Machine Learning (ML) has grown exponentially in the last decade to become the most demanding technology for the next generation. ML, taken as a subset of Artificial Intelligence (AI), is used to develop systems or algorithms that can first learn from data, discover patterns and concepts from this information, and then plan or make decisions based on these learnings.
Today, researchers worldwide use machine learning in their applications across several verticals, such as agriculture, banking, marketing, search engines, linguistics, medical diagnosis, etc.
ML is a popular 21st-century career with unlimited scope and potential for next-generation as more and more organisations rely on data to scale their growth. Machine Learning Engineer is a term associated with a professional building career in this field. Many companies also use Machine Learning Scientists, software engineers, or ML experts in their job descriptions. As per Glassdoor, a person working as a Machine Learning Engineer in 2022 is earning on average $114,000 per year in the US with additional perks, bonuses, and more.
Machine Learning has different subsets, including Neural Networks, Natural Language Processing (NLP), and Deep Learning (DL). Many industry verticals are leveraging ML in various aspects to enhance their business prospects for the future.
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Possibilities of New ML Applications
Machine Learning has opened pandora’s box for technologies to learn and build sophisticated models. Here are some of the main possibilities that can have a significant impact on our life altogether:
1. Sentiment analysis
Sentiments or emotions analysis from ML-based applications will help define the document’s tone or a customer review. This decision-making application will have the capability to realise the customer’s style by reading his review or any form and giving prediction based on its assessment.
2. Language translation
Natural language processing (NLP) has also progressed rapidly in the last decade in building a communication link between the human language and computer. Some of the crucial hurdles in NLP are natural language generation, speech recognition, and understanding the natural language progression.
3. User Behaviour and Recommendations—Products and Movies
ML-based models are also used to study the changing trends and user behaviour corresponding to the market. Product recommendation is among the most successful applications of ML. Every year, we see new designs and changes in products. These ML models make the system understand behaviour based on different parameters such as timing, mood, seasonal, choice, reference, and many others.
4. Medical Diagnosis—Healthcare
Medical diagnosis is among the most advantageous possibilities for Machine Learning. Moreover, ML–AI in healthcare have proven their success in defining treatment protocol, personalised care, monitoring, and developing drugs. Predicting heart failure from exam reports and discovering patterns from cardiovascular records is gaining popularity in healthcare.
Most of the global companies are using Machine Learning in their IT architecture in several aspects—Pinterest for discovering unique and engaging content, Yelp for Image curation, Neural network in Google, Baidu Voice search, highly Intelligent CRMS at Salesforce, Ecommerce conversion at Edgecase, curated timelines at Twitter, Chatbots at Facebook, Netflix for recommending movies, Amazon for promoting products, etc.
At the latest, the World Health Organization (WHO) and Massachusetts Institute of Technology (MIT) used ML and AI to study and respond to Corona outbreaks to understand its spreading behaviour.
How Has the Demand for Machine Learning in 2022 and Beyond Been Increased?
Machine Learning is continuously evolving as businesses are now shifting towards data and algorithms to study information. These study models are highly significant and shed insights into crucial factors in business growth. The global Machine learning market (ML), from its US$ projection of 8.43 billion in 2019, will increase at an alarming rate of 39.2% (CAGR) to US$ 117.19 billion by 2027.
Machine Learning Market Size and Growth: source
Machine Learning opens many career pathways for Data Science, Artificial Intelligence, Data Architect, Cloud Computing, Machine Learning as a Service (MLaaS), Big Data, and top executives level in organisations. With the rapid progress of deep learning in industries, several global companies are pushing their scope with ML and data analytics-driven solutions.
Some of the top MNCs for ML include IBM, Hewlett Packard (HP), Amazon Web Services (AWS), Google LLC, H2o.AI, Intel Corporation, Oracle Corporation, Microsoft Corporation, SAS Institute, Baidu, and more.
Applications in Retail, Healthcare and E-Commerce Industries
Today, Machine Learning has got integrated into more than 100 industries and counting. These aspects touch our lives daily and ease our decision-making capabilities. And with continuous research, this ML trend will further refine to build more sophisticated models for the future.
