{"id":111827,"date":"2024-12-11T08:38:58","date_gmt":"2024-12-11T16:38:58","guid":{"rendered":"https:\/\/www.backblaze.com\/blog\/?p=111827"},"modified":"2025-12-11T12:23:57","modified_gmt":"2025-12-11T20:23:57","slug":"ai-101-building-and-deploying-an-ai-model","status":"publish","type":"post","link":"https:\/\/www.backblaze.com\/blog\/ai-101-building-and-deploying-an-ai-model\/","title":{"rendered":"AI 101: Building and Deploying an AI Model"},"content":{"rendered":"\r\n
\"A<\/figure>\r\n\r\n\r\n\r\n
\u00a0<\/div>\r\n\r\n\r\n\r\n

Should you build your own AI model? Or use other services to help you accelerate the process?<\/p>\r\n\r\n\r\n\r\n

Once you\u2019ve defined the problem<\/a> you\u2019re trying to solve and the AI model type<\/a> that best fits your needs, these are the questions you\u2019re faced with next\u2014where to deploy an AI model and how to go about doing it. In most cases, there is very little reason for you to build, train, and deploy your AI model from scratch, particularly as more and more vendors are stepping in to help companies with all or some of the process. It\u2019s fundamentally complex, takes tons of resources and requires specialized knowledge to do correctly.\u00a0<\/p>\r\n\r\n\r\n\r\n

Still, you should have a basic understanding of the process for deploying AI model, as these learnings will be useful as later on as you explore various predefined tools, applications, and services<\/a> you can use to expedite or enhance your ability to use AI within your organization. That\u2019s what I\u2019m digging into today.<\/p>\r\n\r\n\r\n\r\n

How AI model training works<\/strong><\/p>\r\n\r\n\r\n\r\n

There are several steps in training and deploying an AI model which include identification and gathering the data required, data cleansing and assembly, training the model, checkpointing, and, finally, model serving<\/a> where the model is deployed into the production environment. Here\u2019s an overview of the process.\u00a0<\/p>\r\n\r\n\r\n

\r\n
\"A<\/figure>\r\n<\/div>\r\n\r\n\r\n

Let\u2019s take a minute to explore each of the steps for building and deploying an AI model in a little more detail.<\/p>\r\n\r\n\r\n\r\n

Step 1: Review<\/strong>\u00a0<\/p>\r\n\r\n\r\n\r\n

The organizational data needed to help educate your model will either be structured or unstructured<\/a>. Structured data is found in databases, tables, and so on. Unstructured data is basically everything else. Some unstructured data is easy to process, such as text files, while other data is harder to extract, such as PDFs and images.\u00a0<\/p>\r\n\r\n\r\n\r\n

In general, the more data you can provide, the better your trained model can be. But, remember to include data that is not<\/em> what you want as well\u2014this helps models to hone in on the specific piece of information when things are similar. Take this example scenario, for instance:\u00a0\u00a0<\/p>\r\n\r\n\r\n\r\n

You are monitoring hundreds of thousands of wooded acres to determine if there is a fire on the land. As part of training the model, you need to provide images of the legitimate flora and fauna along with images of fire. But you should also provide images of what is not fire, for example reflections of the sun or moon on a lake, a group of lightning bugs at night, car headlights, and so on.<\/p>\r\n\r\n\r\n\r\n

Step 2: Clean<\/strong>\u00a0<\/p>\r\n\r\n\r\n\r\n

As the data is collected, it will need to be pre-processed, which involves several techniques such as cleaning the data to handle missing values, removing outliers, scaling features, encoding categorical variables, and splitting the data into training and testing sets. The data needs to be arranged in a manner acceptable to the model itself. This sounds relatively simple, but some studies show that this can take up to 80% of the total model development process time<\/a>.\u00a0<\/p>\r\n\r\n\r\n\r\n

Step 3: Stage<\/strong>\u00a0<\/p>\r\n\r\n\r\n\r\n

This is a collection point for all of the clean, ready to be processed, data. This data will arrive as it is processed (cleaned) which can occur over several days or even weeks. Having this data on hand will be useful if the model is not generated correctly or in the future as a starting point to retrain the model.<\/p>\r\n\r\n\r\n\r\n

