Building and deploying AI - How AI is put into motion

Understanding AI from a technical perspective will help you learn how to unlock new opportunities, drive innovation, and create lasting value. But with so many terms to understand, what is the best way to approach it? As part of our Demystifying AI series, we have created three short articles that cover the 37 top terms you need to know, with each article focusing on a key AI domain area: learning paradigms, how to build and deploy AI, and gen AI specifics.

This article explores terms related to building and deploying AI, using our Foundational Understanding terminology. Read our post on understanding AI learning paradigms. By the conclusion of this series, you'll also have a comprehensive understanding of how AI is set into motion.

There are several key concepts that all work together to create a robust and reliable AI system.

• Data processing and engineering prepare the data, AI frameworks provide the tools, hyperparameter tuning optimizes the model, and evaluate models to prevent overfitting ensure generalization.

• Inference puts the model into action, while model deployment and serving make it usable.

• Robustness and reliability build trust, and transparency and accountability foster responsible use.

• MLOps streamlines the lifecycle, and evaluation and monitoring ensure ongoing performance.

Together, these components enable the development and deployment of effective AI models, using key processes and workflows to create end-to-end AI applications.

Please read on to explore these terms more fully.

Data processing & engineering:

First steps for converting raw data into insights

In AI, data is the fuel that powers the engines of learning and decision-making. But raw data is often messy, incomplete, and inconsistent. Data processing is the crucial step that transforms this raw material into a clean, valuable fuel for AI models.

Data processing involves a series of steps, including cleaning, transforming, and organizing data to ensure its accuracy, completeness, and consistency. This might include handling missing values, converting data types, standardizing formats, and removing duplicates or errors. By effectively processing data, we empower AI models to extract more meaningful insights, learn from patterns, and make accurate predictions – ultimately driving innovation and informed decision-making. This careful preparation of data is also essential for ensuring that the model's results are accurate and trustworthy.

Data engineers are the architects of the AI data pipeline, designing and implementing systems to extract, transform, and load (ETL) data from various sources. They ensure data quality, handle missing values, and address inconsistencies – thereby creating a clean and reliable dataset for AI models to learn from.

AI frameworks: The construction kits for AI innovation

AI frameworks provide a structured environment and a set of pre-built tools that simplify the complex process of creating and deploying AI models. AI frameworks feature a collection of libraries, algorithms, and utilities that streamline various stages of AI development. They provide standardized ways to handle data preprocessing, model building, training, and even deployment. Popular AI frameworks like TensorFlow, PyTorch, and Keras act as powerful accelerators, helping developers to focus on the core logic of their AI applications, leaving the heavy lifting of implementation and optimization to these tools.

Hyperparameter tuning: Finding the perfect recipe

Hyperparameters are settings that influence how a machine learning model learns, but they're not learned from the data itself. They're like the knobs and dials on a machine that need to be adjusted to achieve optimal performance. Hyperparameter tuning is the process of experimenting with different combinations of these settings to find the best ones for a particular task. This process can be time-consuming, but it's crucial for getting the most out of a machine learning model.

Overfitting: Avoiding the memorization trap

Overfitting is a common pitfall in machine learning where a model learns the training data too well, memorizing even the noise and random fluctuations present in the data. This can lead to excellent performance on the training data but poor generalization to new, unseen data. It's akin to a student who memorizes the textbook but struggles to apply the concepts to new problems. You’ll hear about terms such as regularization and cross-validation to help prevent overfitting, ensuring that models learn the underlying patterns in the data rather than memorizing specific examples.

Inference: Putting AI into action

Once a machine learning model is trained, it's ready to be put to work! Inference is the process of using the newly trained model to make predictions or decisions on new, unseen data. It's where the model applies the patterns it has discovered to analyze new inputs and generate corresponding outputs. This is where the model's true value is realized, as it can now be used to solve real-world problems, automate tasks, or make informed decisions that drive business success.

Model deployment & serving: Making AI ready for the real world

Model deployment takes a trained AI model and integrates it into a real-world environment, like a website or a mobile app. For example, a model that translates languages could be deployed on a website to provide real-time translation services to users. Model serving ensures this deployed model can efficiently handle requests, providing translations quickly and reliably. This makes the model's capabilities usable and allows it to interact with users or other systems, bringing the power of AI to life in practical applications.

MLOps: Streamlining the machine learning lifecycle

In machine learning, building and training a model is just the first step. To truly harness its potential, it needs to be seamlessly integrated into real-world applications, continuously monitored for performance, and updated as new data becomes available. This is where MLOps, or Machine Learning Operations, comes into play. MLOps is a set of practices and tools that streamlines the entire machine learning lifecycle, from data preparation and model training to deployment and monitoring. It fosters collaboration between data scientists, engineers, and operations teams, ensuring that models are not only developed efficiently but also deployed reliably and maintained effectively.

MLOps enables organizations to automate key processes like model testing, validation, and deployment, making it easier to scale AI initiatives and deliver value faster. By adopting MLOps principles, businesses can transform machine learning from an experimental endeavor into a robust and sustainable driver of innovation and growth.

Evaluation and monitoring: Keeping AI on track

In the ever-evolving world of AI, models are not static entities; they need continuous evaluation and monitoring to ensure they remain effective and reliable. Think of it like a regular health checkup for your AI systems. Evaluation involves assessing the model's performance using various metrics, like accuracy, precision, and recall. This helps you understand how well the model is doing its job and identify areas where it might be falling short. Monitoring takes this a step further by continuously tracking the model's performance in real-world scenarios. It's like keeping a watchful eye on your AI to make sure it's not drifting off course or making unexpected errors.

By monitoring key metrics and analyzing user feedback, you can detect any degradation in performance, identify potential biases, and take corrective action to ensure your AI systems remain accurate, fair, and aligned with your business goals. This proactive approach helps build trust in AI and maximizes its value for your organization.

Robustness & reliability: Building AI you can trust

It's not enough for a model to simply work in ideal conditions; it needs to be robust and reliable. Robustness means the AI can handle surprises – unexpected inputs, noisy data, or changes in its environment – without failing. A lack of robustness in AI would look like a self-driving car that performs flawlessly in sunshine but malfunctions in rain. Reliability, on the other hand, is about consistency – knowing the self-driving car will perform as expected and deliver accurate results over time. To achieve this, we need high-quality, diverse training data, rigorous testing, and continuous monitoring to ensure our AI systems are dependable and trustworthy in the real world.

Transparency & accountability: Shining a light on AI's decisions

Transparency and accountability in AI are about making sure we can understand why AI makes the decisions it does and ensuring responsible use. By prioritizing transparency and accountability, we can build trust in AI systems, foster responsible use, and ensure that AI remains a force for good in society.

Transparency means being open about the data used to train the AI, the model's architecture, and the evaluation process. It also involves documenting design choices and potential limitations. This helps build trust and allows for scrutiny of the AI's workings.

Explainability goes a step further by providing insights into how the AI arrives at its conclusions. This could involve techniques that visualize the model's decision-making process or highlight the factors that influenced its output. Explainability is crucial for understanding and addressing potential biases, errors, or unintended consequences of AI's actions.

We extend our gratitude to Dr. Andrew Ng of DeepLearning.AI and Dr. Savannah Thais of Columbia University for their invaluable review and insights, which greatly enriched this blog series.