Global Machine Learning Market Share By Industry in 2019
The use of Machine Learning technology has significantly increased in the retail industry in the last few years. Today’s online platforms have incredible user experience with recommendation engines to add more visibility to their products or services. Visual search adds more credibility in reaching the desired results easier. Users can seamlessly upload the image to find their exact product, such as Google Lens and image search, Pinterest Lens Your Look, etc.
With modern economies changing user behaviour, machine learning algorithms help businesses in pricing strategies, offering discounts, and several cost optimisation techniques. ML-led systems have shown incredible success in predicting customer behaviour and giving them relevant offers to get more businesses conversions.
Machine Learning has shown remarkable success in the healthcare industry. Digital recording on smart devices helps medical professionals optimise proficiency, standardise decisions, and diagnose cancer elements in the human body with more accuracy and speed to get desired results. Various data and analytics models have come up in healthcare systems that add more reliability and trust.
Overall, ML-based algorithms have played a tremendous role in assessing diseases’ treatment and setting their protocols with long-term planning; several benefits have come from using ML–AI combination, including lower hospital stay, predicting chronic disease, lower mortality rate, analysing no show, lower readmissions, likely complication of conditions, and so on.
3. E-commerce Industries
Personalisation is one of the main benefits that have come up with the integration of Machine Learning. Here are the essential roles Machine Learning is involved in concerning e-commerce industries:
- Optimising web search with intelligent results with unique indicators.
- Detecting fraud from hundreds and thousands of transactions taking place every day.
- Product recommendations based on customer past historical and browsing activities.
- Specific target campaigning with time, location, user spending behaviour.
- Building sophisticated pricing strategies to get more conversion
- Customer support with chatbots has reached an incredible level.
- Keeping a smooth balance between demand and supply with omnichannel planning & strategy.
Reasons to Choose Machine Learning in 2022 as a Career
Although ML requires a steep learning curve and continuous improvement, accompanied by a plethora of skills and education, it is a lucrative offer for the younger generation today. Professionals working as ML Engineers make huge earnings.
Here are the main reasons to choose Machine Learning Engineer in 2022 and have a chance at a bright future ahead:
- Impeccable career choices and growth opportunities with several businesses, leveraging ML to enhance their scope for the future.
- Machine Learning, along with Data Science and Artificial Intelligence (AI), is taken as the future technology that will drive business growth.
- Professionals can earn their potential with a career in ML.
- Every industry is now leveraging data to help them build strategies and plan for the future. With Machine Learning, you can solve real-life challenges and
- ML is a continuous learning curve with newer opportunities coming for more unique industry verticals.
Overall, Machine Learning in 2022 is one of the most rewarding careers with unmatched potential. Businesses today are pacing towards gaining a competitive advantage for the future. ML with deep learning, Data Analytics, and Artificial advantage are pillars of the next generation. So if you want to be the leaders of tomorrow, then Machine learning is your choice to be on one.
Even the current once-in-a-lifetime pandemic COVID situation has little impact on the demand for Machine Learning career opportunities. Machine Learning Engineer in 2022 jobs multiply, with industries shifting their focus towards this incredible technology ready for the futuristic challenges. With Machine Learning an essential part of Artificial Intelligence, you can expect ML to bring new opportunities and expand research areas to scalable heights.
If you’re interested to learn more about machine learning, check out IIIT-B & upGrad’s PG Diploma in Machine Learning & AI which is designed for working professionals and offers 450+ hours of rigorous training, 30+ case studies & assignments, IIIT-B Alumni status, & 10 practical hands-on capstone projects.
To become a machine learning engineer you need a solid software engineering background as it will help you understand the concepts better. Getting hands-on experience with algorithms and software design will help you in gaining ML experience and lastly, practice will make you a good machine learning engineer. Yes, it definitely is a good career option. In terms of all the aspects like salary, growth, and challenges to keep you excited every day. Python is one of the most popular ML programming as it supports a variety of libraries and tools.
How can I become a Machine Learning Engineer in 2022?
Is Machine Learning Engineer a good career ?
What is the best programming language for machine learning?
To become a machine learning engineer you need a solid software engineering background as it will help you understand the concepts better. Getting hands-on experience with algorithms and software design will help you in gaining ML experience and lastly, practice will make you a good machine learning engineer.
Yes, it definitely is a good career option. In terms of all the aspects like salary, growth, and challenges to keep you excited every day.
Python is one of the most popular ML programming as it supports a variety of libraries and tools.