lex is a fully managed service so as your user engagement increases, you don’t need to worry about provisioning hardware and managing infrastructure to improve your bot experience.

You can interactively discover, clean and transform your data, use familiar open source tools with Jupyter notebooks and RStudio, access the most popular libraries, train deep neural networks, among a a vast array of other things.

It can take in various kinds of data and uses natural language processing at it’s core to generate a detailed report.

But these are excellent tools to assist organizations that are looking to start out with machine learning or are looking for alternate options to add to their existing catalogue.

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