Machine Learning & AI
Table of Contents
Last updated August 23, 2024
What is AI and Machine Learning?
The umbrella term, Artificial Intelligence (AI), has existed since the 1950’s, but has accelerated rapidly in the last 10 years. Machine Learning (ML) is a subcomponent of AI that uses specific statistical algorithms to process massive amounts of data in order to produce insights, predictions, and unique outputs.
As Evan Shy, the CEO of Coding Temple, describes: “The World Economic Forum predicts that tech advancements, from automation, artificial intelligence, to robotics, will displace 85 million jobs by 2025. However, this same technology will also create 97 million new jobs in areas like data analysis, software development, and cybersecurity. Ultimately it’ll depend on how you prepare for these inevitable changes.”
How to Learn AI and Machine Learning
Machine learning and AI (Artificial Intelligence) bootcamps last 12 to 24 weeks and cost anywhere from free to $30,000. Realistically, a complete beginner in tech is not going to become an AI Engineer in 12 weeks. If you don't have a technical background, then start with a Software Engineering or Data Science bootcamp. Once you graduate, find an entry-level job working with data, and continue to learn! You can also add to your skill set with an entry-level AI Intro course or an advanced course on AI.
What Does a Machine Learning or AI Engineer Do?
Expect a job description for a Machine Learning Engineer or AI Engineer to ask for knowledge of Python and Spark. You may also see generative AI tools like ChatGPT or OpenAI. Codesmith’s Director of Machine Learning, Weylin Wagnon, says, “You need to be able to work with large amounts of data, be a smart programmer, understand neural networks, and have machine learning skills…in general, machine learning is equal parts math, statistics, computer science, and voodoo.”
Varun Kumar, an AI Engineer who graduated from Flatiron School, says his job is “Part data wrangling, part coding, and part researching new techniques and software that has been developed in dealing with large language models and processing natural language.” Varun breaks it down even further into six categories of on-the-job requirements:
- Research: Stay updated with the latest advancements in the field. This could involve reading research papers, attending seminars or webinars, and participating in online forums and communities. This is crucial as the field of AI and machine learning is evolving rapidly.
- Data Preparation: Work on preparing and pre-processing the data for training language models. This involves collecting data, cleaning it, and converting it into a format that can be used for machine learning.
- Model Development and Training: Design and implement machine learning models. This includes choosing the right algorithms, tuning parameters, and training the model on the prepared data. This process often requires running experiments and making iterative improvements based on the results. Many times, I am building on pre-trained models with either fine tuning, or instruction via prompts.
- Model Evaluation: Evaluate the performance of the models using appropriate metrics. This often involves testing the model on a held-out validation set and analyzing the results.
- Collaboration: Work closely with other teams, such as product development, to integrate the AI models into products or services. This could involve optimizing the model for deployment, working on the user interface, or addressing user feedback.
- Documentation and Presentation: Document the work for future reference and present findings to stakeholders or to the technical team. This might involve writing technical reports, creating presentations, or showing working code.
Types of Machine Learning & AI Jobs
Traditional tech roles like Software Engineers and Data Scientists can incorporate AI and Machine Learning skills into their current jobs. However, companies are now hiring for AI-specific roles like Prompt Engineer and AI Integration Specialist. Expect a lot of variability between job listings until these roles become more defined.
Some common ML/AI job titles include:
- Data Scientist
- Data Engineer
- Prompt Engineer
- AI Engineer
- Software Engineer
- Product Manager
- AI Ethics Officer
- AI Data Curator
- AI Trainer
- AI Integration Specialist
What Kind of Skills Do Machine Learning and AI Engineers Need?
To get started in AI, Machine Learning and AI Engineers need a variety of skills and continuous learning is a must. According to Carianne Burnley, a Career Coach at Springboard, “The most widely used programming language in AI is Python, and the libraries and frameworks associated with it. Knowing other languages like Java and C++ can be helpful as well.”
Hard Skills Required for Machine Learning and AI
The most important AI technical skills and languages are:
- Python
- Databases
- Big data tools like Spark
- Cloud platforms like AWS or Azure
- Data visualization tools like Tableau, PowerBI, or R
- Mathematics like linear algebra, data interpretation, and deep learning.
Even if you learn all of these topics at an AI Bootcamp, expect to continue learning “on the job” where you'll be working with data at scale. Imesh Ekanayake, a mentor at Metana bootcamp, stresses, "I find that where people often lack skills is when attempting to handle tasks at scale, especially in the cloud. Dealing with multi-terabyte or terabyte-scale datasets adds a whole new level of complexity to the equation."
Soft Skills Needed for AI and Machine Learning
Employers are also looking for AI professionals with strong soft skills to help them integrate into the workplace and achieve success. Some soft skills that are important for AI and Machine Learning Engineers are:
- Critical thinking
- Problem-solving
- Communication
- Time management
- A desire for continuous learning
- Flexibility and adaptability
Job Market and Salary Insights
Overall, the job market for artificial intelligence positions is expected to grow at a rate that is faster than average over the next ten years, with Machine Learning and AI positions seeing a 53 percent growth rate during that time, making it #8 on Indeed’s Best Jobs of 2023 list.
The average Machine Learning Engineer salary is $161,407 per year, but salary is largely dependent on experience. The average base salary for an entry-level Machine Learning Engineer is $97,205 per year, $162,774 for a mid-level position, and $185,416 for Engineers with more than ten years of experience. Location matters, too, with the average salary around $205,000 for a Machine Learning Engineer in cities such as New York, with similar wages for other large metropolitan areas like San Francisco, Austin, and San Diego.
Newly-created AI roles like Prompt Engineer can earn up to $335,000 per year.
FAQ Section
How do you start a career in AI and machine learning?
If you have a degree in computer science or a strong technical background, consider an AI/machine learning bootcamp or an advanced AI course. Machine learning & AI bootcamps last between 12-24 weeks and cost anywhere from free to $30,000.
If you don't have a technical background or degree, start with a Software Engineering or Data Science bootcamp. Once you graduate, find an entry-level job working with data, and continue to learn new skills to get into AI.
What are popular AI career fields?
The great thing about a career in AI and Machine Learning is that there is a wide variety of areas in which you can specialize, largely due to the swift growth of the AI industry. Popular fields for AI careers include healthcare, government, tech, finance, manufacturing, and e-commerce. In addition to Machine Learning Engineer, AI career positions you can consider are:
- AI Consultant
- AI Programmer
- AI Research Scientist
- Software Engineer
- Data Scientist
- Natural Language Processing Engineer
- Business Intelligence Developer
- Deep Learning Engineer
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