Machine Learning Engineer Salary: How Much Can You Make?

In this article let's learn about Machine Learning Engineer Salary: How Much Can You Make?. The notion of machine learning has been known for a long time, with Alan Turing’s Enigma machine from World War II serving as the first practical example. Currently, machine learning is employed in virtually every area of our lives, from simple everyday chores to more complicated computations involving large amounts of data. Google’s self-driving car, for example, is powered by machine learning, which also powers personalized recommendations on websites such as Netflix, Amazon, and Spotify. Because of the growing demand for machine learning, India has one of the highest compensation rates in the world.

Machine learning’s primary purpose is to assist businesses in improving their organizations’ overall functioning, efficiency, and decision-making processes by analyzing large volumes of data. As algorithms enable machines to learn, organisations will be able to identify patterns in data that will help them make better decisions without the need for human engagement. This will allow businesses to save time and money by eliminating the requirement for human interaction.

Who is a Machine Learning Engineer?

A Machine Learning Engineer is an avid programmer who helps machines understand and pick up knowledge as required. Their core deliverables include creating programs that enable machines to take specific actions without explicit directions.

Apart from programming, Machine Learning engineers are also responsible for customising data sets for analysis, personalising web experiences, identifying and predicting business requirements. This role also demands exceptional communication skills since they often collaborate with other teams to drive different optimisation projects. Hiring companies typically look for candidates with a master’s degree and a few years of experience in similar roles.

Following are a few of the Responsibilities of a Machine Learning Engineer

Creating Machine Learning programs using ML libraries
Experimenting with various machine learning programs to test their efficiency
Adapting Machine Learning programs for scalability
Maintaining data flow between database and backend systems
Debugging custom machine learning codes
Optimising Machine Learning technologies in production environment

Machine Learning Engineer Job Description

We are looking for a Machine Learning Engineer to develop ML algorithms and make them production-ready. Our research focuses on human-ageing related diseases. The ideal candidate must have some background in research-oriented responsibilities to support us with relevant tools and programs. S/he will cross-function to create world-class machine learning platforms to advance our research effort. They will play a critical role in defining and executing optimisation strategies in computational biology and machine learning. Our biochemical formulas require candidates who can handle different types of biological data in huge volumes.

Ideal candidates should be equipped with various data analysing techniques. They should have prior experience in implementing, extending, and debugging machine learning techniques. They should be able to design and build high-leverage data infrastructure and tools.

Responsibilities of a Machine Learning Engineer

Prototypes in data science should be studied and converted.
Machine Learning systems and schemes must be designed and developed.
Using test findings, undertake statistical analysis and fine-tune models.
To locate available datasets for training purposes on the internet.
To train and retrain machine learning systems and models as needed.
Extend and improve existing machine learning frameworks and libraries.
To create Machine Learning apps that meet the needs of customers and clients.
To investigate, test, and deploy appropriate machine learning algorithms and tools.
To assess machine learning algorithms’ problem-solving skills and applications and rate them according to their likelihood of success.
to investigate and observe

A sub-branch of Artificial Intelligence, Machine Learning demands a basic understanding of all the major AIML concepts. Machine Learning Engineers, in particular, are expected to be familiar with computer science, a little bit of data science, consumer trends and more. The following skillsets are, however, mandatory requirements to excel in the domain:

Machine Learning Engineer Skills

Programming Language Knowledge: One of the foremost requirements of a career in Machine Learning is programming skills. There are different programming languages like Python, R, Java and C++ for different functions. While Python is the most commonly used machine learning language owing to its versatility and flexibility, other languages have their own benefits. For example, C++ is best suited to speed up your codes and R works better for statistics and plots. All these languages together help a machine language expert to understand data structures, memory management and class structure.
Probability and Statistics: Machine Learning engineers need to be adept in statistical concepts like Mean, Regression, Gaussian Distributions and Standard Deviations. Knowledge of probability theory is important for creating algorithms for Hidden Markov models, Gaussian Mixture Models, and Naive Bayes. These probability techniques help an ML engineer to handle the uncertainties of real-world challenges. Apart from distribution models, Statistical knowledge also equips ML engineers to work on analysis methods like hypothesis testing and ANOVA. In fact, a lot of the machine learning algorithms build on existing statistical models.
Data Modeling & Evaluation: Data modelling helps Machine Learning professionals to create or estimate the structures of any given dataset. Essentially, data modelling allows data scientists and ML engineers to prepare the data set for any specific kind of analysis. This process helps in identifying patterns (clusters, correlations, etc), predicting properties (classification, anomaly detection, regression) and creating the analysis models accordingly. The data evaluation process further helps by choosing the best model to represent the data. Data evaluating can also help in estimating the success of any data model.
Distributed Computing: Machine Learning experts often work with large data sets which involve using multiple machines. Knowledge of projects like Apache Hadoop and cloud services like Amazon EC2 comes handy in such situations to distribute it in clusters.
Signal Processing Techniques: Feature extraction is a crucial part of machine learning. Hence it is important for ML professionals to know signal processing techniques to solve different problems. Apart from the advanced signal processing algorithm (Wavelets, Curvelets, Bandlets, Shearlets etc), time-frequency analysis also helps ML engineers in complex problem-solving.
Computer Science Fundamentals: Computer Science fundamentals like computer architecture, data structure, computability and complexity are important for a machine learning engineer to implement or modify programs according to requirements.
Machine Learning Algorithms and Libraries: Even though ML libraries and packages are freely available with algorithms, not all of them are suited for all kinds of applications. ML engineers should know how to apply them effectively to optimise the outcome. Choosing the right data model, algorithm, and learning procedure is as important as knowing the libraries or languages. ML engineers should be able to discern the advantages and disadvantages of any particular algorithm and when to use them.

