BIG DATA WITH MAX WELLING: THE CHALLENGES


The challenges

Max Welling, Research chair in Machine Learning at the University of Amsterdam and VP technologies Qualcomm Netherlands, talks about Gdpr, augmented reality, wearables, interactive chatbots and the best skills to learn for a student.

Olena Matey

In response to Kaan Buğra Kundakçı

Machine learning is often a big part of a "data science" project, e.g., it is often heavily used for exploratory analysis and discovery (clustering algorithms) and building predictive models (supervised learning algorithms). However, in data science, you often also worry about the collection, wrangling, and cleaning of your data (i.e., data engineering), and eventually, you want to draw conclusions from your data that help you solve a particular problem.

There are numerous examples of data science applications. Assume you are working for a credit company. Your boss gives you the task to find out whether a customer is creditworthy or not. You collect transaction data, maybe shipping records and customer ratings and so forth. Next, you'll probably use a machine learning algorithm to learn a predictive model. For example, let's assume you chose to grow a decision tree, and you concluded that this particular customer is not creditworthy. Finally, you prepare a nice presentation visualizing the decision tree to answer your boss' next question: Why is this customer not creditworthy?

Kaan Buğra Kundakçı

Machine learning is often a big part of a "data science" project, e.g., it is often heavily used for exploratory analysis and discovery (clustering algorithms) and building predictive models (supervised learning algorithms). However, in data science, you often also worry about the collection, wrangling, and cleaning of your data (i.e., data engineering), and eventually, you want to draw conclusions from your data that help you solve a particular problem.

Denny Daskalov

While AI tools present a range of new functionality for businesses, artificial intellignce also raises some ethical questions. Deep learning algorithms, which underpin many of the most advanced AI tools, only know what's in the data used during training. Most available data sets for training likely contain traces of human bias. This in turn can make the AI tools biased in their function.

Jalen Sepi Ozols

Artificial Intelligence will do wonders to help automate processes that, today, take time and manual labor but don’t contribute much to the bottom line or moving forward as a company. Automation will allow additional time and resources to be dedicated to what companies need to focus their energy on: customer experience.

Benjamin

In response to Tatum Okorie

While Big Data offers a ton of benefits, it comes with its own set of issues. This is a new set of complex technologies, while still in the nascent stages of development and evolution.  Some of the commonly faced issues include inadequate knowledge about the technologies involved, data privacy, and inadequate analytical capabilities of organizations. A lot of enterprises also face the issue of a lack of skills for dealing with Big Data technologies. Not many people are actually trained to work with Big Data, which then becomes an even bigger problem.

Well, i think every second programmer can be trained to work with AI. Of course it will take a while but an experienced professional programmer can become an AI developer for a few months i believe.

Professor Dodds

A lot of organizations claim that they face trouble with Data Security. This happens to be a bigger challenge for them than many other data-related problems. The data that comes into enterprises is made available from a wide range of sources, some of which cannot be trusted to be secure and compliant within organizational standards.  They need to use a variety of data collection strategies to keep up with data needs. This in turn leads to inconsistencies in the data, and then the outcomes of the analysis.

George Waters

Netflix is a content streaming platform based on Node.js. With the increased load of content and the complex formats available on the platform, they needed a stack that could handle the storage and retrieval of the data. They used the MEAN stack, and with a relational database model, they could in fact manage the data.

Tatum Okorie

While Big Data offers a ton of benefits, it comes with its own set of issues. This is a new set of complex technologies, while still in the nascent stages of development and evolution.  Some of the commonly faced issues include inadequate knowledge about the technologies involved, data privacy, and inadequate analytical capabilities of organizations. A lot of enterprises also face the issue of a lack of skills for dealing with Big Data technologies. Not many people are actually trained to work with Big Data, which then becomes an even bigger problem.

Sanjeev Jehoram Moriarty

Data volumes are continuing to grow and so are the possibilities of what can be done with so much raw data available. However, organizations need to be able to know just what they can do with that data and how much they can leverage to build insights for their consumers, products, and services. Of the 85% of companies using Big Data, only 37% have been successful in data-driven insights. A 10% increase in the accessibility of the data can lead to an increase of $65Mn in the net income of a company.

Alex Tetradze

80% of the data getting generated today is unstructured and cannot be handled by our traditional technologies. Earlier, an amount of data generated was not that high. We kept archiving the data as there was just need of historical analysis of data. But today data generation is in petabytes that it is not possible to archive the data again and again and retrieve it again when needed as Data scientists need to play with data now and then for predictive analysis unlike historical as used to be done with traditional.

Ruslan Grześkiewicz

Because data science is a broad term for multiple disciplines, machine learning fits within data science. Machine learning uses various techniques like regression and supervised clustering. On the other hand, ‘data’ in data science may or may not evolve from a machine or a mechanical process. So, the main difference between the two is that data science as a broader term not only focusses on algorithms and statistics but also takes care of the entire data processing methodology.

Lovro Dzvezdan Lam

Data science, analytics, and machine learning are growing at an astronomical rate and companies are now looking for professionals who can sift through the goldmine of data and help them drive swift business decisions efficiently.

Baldur Helgason

The need for big data velocity imposes unique demands on the underlying compute infrastructure. The computing power required to quickly process huge volumes and varieties of data can overwhelm a single server or server cluster. Organizations must apply adequate compute power to big data tasks to achieve the desired velocity. This can potentially demand hundreds or thousands of servers that can distribute the work and operate collaboratively.

Oberto

Python is the most common coding language I typically see required in data science roles, along with Java, Perl, or C/C++. Python is a great programming language for data scientists.  Are there any other technical skills a data scientist needs to have?

Timotej Vlašič

In response to Shila Vasuda Gupta

Currently, major companies are investing in AI to handle difficult customers in the future. Google's most recent development analyzes language and converts speech into text. The platform can identify angry customers through their language and respond appropriately. :)

Shila,

I am glad you brought that up.  Artificial intelligence is implemented in automated online assistants that can be seen as avatars on web pages. It can avail for enterprises to reduce their operation and training cost. A major underlying technology to such systems is natural language processing.

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