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Monetizing data is a difficult technological and operational challenge. If your goal is to unlock your data's full revenue potential, you need to be able to reach all possible data buyers and reach them efficiently, but doing so with an in-house solution takes a lot of effort:For example, buyers who are looking for data that can be integrated into their own applications may prefer the data in the form of an API. Creating a best-practices API that conforms to all necessary standards (e.g. REST, XML, JSON) and has the right authentication / API key mechanism, the right metering/pricing mechanism, the right level of documentation, etc, is a difficult task in itself.Other data buyers may be looking to download data in bulk, either in whole or in part (for example, to be analyzed in a spreadsheet). Here, one needs to create an online tool to select, pay for, download, and often re-download the right subset of data; The tool must have sufficient usability for non-technical data buyers, and must support all necessary data formats - again, a difficult set of challenges.Regardless of how the data is delivered, a data vendor needs to provide a mechanism for data buyers to sign up and pay for data online in a self-service way. Mechanisms are also needed for data vendors to validate buyers and prevent fraud, to view reports/analytics on data purchase activity, to set up data pricing rules (which may often be fairly complex) or other access plans such as free trials and promotions.Finally, there are the operational challenges: payments need to be processed from data buyers, payment issues need to be resolved, technical support must be provided (which is especially difficult in the case of buyers who access data via APIs), SLAs must be maintained, data needs to be hosted and continuously updated.WebServius, as well as a number of other companies, set out to solve all of the challenges above. There are economies of scale at play here, and it is far more efficient for these challenges to be solved once for all data providers on a particular platform, than for each provider to reinvent the wheel.For data vendors, we set out to offerr a true end-to-end data monetization experience: If you provide us with access to valuable data, we will provide you with a way to monetize it in a variety of compelling ways (APIs, bulk data download, etc.), without you having to worry about any of the difficult problems above, and with you staying in full control of the pricing, branding and terms.An analogy here is the sale of physical goods: If you are selling physical goods online, it is often much more efficient to leverage an existing platform (Amazon Stores, Amazon FBA, eBay, etc.) than to build out an entire e-Commerce solution from scratch. I see WebServius and some of our competitors as the "Amazon/eBay of data".

the TL;DR of this post: Marketing Automation data - otherwise called "behavior data" is absolutely valuable but should it be combined into one predictive score? Definitely not.Quick Primer:Fit score - How well does this company fit as a potential customer? Do we predict they will convert?Behavior score: Has this company engaged with me? e.g., have they downloaded whitepapers?Why is it Better to Keep this Data Separate?The name of the game in predictive lead scoring is actionable data - combining the score robs you of that ability. By setting standards in which you combine behavior and fit data - you set artificial barriers for action!If your vendor does this, you'll see many examples of the followingLow Scores for Great Fitting Companies: These are companies, that based on your data and your vendor's data science, have a much higher likelihood to convert. Does it make sense to let your competitors to call into these ideal customers first while you are waiting for them to download a whitepaper? Maybe if you called in first, you could actually cause the activity to build up!High Scores for Bad Fitting Leads: If you go with this type of model that combines both, some of your top leads will be otherwise bad fits e.g., a college student, that will never convert. They rise to the top because they have done a lot of activity like download every whitepaper.Fit is overwhelmingly more important in most business applications but behavior data can be very valuable as a secondary priority tool or for specific Marketing purposes. So don't lose this data, just don't combine it with fit!What is the leading practice?Many folks follow a SiriusDecisions model in which you separate fit and behavior scoring. This allows companies to benefit from evaluating their leads through both lenses.Finally, to correct an inconsistency on an earlier post, there are multiple vendors that use behavioral data in modeling.Hope this answers your question!(Full disclosure: After being a fan of predictive analytics for awhile, I actually recently joined Infer (Predictive Lead Scoring for Sales & Marketing) Here's a blogpost written earlier by Infer which covers some more on this topic - Two approaches to scoring leads - Fit vs. Activity )

