MACHINE LEARNING IN 2022: A GUIDE FOR BUSINESSES
Artificial Intelligence has become a common term in the world of computing and has percolated into all areas of business and day-to-day life. The wonder here is how a computer can understand and respond logically. This is what defines Machine Learning.
Machine Learning is the technology that focuses on making smart machines where they can be more independent and human-like and are capable of self–learning. It is based on other interdependent technologies like Artificial Intelligence and Data Science.
This blog attempts to throw insights into the requirements of ML in today’s world and the skill sets and qualifications required to become a Machine Learning Engineer.
- Market Growth: The value of the global machine learning market is forecasted to touch USD 117 billion by 2027.
- Market Adoption-
- ML investments by companies are growing most often by 25% with the banking, manufacturing, and IT industries allocating large budgets.
- 20% of C-level executives (across ten countries and 14 different industries) stated that ML is a core part of their business.
(GlobeNewswire, Algoorithmia, Mckinsey, research.aimultiple.)
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 increase exponentially.
Surveyed Data Scientists and C-level executives state that the top drivers of ML adoption are due to
- Access to better quality information (60%)
- Better value from data (31%)
- Boosting Business Processes Productivity and Speed (48%)
- Reduced costs (46%)
- 65% of companies planning to adopt machine learning say it’s to help them with decision-making.
- Businesses who have implemented machine learning models report increased revenues, costs reductions or operational efficiency.
- Google’s Deep Learning machine learning program has a high accuracy rate of 89% in detecting breast cancer.
- Google translates accuracy increased from 55% to 85% with an ML algorithm.
- Azure Machine Learning framework can accurately forecast stock market highs and lows by 62%
(Refinitiv, MemSQL, Microsoft, Google, Research.aimultiple.)
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.
Major job responsibilities of a Machine Learning Engineer include:
- Designing and building ML and deep learning systems
- Conducting machine learning tests and experiments
- Executing appropriate ML algorithms
- Study data science prototypes
- Work on datasets and data representation methods
- Extend current ML libraries and frameworks
Let's assess some of the domain-specific knowledge and skills needed to become a Machine Learning Engineer.
Computer Science and Programming Languages
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.
Machine Learning Algorithms
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.
Natural Language Processing
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.
Data Modeling and Evaluation
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 of them.
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 economical.
At Orion, 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.
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