Machine Learning is no more a buzzword – it is a living reality of our time that has given birth to numerous unique professions in the Data Science domain. From being a technology that was once out of reach for small and medium-sized enterprises, ML is now a mainstream technology, thanks to the public cloud.
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Today, the top cloud computing platforms like Amazon (AWS), Google (ML Engine), and Microsoft (Azure) have helped democratize AI and ML and made them both accessible and affordable. Both big and small cloud platforms are reinventing AI and ML to create innovative services that can posit these disruptive technologies within organizational structures.
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Of all the services offered by the cloud, ML platforms are one of the fastest-growing services. This is mainly because of the versatility with which they can be rolled out. Contrary to other cloud-based services, cloud ML platforms can be delivered through a host of different delivery models, including cognitive computing, GPU-based computing, automated machine learning, and ML model management.
As an increasing number of enterprises across all industrial sectors are leveraging ML, it is boosting the employment prospects in this domain. According to Indeed’s 2019 report of “The Best Jobs in the US,” Machine Learning Engineer is the top-ranking job with a staggering 344% growth and an average base salary of $146,085 per year!
And with the cloud evolving as a major destination for ML-based projects and services, careers in the cloud are skyrocketing as well. Even in India, It is one of the highest paid job for freshers. It is estimated that by 2021, the total revenue for cloud computing services will exceed $300 billion.
The new mind in the industries must be approaching via machine learning vs cloud computing, rather they are complimentary. Machine learning provides intelligence to the software or machine whereas the cloud provides storage space and security. So, there is should not be any question of “Which is better cloud computing or machine learning”? Rather they both have their importance.
Machine Learning in the cloud is the new trend in the industry because when combined, the potential and capabilities of both ML and the cloud multiply. Since ML in the cloud doesn’t demand a specific set of advanced skills (a basic knowledge of ML concepts and the cloud platform will do), it presents a wonderful opportunity for career building. Also, the cloud makes ML models/services much more scalable, offering enough scope for meeting dynamic business requirements.
Now, without further ado, let’s look at some of the most in-demand ML in the cloud job profiles.
Top 7 Machine Learning & Cloud Job Profiles
1. Machine Learning Engineer
The job profile of an ML Engineer is one of the most sought-after roles in the Data Science domain. ML Engineers are primarily responsible for designing and implementing ML algorithms using different programming languages and ML libraries. These ML algorithms are then used to process and analyze large datasets to extract and uncover meaningful patterns from them.
Some of the skills required by the machine learning engineer are mentioned below-
- Probability and Statistical Concepts
- Machine Learning Algorithms
- Data Modelling
- Applied Mathematics
- Computer Science
- Neural Networks
- Natural Language Processing
- Deep Learning
Machine learning has a high demand and the market is expanding. The market is expected to reach USD 209.91 billion by 2029 (Source).
On average the salary of a machine learning engineer is 7.5 lakhs per annum. The average salary ranges from 3.5 lakhs per annum to 22.0 lakhs per annum (Source).
2. Data Scientist (using the cloud)
In light of the ever-increasing pile of Big Data, the cloud has become the ultimate destination for storing and accessing data. Hence, understanding the functioning of the cloud is pivotal for a Data Scientist. Usually, Data Scientists have to work with a variety of data (structured, semi-structured, unstructured), analytics tools, and programming languages by leveraging the cloud platform.
Some of the skills required from a data scientist are mentioned below-
- Data Management
- Programming Languages
- Tools understanding
- Big Data Understanding
- Cloud Computing Services
- Data Acquisition, Data Cleansing, Data Modelling, etc.
Data is central to the industry, the organizations require the data to be managed appropriately and thus they invest to hire experts who can make sense of the data. Data is everywhere around us, and the demand for data professionals is increasing. The market for data science is expected to grow CAGR of 16.43% from 2022 to 2030 (Source).
It is a high-paying industry, the average salary of a data scientist is 10.5 lakhs per annum. The average salary ranges from 4.5 lakhs per annum to 25.9 lakhs per annum (Source). So, the apprehension “Which is better machine learning or cloud computing?” is subjective and is guided by various factors.
3. Data Engineer
Data Engineers develop, construct, test, and maintain important data architectures, including databases and large-scale processing systems within an organization. Data engineers often deal with raw data that may or may not be validated, and hence, it may contain human or machine errors. They use different tools and programming languages to enhance data quality, efficiency, and reliability.
