Artificial intelligence simply refers to software used by computers to mimic aspects of human intelligence like- a program that recommends what you should read based on books you’ve bought or a robot vacuum that has a basic grasp of the world around it. AI is made from vast amounts of natural resources, fuel and human labour and it’s not intelligent in any kind of human intelligence way. As it is not able to discern things without extensive human training and it has a completely different statistical logic for how meaning is derived.

Artificial intelligence engineers play an incredibly important role in the modern economy, especially in sectors and verticals where artificial intelligence technology has already had a significant impact. AI engineers in niche roles may be responsible for vastly different things, there’s a common set of basic responsibilities that anyone looking to get into the industry would want to develop a proficiency in, including;

  • Establishing and achieving objectives using techniques associated with AI reasoning and uncertainty.
  • Applying logic, probability analysis and machine-learning concepts to problem-solving initiatives.
  • Using AI best practices in regards to applications in speech recognition, data processing, data mining and robotic control.
  • Analysing systems to effectively monitor and control development projects.

Talking about ML engineer role, working in this branch of artificial intelligence expects you to be responsible for creating programmes & algorithms that enable machines to take actions without being directed. An example of a system you may produce is a self-driving car or a customized newsfeed.

AI and ML both refer to a process or technology that helps make machines intelligent, allowing them to be more useful to the humans that rely on them to solve complex problems and perform complicated tasks. You may hear these terms AI & ML used interchangeably, but they aren’t necessarily the same thing as:

  • AI is a broader term that describes applications where a machine mimics human cognitive functions like learning & problem-solving. An AI system can be incredibly complex or as simple as a series of nested if-else statements.
  • ML utilizes classical algorithms to complete tasks, like clustering, regression or classification & ML algorithms must be trained on data. The more trained they are, the better they perform.

If you’ve been paying attention to the economy over the past years, then you’re probably well-aware of the recent applications for AI & ML technologies as you can see them in the play. The following table would make it easier for you to understand these roles.

ROLE & RESPONSIBILITY Coordinating between data scientists & business analysts, automate infrastructure used by data science team, test & deploy models, develop minimum viable products based on ML, automate processes by utilizing ML, impart novel capabilities using AI, come up with improvements in ML algorithms, utilize modern software development methodologies & deploy software in production, providing advanced coding in various languages, coordinating with multiple teams internally and externally, conducting statistical analysis & interpreting results essential in the decision-making process for the organization, automating important infrastructure for data science team, building AI models and transforming ML models into APIs that other applications can interact with Designing ML systems, researching and implementing ML algorithms and tools, selecting appropriate data sets, picking appropriate data representation methods, identifying differences in data distribution that affects model performance, verifying data quality, transforming & converting data science prototypes, performing statistical analysis, running ML tests, using results to improve models, training & retraining systems when needed, extending ML libraries and developing ML apps according to client requirements
SKILLS REQUIRED Technical skills- Programming languages, linear algebra, probability & statistics, spark & big data technologies, algorithms & frameworks

Non-technical skills- Communication & problem-solving skills, be a team player

Applied mathematics, computer science fundamentals & programming, ML algorithms, data modelling & evaluation, neural networks, NLP, communication skills, data storytelling
QUALIFICATION PREREQUISITES Bachelor’s &/or Master’s degree in IT, computer science, statistics, data science, finance; Certification in data science, ML, etc Master’s degree or a PhD in Computer science, software engineering or a related field like- ML, computer vision, neural networks, deep learning or others

AI is a bigger concept to create intelligent machines that can stimulate human thinking capability and behaviour, whereas ML is an application or a subset of AI that allows machines to learn from data without being programmed explicitly.

Key differences between AI & ML are as follows:

It enables machine to stimulate human behaviour It allows machine to automatically learn from past data without programming explicitly
Its goal is to make a smart computer system like humans to solve complex problems Its goal is to allow machines to learn from data so that they can give accurate output
ML & Deep learning are a subset of AI Deep learning is a main subset of ML
AI has wider scope ML has limited scope
AI system is concerned about maximizing the chances of success ML is concerned about accuracy and patterns
Main applications of AI are Siri, Customer support using Chatbots, expert system, online game playing, intelligent humanoid, robot, etc Main applications of ML include Online recommender system, Google search algorithms, Facebook auto friend tagging suggestions, etc
On the basis of capabilities, AI can be divided into – Weak, General & Strong AI ML is divided into Supervised, Unsupervised & Reinforcement learning
AI deals with structured, semi-structured & unstructured data ML deals with structured & semi-structured data only

AI engineer skills are much in demand since the marketplace has become so volatile, thereby increasing the applicability and necessity of such AI roles for higher efficiency. If you plan to take the plunge and dive into the vast arena of AI engineer roles, apart from the above differences among the roles and the prerequisites for the same, one needs to equip themselves with credible AI certifications from Google or USAII. Investing in the right AI certification will lead you up the ladder and provide a levelled playing field internationally. Talking about the leading USAII’s CAIE (Certified Artificial Intelligence Engineer) certification, it is a credential that sets you at a higher footing from others in your AI career. Its star points involve it being self-paced program, vetted by 15+ AI SMEs in the world, it’s the most powerful & recognized credential, comes packed with free personalized study books, eLearning materials & workshops. Partner with this game changer AI certification from USAII to give multitudinous growth to your AI engineer career. Invest in the right AI certification to scale higher.

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