So what really is ai?
AI if the science that empowers
computers to mimic human intelligence like
decision-making reasoning text processing and beholding.
AI if the broader general field that
entails several subfields like machine
learning robotics and computer vision.
Official intelligence market worldwide is projected to grow by 284 billion
dollars driven by a compounded growth of 43.9%.
Before we start with the primary
career path in AI please note that the AI field is new and emerging and also these definitions and job
titles dramatically change looking on
the scale of the corporate and the industry similarly.
Data Scientists
Data science is that the science of extracting useful information from an oversized unstructured amount of information.
The goal of a knowledge
scientist is to resolve company
problems using data.
Data science entails everything that has something to try and do with data like
data collection, data cleaning visualizing the information analyzing it and creating predictive models supported.
The data in step with Forbes
data science if the simplest job
in America for the fourth year in an
exceedingly row with a median base salary of 108 thousand dollars a year
Data scientist job responsibilities vary greatly betting on the dimensions
of the corporate.
Generally speaking data scientist analyzes big data but form a B testing and perhaps build simple statistical
models in scikit-learn.
Data scientists generally have solid foundation in mathematics statistics
probability and a few knowledge
of machine learning still.
Preferably data scientists have a engineering
science degree strong coding background preferably in python r or Scala although i've got seen many data scientists with diverse backgrounds and plenty of degrees yet.
Machine Learning Engineer
Machine learning engineers are essentially computer programmers with solid
software skills who can build train and deploy complex predictive models and
program machines to perform specific tasks that would add business value
Machine learning engineers are essentially software engineers who focuses on machine learning.
Machine learning engineers must bear
many iterations of the machine learning workflow like model building training hyper parameters optimization testing
and deployment of the models in production.
Machine learning engineers need a solid understanding of assorted tools and frameworks to make and train
AI and ML models like tensor
flow Kerris AWS stage maker and pie tours also.
In order to land a solid job as a machine learning engineer you'll need coding experience in Java
Python or
Scalar.
A background process of the masters or PhD degree in mathematics or applied science further.
AI Research Scientist
AI research scientists are phd's computer scientists who publish research
papers in AI and push the boundaries of science in AI.
AI research scientists possess expert level knowledge in AI generally in deep learning perception
and computer vision additionally.
AI researchers have the safest job on the
world.
Toby Walsh which is a man-made
intelligence professor at the University of latest South Wales says I always joke that the safest job on the world is AI researcher.
When we have automated AI researchers the machines will literally be able to do everything else by
definition.
AI Architect
AI architects are different compared to machine learning engineers and data
scientists.
Ai architects study the large picture of the AI project and that they are answerable for developing the
architecture of the AI project managing the
strategy coordination and planning of the AI project yet.
Ai architects generally have a few
years of experience in developing AI and ml projects.
An AI architect will must understand
clients needs formulate system level requirements choose the correct technology create and
maintain in architecture using cutting-edge high-tech frameworks and translate
customer requirements into business solutions that may effectively be implemented in business and add value immediately.
Ai architects generally engage with leadership internal product teams to know product roadmaps and future
needs also.
The requirements are Bachelor of Science or Master of Science in EE Computer
Engineering computing or
mathematics and possibly also
strong knowledge in programming like
Python and C++ further.
You need to own a background
with deep neural network training inference optimization in leading frameworks like Python, 10 tensorflow and tensor
R3 further.
You'll also need some experience with performance modeling architecture
simulation and benchmarking analysis still.
Big Data Engineer or Architect
Big data engineers or architects play a key role in designing building big data
environment in Hadoop and spark systems Big Data engineers are generally
experts in data warehousing solutions they'll
work with petabytes of knowledge
on a routine and hiring managers
hunt for Big Data engineers.
Who have strong programming experience in Java C++ PHP Python and scale similarly
Big Data engineers should have strong knowledge in databases like mango DB or Redis.