Artificial Intelligence and Machine Learning Technicians – Bridging the Skills Gap

aiArtificial Intelligence and Machine Learning Technicians – Bridging the Skills Gap

We are entering a new era of advanced technologies like artificial intelligence and machine learning that have the potential to revolutionize entire industries.

Just look at the rapid developments happening in self-driving vehicle technology thanks to computer vision systems powered by neural networks. Or how chatbots now use natural language processing to understand questions and provide consumer service.

But there’s still a major gap between all the cutting-edge artificial intelligence research being done and actual real-world implementation. Big tech companies and academics are publishing all these machine learning breakthroughs and proofs-of-concept, but most businesses are struggling to take those prototypes and put them to work.

And a huge part of this struggle has to do with skills. Most companies just don’t have enough people on staff with practical experience in areas like data analytics and engineering, algorithms, generative AI, and other key facets that allow you to take raw artificial intelligence and machine learning approaches and turn them into functioning, production-ready systems.

It’s that critical bridge between cutting-edge ideas and usable products where things are still lagging. But for those willing to develop expertise in transforming raw artificial intelligence into functioning technologies, from data pipelines to algorithms optimization to system deployment, there are tremendous career opportunities.

They lack personnel with the practical abilities to develop, debug, monitor, and maintain AI systems.

Enter the artificial intelligence and machine learning technician – a new type of role focused on bridging the skills gap through hands-on training in areas like data pipelines, model deployment, and more. AI/ML technicians have great career prospects as companies urgently seek to productize AI research into usable products and services. This article explores the role of AI/ML technicians, the training options available, and how to start a career in this emerging field.

What is an AI tool?

An AI tool is software that uses smart algorithms to do certain jobs or fix issues. AI tools can work in healthcare, business, schools, and more to automatic things, study information, and help people make better choices.

Artificial Intelligence (AI) applications have become an integral part of various industries, revolutionizing the way we live, work, and interact with technology.

The capabilities of AI are diverse, ranging from automating repetitive tasks to solving complex problems and enhancing decision-making processes. Here are some notable AI applications across different domains:


Diagnosis and Treatment: AI is employed in medical imaging for accurate diagnosis through techniques like computer vision. It helps in identifying patterns and anomalies in X-rays, MRIs, and CT scans.

Drug Discovery: AI algorithms analyze biological data to identify potential drug candidates and streamline the drug discovery process.


Fraud Detection: AI algorithms can detect unusual patterns in financial transactions, helping to identify and prevent fraudulent activities.

Algorithmic Trading: AI is used in predicting market trends, optimizing trading strategies, and executing high-frequency trades more efficiently.


Recommendation Systems: AI-powered recommendation engines analyze customer preferences and behavior to suggest personalized products, enhancing the overall shopping experience.

Inventory Management: AI helps optimize inventory levels by predicting demand patterns, reducing excess stock, and minimizing stockouts.


Personalized Learning: AI provides customized learning experiences by adapting content and pacing based on individual student performance and preferences.

Automated Grading: AI can assist in grading routine assignments and assessments, freeing up time for educators to focus on more complex aspects of teaching.

Autonomous Vehicles:

Self-driving Cars: AI technologies such as computer vision and machine learning enable vehicles to navigate and make decisions autonomously, improving road safety and efficiency.

Chatbots: AI-powered chatbots handle customer queries, providing instant responses and assistance, improving customer service efficiency.

Sentiment Analysis: AI is used to analyze customer sentiments and feedback, helping businesses understand and respond to customer needs.

Threat Intelligence: AI is utilized to analyze vast amounts of data to identify and respond to emerging cyber threats more effectively.

Recruitment: AI streamlines the recruitment process by automating resume screening, identifying suitable candidates, and even conducting initial interviews.

As AI continues to advance, its applications are likely to expand further, shaping the future across various industries and domains. It is crucial to ensure ethical considerations and responsible deployment to harness the full potential of AI technology.

The Growing Need for AI Talent

The demand for AI skills has skyrocketed in recent years. A report from LinkedIn found that job postings for AI talent grew 74% annually from 2015 to 2018 in the U.S. alone. 

However, traditional computer science programs are struggling to meet this need. There is a disconnect between cutting-edge artificial intelligence research happening at universities and companies and what operational artificial intelligence implementation requires in areas like data wrangling, machine learning ops (MLOps), model monitoring, and governance.

As Andrew Ng, co-founder of Coursera and Google Brain pioneer, puts it: “There is a huge divide between organizations struggling to deploy machine learning and academics publishing machine learning research papers.”

AI/ML technician roles aim to directly address this divide with intense hands-on training in taking AI/ML models to production.

What Do AI/ML Technicians Do?

AI/ML technicians turn conceptual AI systems into functioning technologies. Key responsibilities include:

  • Working with massive datasets and building data pipelines to feed ML models
  • Training, evaluating, and optimizing ML models like neural networks
  • Translating business problems into opportunities where artificial intelligence can provide solutions
  • Deploying models to applications, edge devices, or the cloud with MLOps procedures
  • Monitoring machine learning models in production and flagging drift or degraded performance
  • Using tools like TensorFlow, Keras, PyTorch, scikit-learn, SQL, cloud platforms, etc.

