AI Engineering

Business / By /
  • AI engineering is the process of designing, building, testing, and deploying artificial intelligence systems. To create systems that can learn, reason, and make decisions, the discipline combines software engineering with data science.
  • AI engineering requires that the appropriate hardware and software be selected to ensure the scalability, reliability, security, and human-centeredness of the system. Once deployed, it must be monitored and maintained to ensure consistent performance.
  • AI engineers must possess diverse technical skills that equip them with a knowledge of the software development life cycle, programming, mathematics, and algorithms. They must also be aware of how ML and AI can provide value to the organization and be skilled and persuasive communicators.

AI engineering is the process of designing, building, testing, and deploying artificial intelligence systems. To create systems that can learn, reason, and make decisions, the discipline combines software engineering with data science.

Understanding AI engineering

AI engineering is a diverse field that encompasses tasks such as data collection, cleaning and pre-processing, algorithm selection and optimization, training of AI models, evaluation, and deployment. 

AI engineering also requires that the appropriate hardware and software be selected to ensure the AI systems are scalable, reliable, secure, and human-centered. Once deployed, the system must be monitored and maintained to ensure consistent performance.

In more specific terms, AI engineering is simply the use (or application) of algorithms, neural networks, big data, and computer programming for the development of AI techniques and products. These techniques and products are predominantly found in industries such as education, marketing, eCommerce, finance, and healthcare.

There has been significant growth in the industry with AI and machine learning (ML) positions increasing by 75% over the past four years. This trend is expected to continue as the industry evolves and matures.

AI engineering required skills

AI engineering requires a mixture of technical, business, and soft skills.

Technical skills

AI engineers must be skilled programmers and possess a robust understanding of the software development lifecycle and related best practices. They should be proficient in languages used to construct and implement AI models such as Python, C++, Java, and R. 

AI engineers must also be capable mathematicians and use probability, algebra, and statistics to better understand models. Likewise for machine learning algorithms such as KNN and Naïve Bayes which are themselves complex mathematical formulas used in model implementation. 

Lastly, AI engineering requires the individual to be proficient in natural language processing (NLP) which combines computer science with information engineering and linguistics. Ideally, they should also have a knack for rapid prototyping. 

Business skills

AI engineers must be aware of how ML and AI can adapt to support business processes and ultimately, provide value to the organization. They decide when a model is ready for deployment and monitor for accuracy to determine if or when it needs to be replaced or retrained.

To effectively add ML capabilities to existing resources such as enterprise resource planning (ERP) and customer relationship management (CRM), AI engineers need to move beyond the purely technical aspects. To do this, they need to understand the company’s core business model, the customer it serves, and the current state of the market.

Soft skills

Soft skills like the ability to communicate and collaborate with others are now standard practice in most industries. AI engineering is no different.

In addition, AI engineers combine creative thinking with a more analytical approach to solve enterprise problems. Using their communication skills, they present their ideas to relevant stakeholders with terms anyone can understand. 

AI engineers must also be persuasive speakers who can convince stakeholders that AI-based solutions can be implemented with minimum effort and maximum effect.

Key Takeaways:

  • AI Engineering Overview: AI engineering involves the process of designing, developing, testing, and deploying artificial intelligence systems. It combines elements of software engineering and data science to create systems that can learn, reason, and make decisions.
  • Components of AI Engineering:
    • Data Collection and Pre-processing: Gathering and preparing data for training AI models.
    • Algorithm Selection and Optimization: Choosing and fine-tuning algorithms for optimal performance.
    • Model Training: Training AI models using data and algorithms.
    • Evaluation: Assessing model performance and accuracy.
    • Deployment: Putting AI systems into operation in real-world applications.
    • Monitoring and Maintenance: Continuously monitoring and maintaining AI systems for consistent performance.
  • Required Skills:
    • Technical Skills: Proficiency in programming languages like Python, C++, Java, and R. Strong mathematical foundation, including probability, algebra, and statistics. Familiarity with machine learning algorithms and natural language processing (NLP). Rapid prototyping abilities.
    • Business Skills: Understanding how AI can add value to business processes and aligning AI solutions with business goals. Knowledge of the company’s core business model and market conditions.
    • Soft Skills: Effective communication and collaboration skills. Creative problem-solving combined with analytical thinking. Ability to present ideas to stakeholders and persuade them of the benefits of AI solutions.