Some of the skills required by the data engineer are mentioned below-
- Machine learning
- Programming languages
- Tools understanding
- Data Architecture
- Data Warehousing
- Data Management
Data Engineering is a growing field and the market is expanding. The market is expected to reach USD 86.9 billion in 2027 (Source).
It is a high-paying field, and the average salary of a data engineer is 8.2 lakhs per annum. The average salary ranges from 3.2 lakhs per annum to 21.0 lakhs per annum (Source).
4. DevOps Engineer
DevOps Engineers are IT experts who are well-versed in the Software Development Life Cycle (SLDC). They work in close collaboration with Software Developers and Operations teams to handle and oversee code releases. DevOps Engineers usually have an excellent understanding of automation tools required for building digital pipelines (CI/CD pipelines). They deploy product updates, identify issues (if any) in production, and implement the necessary integrations to meet customer needs.
Some of the skills required from a DevOps Engineer include
- Testing skills
- Concepts of Container
The market for DevOps Engineering is expected to reach USD 12,215.54 million by 2026 (Source).
The average salary of a DevOps Engineer is 6.0 lakhs per annum. The average salary ranges from 4.2 lakhs per annum to 12.5 lakhs per annum (Source).
5. Software Engineer/Developer (Machine Learning)
Software Engineers/Developers are primarily responsible for developing software that can solve business problems and challenges. Software Engineers/Developers employ various ML techniques and tools throughout the SLDC to analyze customer needs and design, test, and develop the software accordingly. They must continually work to improve the system and product quality by identifying issues, fixing them, and finding new opportunities for improvement using different ML tools and algorithms.
Skills required from a software engineer in today’s market include
- Programming skills
- Understanding of tools
- Software Testing
- Backend Development
- Frontend Development
- Time Management
- Verbal and written communication
The market for software engineering is expected to reach USD 123.5 Billion (Source).
The average salary of a software engineer is 5.3 lakhs per annum. The average salary ranges from 3.0 lakhs per annum to 14.0 lakhs per annum (Source).
6. Deep Learning Engineer
Deep Learning Engineers are ML experts who specialize in Deep Learning platforms. Their primary task is to develop intelligent programming models/systems that can mimic the function of the human brain. To achieve this end, Deep Learning Engineers use artificial neural networks for building machines that can operate without human intervention and learn from experience.
The skill sets required from a Deep Learning Engineer are-
- Deep Learning
- Data Engineering skills
- Computer Science
The average market growth of deep learning engineering is USD 49.6 billion in 2022 (Source).
The average salary of a deep learning engineer is 8.0 lakhs per annum. The average salary ranges from 3.5 lakhs per annum to 21.0 lakhs per annum (Source).
7. Technical Program Manager
Technical Program Managers are responsible for overseeing and managing all kinds of technical projects through every stage of development, from idea and design to completion. Technical Program Managers continually work to identify new sources of revenue for the company and develop new products to increase company profits. They lead teams of Project Developers and Designers and report to upper-level management executives.
Some of the skills required from a technical program manager include-
- Project Management
- Technical understanding
- Resource utilisation
- Research Aptitude
- Business Acumen
- Planning, Design, and Development
The average salary of a technical program manager is 26.4 lakhs per annum. The average salary ranges from 13.8 lakhs per annum to 45.0 lakhs per annum (Source). The average salaries may differ due to various factors such as location, skill set, experience, organisation, etc.
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To conclude, the career prospects in ML and the cloud look highly promising. As these domains are ever-evolving, new breakthroughs are being made every day. The rapid growth and adoption of ML and cloud technologies are further creating a massive demand for professionals with ML and cloud computing skills.
Another great thing about a career in ML and the cloud is that the shortage of talent in these spheres has pushed the salaries of most job profiles to six figures. So, rest assured, anyone wishing to build a career in ML or the cloud can look forward to a promising future while making tons of money!
If you are interested to learn about cloud computing and Machine learning, upGrad in collaboration with IIT- Bangalore, has launched the Master of Science in Machine Learning & AI. The course will equip you with the necessary skills for this role: maths, data wrangling, statistics, programming, cloud-related skills, as well as ready you for getting the job of your dreams.