Communicating technical concepts to senior stakeholders

Essentially, they build the scaffolding required to make artificial intelligence applications work in practice. AI researchers or data scientists may develop theoretical ML models or algorithms, but technicians handle the dirty work of launching and running them.

The role combines software engineering, data engineering, system administration, and statistical modeling into a multidisciplinary position.

As such, AI/ML technicians offer tremendous value with their broad skill sets. An experienced technician can evaluate use cases for AI within a company, assemble datasets, try different learning algorithms, production the best model, monitor its predictive quality over time, and explain technical details to executives for business decision-making.

How To Become an AI/ML Technician

Given the industry demand, becoming an AI/ML technician offers bright job prospects and high starting salaries even for early career candidates. Here are the typical requirements to break into the role:

Education: Most positions require at least a bachelor’s degree in computer science, information technology, or a related quantitative field like engineering, statistics, or mathematics. Some companies may accept demonstrable skills and experience instead of a degree.

Technical skills: Necessary capabilities include programming in languages like Python and SQL, working with data pipelines and machine learning libraries, model evaluation techniques, and tools like Git, Docker, and Kubernetes. Familiarity with cloud platforms like AWS, GCP or Azure is also valued.

Math: Mathematical maturity, including concepts from probability, linear algebra, calculus, and statistics are needed to manipulate, analyze, and interpret data as well as understand ML model internals.

Communication: Because technicians serve as liaisons between engineering and business leadership teams, developing communication skills to explain technical details to non-technical stakeholders is vital.

Portfolio of AI projects: Demonstrating hands-on applied experience implementing and deploying machine learning systems via projects or internships gives a significant edge for entering the field.

Options for Gaining AI/ML Skills

For those looking to transition into the AI/ML technician career track, here are full-time, part-time, and self-paced options to obtain the requisite skills:

University AI Master’s Programs – For learners committed to an intensive full-time AI education, 12-16 month on-campus or online Master’s programs in Artificial Intelligence, Applied Data Science or Machine Learning Engineering equip students with cutting-edge technical skills through hands-on coursework, projects, and internship opportunities.

AI Bootcamps – Full-time immersive boot camps like Metis and Fullstack Academy offer 12-week developer training programs focused on data science or machine learning. Students learn directly applicable capabilities like Python programming, statistical modeling, deep learning techniques, MLOps, and cloud platform fluency through real-world industry projects.

Professional Certificates – Part-time professional certificates like Google’s Machine Learning EngineerNanodegree or IBM’s Data Science Professional Certificate allow working professionals to skill up in AI disciplines through self-paced courses. Programs take 3-6 months to complete and center project-based curriculums around Python, data analysis, ML applications, and open-sourceopen-source tools.

MOOCs– Massive open online courses like Andrew Ng’s Machine Learning on Coursera, MITx’s Statistics and Data Science MicroMasters, and Udacity’s Intro to Machine Learning with TensorFlow quickly equip students with foundational data science and applied skills at low cost. Students earn credentials by completing graded assignments and building projects.

Few of the free AI course online 


Course: “Machine Learning” by Andrew Ng

Link: Machine Learning on Coursera


Course: “Introduction to Artificial Intelligence (AI)” by Microsoft

Link: Introduction to Artificial Intelligence (AI) on edX


Course: “Intro to Artificial Intelligence” by Sebastian Thrun and Peter Norvig

Artificial Intelligence Free Course with Certificate:

Several reputable platforms offer free courses in Artificial Intelligence with certificates.

Some notable examples include:

Coursera: “AI for Everyone” by Andrew Ng: A beginner-friendly course covering the basics of AI and its societal impact.

edX: “Introduction to Artificial Intelligence” by Microsoft: A foundational course introducing AI concepts and applications.

MIT OpenCourseWare: “Introduction to Deep Learning” by MIT: An in-depth exploration of deep learning techniques.

The Path Forward as an AI Technician

Whichever educational route aspiring technicians pursue, gaining hands-on development experience is critical for breaking into AI roles. Luckily, abundant open-sourced data sets like MNIST, ImageNet, Model Zoo, and more allow anyone to practice building neural networks for image recognition, NLP models, forecasting systems, and other applications from their own computers.

By proactively honing technical skills and compiling a portfolio of functioning AI demos, aspiring technicians substantially improve their odds of landing that first crucial role. Once they secure that initial job, talented technicians have their choice of AI career paths to navigate – whether specializing as MLOps engineers, data science consultants, computer vision developers at innovative startups, or leading in-house AI labs at enterprises.

The future is very bright for these AI builder roles. As more companies connect their fortunes and competitiveness to successfully leveraging technologies like machine learning, demand for the practical abilities of AI/ML technicians will only accelerate. There has never been a better time to join this exploding profession.


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