Interest in Machine Learning and Deep Learning has exploded over the past decade. You see machine learning in computer science programs, industry conferences, and in many applications in daily life.I am assuming you already know about Machine Learning, therefore I will not be explaining What and Why.So, I find many beginners facing problems while installing libraries and setting up environment. As i have faced first time when i was trying. So this guide is totally for beginners .In this story I will tell you how you can easily setup a python environment on your system. I am using Windows but this guide is also suitable for Ubuntu & Linux users.After completing this tutorial, you will have a working Python environment to begin learning, and developing machine learning and deep learning software.PC Hardware SetupFirs of all to perform machine learning and deep learning on any dataset, the software/program requires a computer system powerful enough to handle the computing power necessary. So the following is required:Central Processing Unit (CPU) — Intel Core i5 6th Generation processor or higher. An AMD equivalent processor will also be optimal.RAM — 8 GB minimum, 16 GB or higher is recommended.Graphics Processing Unit (GPU) — NVIDIA GeForce GTX 960 or higher. AMD GPUs are not able to perform deep learning regardless. For more information on NVIDIA GPUs for deep learning please visit https://developer.nvidia.com/cuda-gpus.Operating System — Ubuntu or Microsoft Windows 10. I recommend updating Windows 10 to the latest version before proceeding forward.Note: In the case of laptops, the ideal option would be to purchase a gaming laptop from any vendor deemed suitable such as Alienware, ASUS, Lenovo Legion, Acer Predator etc.Let’s just get straight to the installation process. we are gonna hit the rockOverviewIn this tutorial, we will cover the following steps:Download AnacondaInstall Anaconda & PythonStart and Update AnacondaInstall CUDA Toolkit & cuDNNCreate an Anaconda EnvironmentInstall Deep Learning API’s (TensorFlow & Keras)Step 1: Download AnacondaIn this step, we will download the Anaconda Python package for your platform.Anaconda is a free and easy-to-use environment for scientific Python.1.Install Anaconda (Python 3.6 version) DownloadI am using Windows you can choose according to your OS.Step 2: Install AnacondaIn this step, we will install the Anaconda Python software on your system.Installation is very easy and quick once you download the setup. Open the setup and follow the wizard instructions.#Note: It will automatically install Python and some basic libraries with it.It might take 5 to 10 minutes or some more time according to your system.Step 3: Update AnacondaOpen Anaconda Prompt to type the following command(s). Don’t worry Anaconda Prompt is just works same as cmd.conda update condaconda update --all Step 4: Install CUDA Toolkit & cuDNNInstall CUDA Toolkit 9.0 or 8.0 DownloadChoose your version depending on your Operating System and GPU.Here is a guide to check that if your version support your Nvidia Graphic CardFor downloading other versions you can follow this link: https://developer.nvidia.com/cuda-toolkit-archive#Note: CUDA 9.0 is recommended as TensorFlow is NOT compatible with CUDA Toolkit 9.1 and 9.2 version. Kindly choose the CUDA version according to your Nvidia GPU version to avoid errors.#Note: People with version 9.0 Download can also install the given patch in any case of error while proceeding.2. Download cuDNN DownloadDownload the latest version of cuDNN. Choose your version depending on your Operating System and CUDA. Membership registration is required. Don’t worry you can easily create an account using your email.Put your unzipped folder in C drive as follows:C:\cudnn-9.0-windows10-x64-v7 Step 5: Add cuDNN into Environment PathOpen Run dialogue using (Win + R) and run the command sysdm.cplIn Window-10 System Properties, please select the Tab Advanced.Select Environment VariablesAdd the following path in your Environment.C:\cudnn-9.0-windows10-x64-v7\cuda\bin Step 6: Create an Anaconda EnvironmentHere we will create a new anaconda environment for our specific usage so that it will not affect the root of Anaconda. Amazing!! isn’t it?Open Anaconda Prompt to type the following commands.Create a conda environment named “tensorflow” (you can change the name) by invoking the following command:conda create -n tensorflow pip python=3.6 2. Activate the conda environment by issuing the following command:activate tensorflow  (tensorflow)C:> # Your prompt should change Step 7: Install Deep Learning LibrariesIn this step, we will install Python libraries used for deep learning, specifically: TensorFlow, and Keras.TensorFlowTensorFlow is a tool for machine learning. While it contains a wide range of functionality, TensorFlow is mainly designed for deep neural network models.=> For installing TensorFlow, Open Anaconda Prompt to type the following commands.To install the GPU version of TensorFlow:C:\> pip install tensorflow-gpu To install the CPU-only version of TensorFlow:C:\> pip install tensorflow If your machine or system is the only CPU supported you can install CPU version for basic learning and practice.=> You can test the installation by running this program on shell:>>> import tensorflow as tf>>> hello = tf.constant('Hello, TensorFlow!')>>> sess = tf.Session()>>> print(sess.run(hello)) For getting started and documentation you can visit TensorFlow website.2. KerasKeras is a high-level neural networks API, written in Python and capable of running on top of TensorFlow, CNTK, or Theano.=> For installing Keras Open Anaconda Prompt to type the following commands.pip install keras => Let’s try running Mnist_Mlp.Py in your prompt. you can use other examples as well.Open Anaconda Prompt to type the following commands.activate tensorflowpython mnist_mlp.py For getting started and documentation you can visit Keras website.Here is an implementation of Keras Standard Fully Connected Neural Network using Python for Digit Recognition I have done.There are some other famous libraries like Pytorch, Theano, and Caffe2 you can use as per on your choice and use.Congratulations! You have successfully created the environment using TensorFlow, Keras (with Tensorflow backend) over GPU on Windows!