Read Next: Business Engineer, Business Designer.

Connected Business Frameworks And Analyses

AI Paradigm

current-AI-paradigm

Pre-Training

pre-training

Large Language Models

large-language-models-llms
Large language models (LLMs) are AI tools that can read, summarize, and translate text. This enables them to predict words and craft sentences that reflect how humans write and speak.

Generative Models

generative-models

Prompt Engineering

prompt-engineering
Prompt engineering is a natural language processing (NLP) concept that involves discovering inputs that yield desirable or useful results. Like most processes, the quality of the inputs determines the quality of the outputs in prompt engineering. Designing effective prompts increases the likelihood that the model will return a response that is both favorable and contextual. Developed by OpenAI, the CLIP (Contrastive Language-Image Pre-training) model is an example of a model that utilizes prompts to classify images and captions from over 400 million image-caption pairs.

AIOps

aiops
AIOps is the application of artificial intelligence to IT operations. It has become particularly useful for modern IT management in hybridized, distributed, and dynamic environments. AIOps has become a key operational component of modern digital-based organizations, built around software and algorithms.

Machine Learning

mlops
Machine Learning Ops (MLOps) describes a suite of best practices that successfully help a business run artificial intelligence. It consists of the skills, workflows, and processes to create, run, and maintain machine learning models to help various operational processes within organizations.

Continuous Intelligence

continuous-intelligence-business-model
The business intelligence models have transitioned to continuous intelligence, where dynamic technology infrastructure is coupled with continuous deployment and delivery to provide continuous intelligence. In short, the software offered in the cloud will integrate with the company’s data, leveraging on AI/ML to provide answers in real-time to current issues the organization might be experiencing.

Continuous Innovation

continuous-innovation
That is a process that requires a continuous feedback loop to develop a valuable product and build a viable business model. Continuous innovation is a mindset where products and services are designed and delivered to tune them around the customers’ problems and not the technical solution of its founders.

Technological Modeling

technological-modeling
Technological modeling is a discipline to provide the basis for companies to sustain innovation, thus developing incremental products. While also looking at breakthrough innovative products that can pave the way for long-term success. In a sort of Barbell Strategy, technological modeling suggests having a two-sided approach, on the one hand, to keep sustaining continuous innovation as a core part of the business model. On the other hand, it places bets on future developments that have the potential to break through and take a leap forward.

Business Engineering

business-engineering-manifesto

Tech Business Model Template

business-model-template
A tech business model is made of four main components: value model (value propositions, mission, vision), technological model (R&D management), distribution model (sales and marketing organizational structure), and financial model (revenue modeling, cost structure, profitability and cash generation/management). Those elements coming together can serve as the basis to build a solid tech business model.

OpenAI Business Model

how-does-openai-make-money
OpenAI has built the foundational layer of the AI industry. With large generative models like GPT-3 and DALL-E, OpenAI offers API access to businesses that want to develop applications on top of its foundational models while being able to plug these models into their products and customize these models with proprietary data and additional AI features. On the other hand, OpenAI also released ChatGPT, developing around a freemium model. Microsoft also commercializes opener products through its commercial partnership.

OpenAI/Microsoft

openai-microsoft
OpenAI and Microsoft partnered up from a commercial standpoint. The history of the partnership started in 2016 and consolidated in 2019, with Microsoft investing a billion dollars into the partnership. It’s now taking a leap forward, with Microsoft in talks to put $10 billion into this partnership. Microsoft, through OpenAI, is developing its Azure AI Supercomputer while enhancing its Azure Enterprise Platform and integrating OpenAI’s models into its business and consumer products (GitHub, Office, Bing).

Stability AI Business Model

how-does-stability-ai-make-money
Stability AI is the entity behind Stable Diffusion. Stability makes money from our AI products and from providing AI consulting services to businesses. Stability AI monetizes Stable Diffusion via DreamStudio’s APIs. While it also releases it open-source for anyone to download and use. Stability AI also makes money via enterprise services, where its core development team offers the chance to enterprise customers to service, scale, and customize Stable Diffusion or other large generative models to their needs.

Stability AI Ecosystem

stability-ai-ecosystem

Main Guides:

More Resources

Discover more from FourWeekMBA

Subscribe now to keep reading and get access to the full archive.

Continue reading

Scroll to Top
FourWeekMBA