Machine Learning Salary Trends in India

Planning to build a career in Machine Learning and wish to check your salary growth in 5 & 10 years, and compare your current salary v/s peers? Check out Great Learning’s Salary Builder and get powerful insights to grow your career.

Machine Learning Salary based on Experience

Experience Level	Salary
Beginner (1-2 years)	₹ 5,02,000 PA
Mid-Senior (5-8 years)	₹ 6,81,000 PA
Expert (10-15 years)	₹ 20,00,000 PA

Machine Learning Salary based on Experience

Machine Learning Salary based on Job Title

Job Title	Salary
Artificial Intelligence Researcher	₹ 9,00,000 PA
Machine Learning Engineer	₹ 9,29,923 PA

Machine Learning Salary based on Job Title

Machine Learning Salary based on Company

Company	Size
Deloitte	₹ 6,51,000 PA
Amazon	₹ 8,26,000 PA
Accenture	₹15,40,000 PA

Machine Learning Salary based on Company

Machine Learning Salary in Other Countries

Here’s the list of salaries of Machine Learning Engineer in other countries:

Country	Salary
US	$140,675
Canada	$93,684
Australia	$106,532

Machine Learning Salary in Other Countries

In the US, the top companies hiring for this role are eBay, Wish, etc. The cities with the highest salaries are San Francisco Bay Area, Cupertino, and Santa Clara, etc.

In Canada, the top companies hiring for this role are OCAD University, Workday, etc. The cities with the highest salaries are Waterloo, Vancouver, etc.

In Australia, the top company hiring for this role is CSIRO. The cities with the highest salaries are Sydney, Melbourne, Perth, etc.

Machine Learning Salary in India based on Skills

Skills	Average Salary
Machine Learning	7 Lakhs Per Annum
Natural Language Processing	7.3 Lakhs Per Annum
Artificial Intelligence	8 Lakhs Per Annum
Deep Learning	7.5 Lakhs Per Annum
Computer Vision	7.25 Lakhs Per Annum

Machine Learning Salary in India based on Skills

A Day in the Life of a Machine Learning Engineer

Machine Learning engineers usually spend a lot of time programming but before they get into that they start their day by catching up on their emails. Pretty basic right?

You might think that ML engineers function like the rest of us, going through the day managing various routine work. However, you’d be surprised to know that Machine Learning engineers need to work on a lot of interdisciplinary tasks, ranging from data science, analytics, business communication and more. We have tried to put all the tasks together that a machine learning engineer engages in on a typical day.

Check the models that have been active for a while
Connect with the rest of the team for updates
Look through task management platforms to schedule the day
Analyse company codebase using Scikit learn to look for bugs
Code with PyCharm to implement a model or keep the interfaces of a database running
Meet stakeholders to ensure products are updated with new features and changes are implemented according to plans
Discuss how to optimise products and create plans and processes for it
Research on the latest trends in the domain and how the company can benefit from it

What are The Advantages of a Machine Learning Course?

1. More opportunities for advancement and advancement in your career

According to TMR, MLaaS (Machine Learning as a Service) is expected to rise from $1.07 billion in 2016 to $19.9 billion by the end of 2025. This is an astounding level of increase, both in terms of raw numbers and year-over-year comparisons.

Machine learning makes a mockery of anything that can be described as “important” on a financial or global scale. If you want to push your profession to the next level, Machine Learning can help you achieve it. Machine Learning can also help you get involved in something that is both global and relevant today.

