Tag Archives for " data science "
The AI Talent Gap: Locating Global Data Science Centers
Good AI talent is hard to find. The talent pool for anyone with deep expertise in modern artificial intelligence techniques is terribly thin. More and more companies are committing to data and artificial intelligence as their differentiator.
The early adopters will quickly find difficulties in determining which data science expertise meets their needs.
And the AI talent?
If you are not Google, Facebook, Netflix, Amazon, or Apple, good luck.
With the popularity of AI, pockets of expertise are emerging around the world.
For a firm that needs AI expertise to advance its digital strategy, finding these data science hubs becomes increasingly important.
In this article we look at the initiatives different countries are pushing in the race to become AI leaders and we examine existing and potential data science centers.
Race to Be a Global AI Power
It seems as though every country wants to become a global AI power.
- With the Chinese government pledging billions of dollars in AI funding, other countries don’t want to be left behind.
- In Europe, France plans to invest €1.5 billion in AI research over the next 4 years while Germany has universities joining forces with corporations such as Porsche, Bosch, and Daimler to collaborate on AI research.
- Even Amazon, with a contribution of €1.25 million, is collaborating in the AI efforts in Germany’s Cyber Valley around the city of Stuttgart.
- Not one to be left behind, the UK pledged £300 million for AI research as well.
- Other countries to commit money to AI are Singapore, which committed $150 million, and Canada, which not only committed $125 million but also has large data science hubs in Toronto and Montreal.
Yoshua Bengio, one of the fathers of deep learning, is from Montreal, the city with the biggest group of AI researchers in the world.
Toronto has a booming tech industry that naturally attracts AI money.
Data Scientists Worldwide
Examining a variety of sources, data science professionals are spread across the regions where we would expect them.
The graphic below shows the number of members of the site Data Science Central.
Since the site is in English, we expect most of its members to come from English-speaking countries; however, it still gives us some insight as to which countries have higher representation.
Global AI Hubs
It becomes difficult then to determine AI hubs without classifying talent by levels.
One example of this is India; despite its large number of data science professionals, many of them are employed in lower-skilled roles such as data labeling and processing.
So what would be considered a data science hub?
The graphic below defines a hub by the number of advanced AI professionals in the country.
The countries are shown here have AI talent working in companies such as Google, Baidu, Apple, and Amazon.
However, this omits a large group of talent that is not hired by these types of companies.
Global AI Talent Pool
Matching the previous graph with a study conducted by Element AI, we see some commonalities, but also see some new hubs emerge.
The same talent centers remain, but more countries are highlighted on the map.
Element AI’s approach consisted of analyzing LinkedIn profiles, factoring in participation in conferences and publications, and weighing skills highly.
Search for AI Talents
As you search for AI talent, we recommend basing your search on 4 factors:
- workforce availability,
- cost of labor,
- English proficiency,
- and skill level.
Kaggle, one of the most popular data science websites, conducted a salary survey with respondents from 171 countries. The results can be seen below.
Salaries of AI Talent
Salaries are as expected, but show high variability.
By aggregating salary data and the talent pool map, you can decide which countries suit your goals better.
The EF English Proficiency Index shows which countries have the highest proficiency in English and can further weed out those that may have a strong AI presence or low cost of labor, but low English proficiency.
In the end,
you want to hire professionals that understand the problems you are facing and can tailor their work to your specific needs.
With a global mindset, companies can mitigate talent scarcity.
If you are considering sourcing talent globally, we recommend hiring strong leadership locally, who act as AI product managers that can manage a team.
Hire production managers located on-site with your global talent.
They can oversee any data science or AI development and report back to the product manager.
KUNGFU.AI will continue to study these global trends and help ensure companies are equipped with access to the best talent to meet their needs.
Source: gigaom.com
Using Your Whole Data Lake: How the Operational Facilitates the Predictive
Many companies in the corporate world have attempted to set up their first data lake. Maybe they bought a Hadoop distribution, and perhaps they spent significant time, money, and effort connecting their CRM, HR, ERP, and marketing systems to it.
And now that these companies have well-crafted, centralized data repositories, in many cases…they just sit there.
But maybe data lakes fall into disuse because they’re not being looked at for what they are. Most companies see data lakes as auxiliary data warehouses.
And, sure, you can use any number of query technologies against the data in your lake to gain business insights.
But consider that data lakes can – and should – also serve as the foundation for operational, real-time corporate applications that embed AI and predictive analytics.
These two uses of data lakes — for (a) operational applications as well as for (b) insights and predictive analysis — aren’t mutually exclusive, either. With the right architecture, one can dovetail gracefully into the other.
But what database technologies can query and analyze, build machine learning models, and power microservices and applications directly on the data lake?
Join us for this free 1-hour webinar from GigaOm Research. The Webinar features GigaOm analyst Andrew Brust, and Splice Machine CEO and Co-Founder, Monte Zweben.
The discussion will explore how to leverage data lakes as the underpinning of application platforms, driving efficient operations, and predictive analytics that supports real-time decisions.
In this 1-hour webinar, you will discover:
- Why data latency is the enemy and data currency is key to digital transformation success
- Why operational database workloads, analytics, and construction of predictive models should not be segregated activities
- How operational databases can support continually trained predictive models
Register now to join GigaOm Research and Splice Machine for this free expert webinar.
Who Should Attend:
- CIOs
- CTOs
- Chief Data Officers
- Digital Transformation Facilitators
- Application Developers
- Business Analysts
- Data Engineers
- Data Scientists
Source: gigaom.com