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Overview

Overview

AI Engineering Overview

Artificial intelligence (AI) engineering is a discipline that involves designing, building, and maintaining AI systems and applications. It encompasses a wide range of activities, from developing machine learning algorithms and building intelligent systems to implementing AI solutions for businesses and organizations.

AI engineering requires a combination of technical skills and domain knowledge, as well as the ability to think critically and solve problems. It also involves a strong understanding of computer science, data science, and software engineering principles.

To build a discipline of AI engineering, it is important to have a clear understanding of the business or organization’s goals and how AI can be used to achieve them. This may involve conducting research and analysis to identify potential use cases and developing a roadmap for implementing AI solutions.

AI engineers also need to be able to work effectively with a team of data scientists, software engineers, and other specialists to design and build AI systems. They must be able to communicate technical concepts to a non-technical audience, as well as collaborate with stakeholders to ensure that the AI systems are aligned with the organization’s goals.

Overall, building a discipline of AI engineering requires a combination of technical expertise, domain knowledge, and strong communication and collaboration skills. It is a challenging but rewarding field that has the potential to transform industries and organizations around the world.

What is AI?

A definition of AI.

Machine Learning Lifecycle

The machine learning (ML) lifecycle refers to the steps involved in building and deploying machine learning models. It includes a range of activities, from defining the problem and collecting data, to training and evaluating the model, to deploying and maintaining the model in production. Here is a high-level overview of the ML lifecycle: Define the problem: The first step in the ML lifecycle is to define the problem that the model will be solving.

ML Operations

MLOps (Machine Learning Operations) is a practice that involves integrating machine learning (ML) into the software development lifecycle in order to improve the efficiency and effectiveness of ML projects. It combines the principles of software engineering and data science in order to streamline the process of building, deploying, and maintaining ML models. MLOps involves a range of activities, including: Collaboration: MLOps promotes collaboration between data scientists and software engineers in order to build and deploy ML models more efficiently.

Challenges

Challenges of AI Engineering.

    Contents courtesy of ChatGPT.