How To Start A Career In Artificial Intelligence In 2021
Artificial intelligence (AI) and machine learning is already shaping our future, and the demand for talented engineers is skyrocketing. According to the Market Research Future report, the machine learning market is projected to be worth almost $31 billion by 2024.
Applications in AI
Artificial intelligence has impacted every industry. I believes most AI innovation is happening in research and development labs of big tech companies like Amazon, Apple, Facebook, Google, and Microsoft.
AI has applications in areas including:
- Autonomous vehicles
- Social media
- Travel and tourism
- Create a better world
Later, riffed on a few subsets of artificial intelligence, including natural language processing (NLP), computer vision etc.
The life cycle of an AI project for a typical organisation:
- Data engineering: Prepares data and transforms data into formats that other team members can use.
- Modeling: Looks for patterns in data that can help a company predict outcomes of various decisions, business risks and opportunities, or determine cause-and-effect relationships.
- Deployment: Takes a stream of data, combines it with a model, and test the integration before putting the model into production.
- Business analysis: Evaluates a deployed model’s performance and business value and adjusts accordingly to maximise benefit or abandon unproductive models.
- AI infrastructure: Builds and maintains reliable, fast, secure and scalable software systems to help people working in data engineering, modeling, deployment and business analysis.
AI project development life cycle
Based on these tasks, I highlighted various job roles in the field such as data scientists, machine learning engineer, data analysts, software engineer – ML, machine learning researcher and software engineer, among others.
“Though there are quite a lot of overlaps with the kind of tasks that each of these individual roles manages, the extent of the skills required on each task complement each other.”
Citing the difference between machine learning engineers and data scientists, trust me! ML engineers need more hands-on data engineering, modeling, deployment, and AI infrastructure. In contrast, the data scientist role focuses on tasks such as data engineering, modeling and business analysis.
Tasks and skill requirement for machine learning engineer job
Skill-wise, machine learning engineers need to have a software engineering background, algorithmic coding and machine learning skills. On the other hand, data scientists need to have experience in machine learning, mathematics, data science and business acumen.
Task and skill required for a data scientist job
“Salaries in AI and ML job profiles are higher compared to other job roles, including full-stack, back-end and front end engineer or native engineers in Android or iOS. However, the pay scale also depends on various factors like education background, interview process, skills and experience among others”.
Source: Indeed (Salaries in artificial intelligence)
The average annual salaries of machine learning engineers and data scientists are much higher than DevOps engineers, software engineers and web developers. Further, he said machine learning and data scientists are currently among the top 20 emerging jobs globally.
In India, Bengaluru, Delhi NCR and Mumbai are the top three locations for AI/ML jobs, followed by Hyderabad, Pune and Chennai.
"Always be curious as this will help you in the long run", AI will remain at the core of technology innovation and business growth going forward.
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