Why is Machine Learning Important?
ML today has gone beyond being just a hyped-up term. Its ability to
think like a human and its application in a variety of business
verticals has tremendous potential to revolutionise lives and
livelihoods. ML technology has limitless applicability and can be
implemented in every sector like education, health, cybersecurity,
Industry 4.0, retail, etc.
The world is seeing rapid and complex technological advancements and
will require complex and skillful solutions. ML engineers will build
these systems and the demand for machine learning engineers will
Surveyed Data Scientists and C-level executives state that the top drivers of ML adoption are due to
Who is a Machine Learning Engineer?
A master’s degree in computer science, mathematics, or a relevant
field is a prerequisite. A Machine Learning Engineer will definitely see
some overlapping areas with a Data Scientist or a Data Analyst. However
the focus areas are different. Data Scientists and Analysts mainly
prioritize extracting insights from the data to make better business
decisions due to which they should know ML algorithms. Machine Learning
Engineers focus exclusively on building ML software.
Machine Learning Specialist or engineer is a worldwide in-demand job
today with excellent career prospects. From the big companies like
Microsoft, Google, Amazon, Apple to even start-ups and established IT
services, like Orion seek ML engineers.
Roles and Careers
The tiles for an ML Engineer can vary from Data Scientist, ML/MLOps
Engineer Analyst, ML Researcher, Data & Analytics Consultant, etc.
Let's assess some of the domain-specific knowledge and skills needed to become a Machine Learning Engineer.
Machine Learning engineers need to be conversant with CS concepts
like data structures, algorithms, space and time complexity, etc. A good
knowledge of programming languages like Python and R, Spark and Hadoop ,
SQL for database management, Apache Kafka for data pre-processing, etc.
is very important.
Obviously, an ML Engineer should have a sound knowledge of all the
common machine learning algorithms such as Support Vector Machine,
Apriori Algorithm, Decision Trees, Naïve Bayes Classifier, K Means
Clustering, Linear Regression, Logistic Regression, Random Forests, etc.
Maths finds various applications in ML. Mathematical formulas are
used in selecting the correct ML algorithm and parameters etc. ML
algorithms are derived from statistical modeling procedures and hence
it’s an advantage to have a strong foundation in Maths.
NLP aims to train computers in the complexities of the human language
so that machines can better understand human communication. NLP
libraries have various functions to instruct computers to understand
natural language. The Natural Language Toolkit is the most popular
platform for NLP applications.
A Machine Learning engineer should be proficient in data modeling and evaluation to understand the data and establish patterns.
Neural Networks are based on the functioning of the human brain
neurons. They comprise several layers like an input layer to receive
data, which is then processed through multiple layers that transform it
into valuable data suitable for output. These require a mastery of
parallel and sequential computations. There are several neural networks
like Feedforward Neural Network, Recurrent Neural Network, Modular
Neural Network, etc and an ML engineer must know the core fundamentals
How should companies hire an ML Specialist?
Companies need to weigh in their requirements carefully as hiring an ML expert. Hiring a machine learning engineer
requires a sound understanding of technology and job understanding.
Companies can hire a machine learning engineer or outsource from a
specialised AI company like Orion.
Availing the services through a specialised company will provide
access to a vast talent pool, give fast solutions and be more
we are reputed for value-driven services, an expert team of
professionals, and a customer-centric approach to provide end-to-end ML
Services. We can help you with advanced ML solutions that enable you to
analyse complex data more effectively so you can tap into the potential
of your data to become more competitive.
Source link: https://orionesolutions.com/machine-learning-in-2022-a-guide-for-businesses/
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