2. Increased Salaries

The greatest machine learning engineers nowadays are paid as much as really well-known athletes! That is not an exaggeration! The average machine learning engineer income is 8 lakhs per year, according to Glassdoor.co.in – and that’s only at the beginning of one’s career! A skilled machine learning expert might earn anywhere between 15 to 23 lakhs per year.

2. Salary Increases

Today’s top machine learning engineers are paid on par with world-famous athletes! That is not hyperbole! According to Glassdoor.co.in, the typical machine learning engineer earns Rs. 8 lakhs per year – and that’s only at the start of their careers! A proficient machine learning expert can expect to earn between 15 and 23 lakhs per year.

3. Corporations are afflicted by a scarcity of machine learning skills.

Given the rapid rate of technological advancements, many businesses have been forced to play catch-up. The truth is that there are simply not enough machine learning professionals to meet new industry expectations in the digital transformation business.

4. Data science and machine learning are inextricably connected.

Because of its all-explaining nature, as well as its financial and inventive viability, Data Science currently rules the people in the same way that religion governed the people for millennia before modernity.

And Data Science is only a phantom of Machine Learning in terms of functionality. The ability to become adept in each of these areas will allow you to analyze a horrifying amount of data and then extract value and give insight from it, which will propel your career to new heights.

Furthermore, because ML engineers and Data Scientists frequently collaborate on products in many organisations, if you’ve already worked as an ML engineer, you may find yourself exposed to the Data Scientists’ point of view as a result of your previous work.

How to Become a Machine Learning Engineer

Machine Learning has established itself as a promising domain for professionals who want to make a difference in the fast-changing digital economy. Upskilling in this field will land you lucrative offers from international brands. Great Learning’s PGP-Machine Learning offers a comprehensive course structure that prepares candidates with industry insights to meet real-world challenges.

Research and find job openings that meet your skillset and then apply for them. Here’s a list of articles that will help you understand the fundamental concepts of Machine Learning and prepare you for the interview:

Your job doesn’t end at nailing the interview. You must keep yourself updated on Machine Learning trends and company goals to grow in the role.

Machine Learning Career Path – How to Grow in ML Roles

Machine Learning Career Path

Original article source at: https://www.mygreatlearning.com

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Machine Learning Engineer Salary: How Much Can You Make?

sophia tondon

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5 Latest Technology Trends of Machine Learning for 2021

Check out the 5 latest technologies of machine learning trends to boost business growth in 2021 by considering the best version of digital development tools. It is the right time to accelerate user experience by bringing advancement in their lifestyle.

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Nora Joy

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Hire Machine Learning Developers in India

Hire machine learning developers in India ,DxMinds Technologies is the best product engineering company in India making innovative solutions using Machine learning and deep learning. We are among the best to hire machine learning experts in India work in different industry domains like Healthcare retail, banking and finance ,oil and gas, ecommerce, telecommunication ,FMCG, fashion etc.
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Hire machine learning developers in India ,DxMinds Technologies is the best product engineering company in India making innovative solutions using Machine learning and deep learning. We are among the best to hire machine learning experts in India work in different industry domains like Healthcare retail, banking and finance ,oil and gas, ecommerce, telecommunication ,FMCG, fashion etc.

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Maintenance / Support / Sustenance

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Reach us 917483546629

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Applications of machine learning in different industry domains

Machine learning applications are a staple of modern business in this digital age as they allow them to perform tasks on a scale and scope previously impossible to accomplish.Businesses from different domains realize the importance of incorporating machine learning in business processes.Today this trending technology transforming almost every single industry ,business from different industry domains hire dedicated machine learning developers for skyrocket the business growth.Following are the applications of machine learning in different industry domains.

Transportation industry

Machine learning is one of the technologies that have already begun their promising marks in the transportation industry.Autonomous Vehicles,Smartphone Apps,Traffic Management Solutions,Law Enforcement,Passenger Transportation etc are the applications of AI and ML in the transportation industry.Following challenges in the transportation industry can be solved by machine learning and Artificial Intelligence.

ML and AI can offer high security in the transportation industry.
It offers high reliability of their services or vehicles.
The adoption of this technology in the transportation industry can increase the efficiency of the service.
In the transportation industry ML helps scientists and engineers come up with far more environmentally sustainable methods for powering and operating vehicles and machinery for travel and transport.

Healthcare industry

Technology-enabled smart healthcare is the latest trend in the healthcare industry. Different areas of healthcare, such as patient care, medical records, billing, alternative models of staffing, IP capitalization, smart healthcare, and administrative and supply cost reduction. Hire dedicated machine learning developers for any of the following applications.

