Machine learning is facilitating computers to equip and address all the tasks that have been until then performed by man. Machine learning has been a driving force in triggering technological developments in the world.
From driving cars to translating speech, machine learning is igniting an eruption in the field of artificial intelligence – creating sense out of the complex and incalculable real world by the software.
What is Machine Learning?
Machine learning is an outlook to data analysis that automates analytical model building. It is a wing of artificial intelligence based on the idea that systems can discover data, demonstrate patterns, and develop choices with very little human interference.
How Important is Machine Learning? What are its Applications?
With the remarkable availability of data in the world today, collecting and obtaining deliverable results are vital for any business. The practical applications of machine learning accelerate business results which can strikingly influence the crux of a company.
New techniques are manifold briskly in this field and thereby bolstering the application of machine learning to boundless potentiality. Industries that rely on vast quantities of data—and need a system to evaluate it meticulously and accurately, have welcomed machine learning as the best possible way to frame models, arrange and plan.
- Image Recognition- fb – tagging, face recognition
- Speech Recognition such as from speech to text through google assistant
- Traffic Prediction – in maps
- Product recommendation
- Self-driving cars
- Email spam and Malware filtering
- Online Fraud Detection
- Medical Diagnosis
- Stock Market
According to Glassdoor.com the worldwide value of the machine learning market was $8 billion in 2019 and is likely to shoot up to $117 billion by the end of 2027 at a CAGR of 39%.
Machine Learning Job Opportunities:
- Machine learning engineer
- Data scientist
- Artificial Intelligence for IT Operations engineers
- Cybersecurity analyst
- Cloud architect for ML
- Computational linguist
- Human-centered AI systems designer/researcher
- Robotics engineer
- Software Developer
Machine Learning Salary Scales:
According to a 2019 Indeed report, Machine Learning Engineer is the #1 job in the list of The Best Jobs in the US, with a shrieking 344% growth and a median salary of $146,085 per year. The national average salary for Machine Learning jobs in India is 11,05,748 INR.
Who is Machine Learning Engineer?
A Machine Learning Engineer career path is one of the most desired and propitious ones in the field of Data Science. Machine Learning Engineers essentially deal with the style and establishments of ML systems and applications using ML algorithms and tools.
They engage in modeling and instituting powerful self-learning ML applications by executing statistical analysis and refining them using test results. They also manage and run numerous ML experiments using programming languages such as Python, Java, Scala, R, and C++, to name a few.
What Does a Machine Learning Engineer Do?
Machine learning engineers converge in software engineering and data science. They make use of big data tools and programming structures to ensure that the raw data collected from data pipelines are reanalyzed as data science models that are ready to be scaled as needed. Machine learning engineers cater for data into models defined by data scientists.
Machine learning engineers also construct programs that control computers and robots. The algorithms developed by machine learning engineers let a machine discover patterns in its own programming data and permits itself to understand commands and even think for itself.
Qualifications: The candidate should have either a master’s degree in computer science or a master’s or PhD in computer science, mathematics, or statistics and work experience in Java, Python, and R.
What Responsibilities are Carried out by a Machine Learning Engineer?
The responsibilities of a machine learning engineer will be aligned with the respective projects that they’re working on. Building algorithms is an essential responsibility of a Machine learning engineer.
Here’s what these roles typically demand:
- Developing machine learning models
- Responsible for the entire lifecycle from research to maintenance
- Cooperate with data engineers to develop data and model pipelines
- Implementing machine learning and data science techniques and design distributed systems
- Formulating production-level code
- Employ code
- Reviews existing machine learning models
- Fabricate project outcomes and isolate issues
- Execute machine learning algorithms and libraries
- Examine large and complex datasets and come up with valuable insights
- Investigate and execute best practices to strengthen existing machine learning infrastructure
How is ML Engineer different from Data Scientist?
A Data Scientist scrutinizes data in order to produce actionable insights. With the references that they are able to make, business decisions by the company executives. On the other hand, a Machine Learning Engineer also analyses data to create various machine learning algorithms that run autonomously with minimal human supervision. In simpler words, a Data Scientist creates the necessary outputs for humans while a Machine Learning Engineer creates them for machines.
As reported by Indeed.com, Machine Learning Engineer Is The Best Job of 2019 with a growth rate of 344% and an average base salary of $146,085 per year.
The call for accomplished and expert professionals in Machine Learning is increasing at a higher pace and in the future too it will only shoot up higher. Machine Learning career is prodigious with immense job satisfaction and security, it also guarantees hefty annual compensation and speedy career growdath. Indeed, all the more reasons to ponder over building a Machine Learning career path. To know more about Machine Learning and the career paths do check out DataMites Machine Learning Training.