Blogs from this Author

Data Science = Synergistic Teamwork

What is the Future for Data Science Platforms?

What will the future hold for data science and machine learning platforms? Most of you already saw Gartner’s perspective in their latest Magic Quadrant on the evolution of data science that was released at the beginning of 2018 and are familiar with the outcome. However, did you ever ask yourself why SAS, KNIME, RapidMiner, and H2O.ai became […]

Serverless Architecture for Big Data

In the world of Big Data, Data engineers always strive to find a way or method to analyze, process, and compute the Volume, Velocity, and Variety of data, and to provide Data scientists with a resilient backbone to conduct their analysis. Before the introduction of the cloud platforms, all the big data processing and managing […]

Personalization Using Neural Networks

What do I want to watch tonight? What is the next book I might want to read? Those types of questions are usually answered through the suggestions that are embedded into your screen when you’re about to make your selections either on your phone, computer, tablet, or television. Initially, or as I recall, these recommendations were […]

Modeling as Proof of Concept (POC)

Why do I give my precedence to build the model as Proof of Concept (POC) instead of following established methodologies such as CRISP-DM, SEMMA, AIE, MAD Skills, etc.? Even though most of the Data scientists will say that they are two different things and used for different purposes; one is a methodology or a step […]

Lucene and the Future of GIS #Elasticon2018

I always like to dive into the technical details, sometimes to the chagrin of my colleagues. Today I had a brilliant opportunity to look under the covers at Lucene and it’s future through the lens of the ElasticSearch team. Something stood out during this deep technical session that was hinted in yesterday’s keynote as new […]

Accelerate business innovation with application modernization.

The Benefits of Using Cloud-Based Platforms for Data Science

As everything in this world matures and goes through the steps of evolution, Data Science is not much different. Even though it became more affordable for the companies to jump on the Data Science bandwagon and generate quick wins, Volume, Velocity, and Variety are still the bottleneck for most of the Artificial Intelligence (AI) projects. […]

Machine Learning Vs. Statistical Learning

Most of the time as a data scientist I get asked the question, what is the difference between Machine Learning and Statistical Learning? Even though you would think that the answer is obvious, there are a lot of novice data scientists that are still confused about those two approaches. As a beginner data scientist, it […]

The Tensorflow Weakness is a Gluon Strength

On October 12, 2017, Gluon became available to the public. Microsoft’s partnership with Amazon finally has a chance to outshine Google with its great TensorFlow. Gluon is a deep learning multiplatform tool which currently utilizes Apache MXNet and soon will work on Microsoft Cognitive Toolkit (CNTK) as well as other platforms to come shortly after. […]

Data Cube Operations – SQL Queries

Introduction Organizations are usually posed with the challenge of turning data into valuable insights. They realize the need for utilizing increasing amounts of “Big Data” in order to compete with other organizations in terms of efficiency, speed and service. The incredible growth of event data poses new challenges. As event logs grow, data processing techniques […]

Cross-Industry Success with Tableau

Organizations are turning to self-service data visualization and analytics tools like Tableau to explore enterprise data without limiting users to pre-defined questions or IT requests. We’ve helped many companies across a variety of industries implement these data discovery solutions with great results. The following high-level overviews are a sample of these implementations, highlighting the power […]

Watson Use Cases in Customer Service

According to a recent Forrester report, usage of chat bots and automated customer interaction tools is growing, but the success rate is dramatically low. While more than half of global organizations are using these tools, or planning to use the solutions soon, failure rates are often reported around 70%. One factor contributing to this failure […]

Considerations for Integration Strategies

This year, organizations will manage more data than ever, especially as they expand how they engage with customers. However, the lack of an appropriate strategy will hamper these efforts, causing harm to organizational efficiency, agility, and customer care. As we have discussed on this blog in the past, one way to address these challenges is […]

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