Identifying Diseases and Diagnosis
Drug Discovery and Manufacturing
Medical Imaging Diagnosis
Personalized Medicine
Machine Learning-based Behavioral Modification
Smart Health Records
Clinical Trial and Research
Better Radiotherapy
Crowdsourced Data Collection
Outbreak Prediction

**
Finance industry**

In financial industries organizations like banks, fintech, regulators and insurance are Adopting machine learning to improve their facilities.Following are the use cases of machine learning in finance.

Fraud prevention
Risk management
Investment predictions
Customer service
Digital assistants
Marketing
Network security
Loan underwriting
Algorithmic trading
Process automation
Document interpretation
Content creation
Trade settlements
Money-laundering prevention
Custom machine learning solutions

Education industry

Education industry is one of the industries which is investing in machine learning as it offers more efficient and easierlearning.AdaptiveLearning,IncreasingEfficiency,Learning Analytics,Predictive Analytics,Personalized Learning,Evaluating Assessments etc are the applications of machine learning in the education industry.

Outsource your machine learning solution to India,India is the best outsourcing destination offering best in class high performing tasks at an affordable price.Business** hire dedicated machine learning developers in India for making your machine learning app idea into reality.
**
Future of machine learning
Continuous technological advances are bound to hit the field of machine learning, which will shape the future of machine learning as an intensively evolving language.

Improved Unsupervised Algorithms
Increased Adoption of Quantum Computing
Enhanced Personalization
Improved Cognitive Services
Rise of Robots

**Conclusion
**
Today most of the business from different industries are hire machine learning developers in India and achieve their business goals. This technology may have multiple applications, and, interestingly, it hasn’t even started yet but having taken such a massive leap, it also opens up so many possibilities in the existing business models in such a short period of time. There is no question that the increase of machine learning also brings the demand for mobile apps, so most companies and agencies employ Android developers and hire iOS developers to incorporate machine learning features into them.

#hire machine learning developers in india #hire dedicated machine learning developers in india #hire machine learning programmers in india #hire machine learning programmers #hire dedicated machine learning developers #hire machine learning developers

Nora Joy

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Hire Machine Learning Developer | Hire ML Experts in India

Machine learning applications are a staple of modern business in this digital age as they allow them to perform tasks on a scale and scope previously impossible to accomplish.Businesses from different domains realize the importance of incorporating machine learning in business processes.Today this trending technology transforming almost every single industry ,business from different industry domains hire dedicated machine learning developers for skyrocket the business growth.Following are the applications of machine learning in different industry domains.

Transportation industry

Machine learning is one of the technologies that have already begun their promising marks in the transportation industry.Autonomous Vehicles,Smartphone Apps,Traffic Management Solutions,Law Enforcement,Passenger Transportation etc are the applications of AI and ML in the transportation industry.Following challenges in the transportation industry can be solved by machine learning and Artificial Intelligence.

ML and AI can offer high security in the transportation industry.
It offers high reliability of their services or vehicles.
The adoption of this technology in the transportation industry can increase the efficiency of the service.
In the transportation industry ML helps scientists and engineers come up with far more environmentally sustainable methods for powering and operating vehicles and machinery for travel and transport.

Healthcare industry

Technology-enabled smart healthcare is the latest trend in the healthcare industry. Different areas of healthcare, such as patient care, medical records, billing, alternative models of staffing, IP capitalization, smart healthcare, and administrative and supply cost reduction. Hire dedicated machine learning developers for any of the following applications.

Identifying Diseases and Diagnosis
Drug Discovery and Manufacturing
Medical Imaging Diagnosis
Personalized Medicine
Machine Learning-based Behavioral Modification
Smart Health Records
Clinical Trial and Research
Better Radiotherapy
Crowdsourced Data Collection
Outbreak Prediction

**
Finance industry**

In financial industries organizations like banks, fintech, regulators and insurance are Adopting machine learning to improve their facilities.Following are the use cases of machine learning in finance.

Fraud prevention
Risk management
Investment predictions
Customer service
Digital assistants
Marketing
Network security
Loan underwriting
Algorithmic trading
Process automation
Document interpretation
Content creation
Trade settlements
Money-laundering prevention
Custom machine learning solutions

Education industry

Education industry is one of the industries which is investing in machine learning as it offers more efficient and easierlearning.AdaptiveLearning,IncreasingEfficiency,Learning Analytics,Predictive Analytics,Personalized Learning,Evaluating Assessments etc are the applications of machine learning in the education industry.

