• Goutham Ravikumar

Skills required to be a AI & ML Engineer

The world has been constantly evolving at a very rapid pace. This evolution, advancements and developments are seen majorly in the technological field. Out of many such advancements, that we have come across, AI and ML is one of them. Right from the automatic cars to the automatic devices, apps, electronic devices etc., AI & ML have a wide range of impact on how easy it is making a human’s life. Given the pace at which the industry is growing, there’s no doubt that the demand for AI & ML engineers is also increasing. So now what are the essential skills required to be an AI & ML engineer? Let us understand some of the important topics before entering the main skills:

Difference between: AI Engineer vs ML Engineer We all know that AI & ML are related to each other however, the work, job roles and responsibilities may vary a bit. It is because of the various tools and technologies that are used for AI & ML and also the end results may vary. Machine Learning majorly focuses on the accuracy of the data whereas Artificial Intelligence’s main priority is to improve the chances of accuracy of the data. While AI engineers use data for decision making, ML engineers will learn new things from the available data. AI engineers uses Java, C++ and various other programming languages and development tools, ML engineers will always work to understand the algorithms and data tools like TensorFlow, H2O, etc. Essentially, these two job roles will indefinitely get the same output and the methods and the tools used are a bit different to each other. However, companies will hire professionals who are skilled in both the AI & ML methods, techniques, tools and algorithms. Now let’s get into the main topic and understand the skill required to be an AI & ML engineer. Some of the Common Skills required to be an AI and ML Engineers: Let us understand some of the common skills required on the basis of technical and non-technical skills, this might make things a bit easier. Technical Skills

  • Programming Languages: A very good understanding of the programming languages is necessary in this field. Preferable languages like Python, R, Java, C++ would be good for AI & ML. They are simple and comparatively easy than other programming languages. Their applications provide more scope than any other programming languages. Python is the most opt language for Machine Learning.

  • Linear Algebra, Calculus, Statistics: It is highly recommended to have a good understanding of the concepts like: Matrices, Vectors, and Matrix Multiplication. Moreover, when it comes to knowledge in derivatives and integrals and their applications, it is very much essential to understand even a simple concepts like gradient descent.

  • Signal Processing Techniques: Competence in understanding the Signal Processing and its ability to solve several problems using the Signal Processing techniques is crucial for feature extraction, this is considered to be the important aspect of Machine Learning. Then we have Time-frequency Analysis and advanced Signal Processing Techniques/algorithms such as the Wavelets, Shearlets, Curvelets, and Bandlets. A very good theoretical and practical knowledge of these will help you solve complex situations.

  • Neural Network Architectures: The main purpose of Machine Learning is to solve the most complex tasks that are beyond the human capability to code. Neural networks have been understood and proven to be by far the most accurate way of countering many problems like the: Translation, Speech recognitions and also the image classification, playing the pivotal role in the AI department.

  • Applied Math and Algorithms: An excellent knowledge and a solid foundation and expertise in the algorithm theory are surely a must. This skill set will enable in understanding the concepts such as the Gradient Descent, Convex Optimisation, Lagrange, Quadratic Programming, Partial Differential equation and summations, and many more such techniques.

Non-Technical Skills

  • Communication: Irrespective of the type of work you do and the type of industry you choose, this communication plays an important role. AI/ML engineering is no exception. Explaining AI and ML concepts to the fellow team mates, to some layman is only possible by communicating fluently and clearly. It’s quite obvious that an AI & ML engineer does not work alone. Projects will be too big that it cannot be completed by a single person. Projects will involve working alongside a team of engineers and non-technical teams like the Marketing or Sales departments and hence in all such scenarios, communication plays an important role.

  • Domain Knowledge: Machine Learning and Artificial Intelligence projects focus on the major troubling issues and help them finish without any flaws. Irrespective of the industry and AI & ML engineer works for gaining knowledge on how the domain works and what the benefits is to the business. Proper domain knowledge also facilitates in the interpreting potential challenges and enabling the continual running of the business.

  • Rapid Prototyping: It is quite critical to keep working on the perfect idea with the minimum time consumed. Especially in this Artificial Intelligence & Machine Learning field. Right from choosing the right model along with working on the projects with A/B testing holds the key to a project’s success. Rapid Prototyping helps in the formation of different techniques to fasten the development of a scale model.

Other Skills Required:

  • Language, Audio and Video Processing: With Natural Language Processing, AI & ML engineers get a chance to work with two of the foremost areas of work: Linguistics and Computer Science like the text, audio or video. An AI & ML engineer should be well versed with the available libraries such as the Gensim, NLTK, and other techniques like word2vec, Sentimental Analysis and the Summarization.

  • Physics: There will be real-world scenarios that would requires the application of Machine Learning techniques to systems and that is when the knowledge of Physics comes into the act.

  • Reinforcement Learning: The year 2017 had to witness a Reinforcement Learning as the primary reason behind the improvement of deep learning and artificial intelligence to a great extent. This will also act as a helping hand to pave the way into the field of robotics, self-driving cars, or any other lines if work in the AI & ML industry.

  • Computer Vision: Machine Learning and the Computer Vision are the two major computer science branches that can separately work and control the most complex systems, systems that rely exclusively on the CV and ML algorithms but this can also bring more output when the two work in a tandem.

Where will AI and ML head to in the future? With the amount of impact AI and ML has created over the past few years and the way it has changed things, we can easily tell that over the next few years, AI & ML will become more powerful than ever. We can even expect new applications and challenges and we should be ready for the upcoming and never before thought of changes. Without a doubt, we can easily say: - The Future is Now!

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