Outsource your machine learning solution to India,India is the best outsourcing destination offering best in class high performing tasks at an affordable price.Business** hire dedicated machine learning developers in India for making your machine learning app idea into reality.
**
Future of machine learning
Continuous technological advances are bound to hit the field of machine learning, which will shape the future of machine learning as an intensively evolving language.

Improved Unsupervised Algorithms
Increased Adoption of Quantum Computing
Enhanced Personalization
Improved Cognitive Services
Rise of Robots

**Conclusion
**
Today most of the business from different industries are hire machine learning developers in India and achieve their business goals. This technology may have multiple applications, and, interestingly, it hasn’t even started yet but having taken such a massive leap, it also opens up so many possibilities in the existing business models in such a short period of time. There is no question that the increase of machine learning also brings the demand for mobile apps, so most companies and agencies employ Android developers and hire iOS developers to incorporate machine learning features into them.

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Pros and Cons of Machine Learning Language

Amid all the promotion around Big Data, we continue hearing the expression “AI”. In addition to the fact that it offers a profitable vocation, it vows to tackle issues and advantage organizations by making expectations and helping them settle on better choices. In this blog, we will gain proficiency with the Advantages and Disadvantages of Machine Learning. As we will attempt to comprehend where to utilize it and where not to utilize Machine learning.

In this article, we discuss the Pros and Cons of Machine Learning.
Each coin has two faces, each face has its property and highlights. It’s an ideal opportunity to reveal the essence of ML. An extremely integral asset that holds the possibility to reform how things work.

Pros of Machine learning

**Effectively recognizes patterns and examples **

AI can survey enormous volumes of information and find explicit patterns and examples that would not be evident to people. For example, for an online business site like Amazon, it serves to comprehend the perusing practices and buy chronicles of its clients to help oblige the correct items, arrangements, and updates pertinent to them. It utilizes the outcomes to uncover important promotions to them.

**Do you know the Applications of Machine Learning? **

No human mediation required (mechanization)

With ML, you don’t have to keep an eye on the venture at all times. Since it implies enabling machines to learn, it lets them make forecasts and improve the calculations all alone. A typical case of this is hostile to infection programming projects; they figure out how to channel new dangers as they are perceived. ML is additionally acceptable at perceiving spam.

**Constant Improvement **

As ML calculations gain understanding, they continue improving in precision and productivity. This lets them settle on better choices. Let’s assume you have to make a climate figure model. As the measure of information you have continues developing, your calculations figure out how to make increasingly exact expectations quicker.

**Taking care of multi-dimensional and multi-assortment information **

AI calculations are acceptable at taking care of information that is multi-dimensional and multi-assortment, and they can do this in unique or unsure conditions. Key Difference Between Machine Learning and Artificial Intelligence

**Wide Applications **

You could be an e-posterior or a social insurance supplier and make ML work for you. Where it applies, it holds the ability to help convey a considerably more close to home understanding to clients while additionally focusing on the correct clients.

**Cons of Machine Learning **

With every one of those points of interest to its effectiveness and ubiquity, Machine Learning isn’t great. The accompanying components serve to confine it:

1.** Information Acquisition**

AI requires monstrous informational indexes to prepare on, and these ought to be comprehensive/fair-minded, and of good quality. There can likewise be times where they should trust that new information will be created.

**Time and Resources **

ML needs sufficient opportunity to allow the calculations to learn and grow enough to satisfy their motivation with a lot of precision and pertinence. It additionally needs monstrous assets to work. This can mean extra necessities of PC power for you.
**
Likewise, see the eventual fate of Machine Learning **

**Understanding of Results **

Another significant test is the capacity to precisely decipher results produced by the calculations. You should likewise cautiously pick the calculations for your motivation.

High mistake weakness

AI is self-governing yet exceptionally powerless to mistakes. Assume you train a calculation with informational indexes sufficiently little to not be comprehensive. You end up with one-sided expectations originating from a one-sided preparing set. This prompts unessential promotions being shown to clients. On account of ML, such botches can set off a chain of mistakes that can go undetected for extensive periods. What’s more, when they do get saw, it takes very some effort to perceive the wellspring of the issue, and significantly longer to address it.

**Conclusion: **

Subsequently, we have considered the Pros and Cons of Machine Learning. Likewise, this blog causes a person to comprehend why one needs to pick AI. While Machine Learning can be unimaginably ground-breaking when utilized in the correct manners and in the correct spots (where gigantic preparing informational indexes are accessible), it unquestionably isn’t for everybody. You may likewise prefer to peruse Deep Learning Vs Machine Learning.

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