Typically large amounts of your data will be cleaned and staged as it is readied to train the AI model. But, there are no special storage requirements for this data. It just needs to be readily available to be uploaded to the AI training environment when the time comes.\u00a0<\/p>\r\n\r\n\r\n\r\n

Step 4: Train<\/strong>\u00a0<\/p>\r\n\r\n\r\n\r\n

Model training is a resource intensive process where data is copied from staging to high-performance storage located in close proximity to whatever high-powered processor you\u2019re rocking<\/a>, usually a graphical processing unit (GPU). The GPUs then run the algorithms developed specifically for training the model, and the data is iteratively read and processed an indeterminate number of times until training is complete. Minimizing the time spent utilizing these expensive, high-powered storage and processing resources is critical in managing the overall cost of building the model. In other words: get in, process, and get out.<\/p>\r\n\r\n\r\n\r\n

Step 5: Checkpoint<\/strong>\u00a0<\/p>\r\n\r\n\r\n\r\n

During the building of the model, the programming will often create snapshots of the status of the training process. This will include various variables, state changes, and so on. These snapshots are referred to as checkpoints. They initially will be written to local storage within the model training system, and are used to restart the training process from a known good state if something goes wrong.\u00a0<\/p>\r\n\r\n\r\n\r\n

Once the model training process is complete, checkpoints should be written to the same centralized data storage location as your staged data. The checkpoint data will become part of the documentation of the model and may be used for forensic purposes should the model not behave appropriately once it is deployed.<\/p>\r\n\r\n\r\n\r\n

Step 6: Serve<\/strong>\u00a0<\/p>\r\n\r\n\r\n\r\n

Once the training process is complete, the model can be exported to your central storage location. This will once again help document the system, and from there the model can then be uploaded to the local or cloud compute environment where it will be used.\u00a0\u00a0<\/p>\r\n\r\n\r\n\r\n

At this point you have a clean version of the source data, the checkpoints of the model created, and a copy of the model itself, all stored in your centralized location under your control and readily available should they be needed in the future.\u00a0<\/p>\r\n\r\n\r\n\r\n

AI model inference<\/strong><\/p>\r\n\r\n\r\n\r\n

The term inference<\/a> is derived from the AI model\u2019s perspective. At a high level, when given a prompt, the model infers its response from the trained model and its data. In simple terms, you\u2019ve trained your model to recognize cats, and then you bring it new data (a picture of a family reunion) and ask your model if it sees any cats in the photo (I\u2019m hoping the answer is yes).\u00a0<\/p>\r\n\r\n\r\n\r\n

In AI, the prompt is viewed as new data which is compared to the model\u2019s existing data to determine a response typically in the form of a decision, prediction, or new content as is the case with generative AI models.\u00a0<\/p>\r\n\r\n\r\n\r\n

An overview of the inference process is below:<\/p>\r\n\r\n\r\n

\r\n
\"A<\/figure>\r\n<\/div>\r\n\r\n\r\n

In some AI systems, the inference process flow includes some additional code to help improve your model. These types of filters can have a range of uses and can happen on either the input or the output stage. For example, if you want to filter inappropriate queries or information, you could include something like keyword filtering when data (the prompt) is input. Or, you could introduce a toxicity detection filter on the output side, which reviews responses and prevents harmful or offensive content to be presented to the user.\u00a0\u00a0<\/p>\r\n\r\n\r\n\r\n

A perhaps better understood problem that filters like this can address is how to get accurate and up-to-date information out of your queried response. On the input flow side of things, retrieval-augmented generation (RAG)<\/a> directs a trained model to incorporate and weight more heavily information from trusted sources that the user designates. On the output side, you might add a hallucination prevention filter, which would stop the model from presenting false or misleading information.\u00a0\u00a0<\/p>\r\n\r\n\r\n\r\n

More broadly, you\u2019ll notice that both the prompt and response are saved. It is important to review this information on a periodic basis. This is especially true if the model is public facing, if\u00a0 you are using a model which can change over time such as a foundation model, or if you are using a model which utilizes RAG techniques to include new or external content.\u00a0<\/p>\r\n\r\n\r\n\r\n

In all of those examples, your model can drift as new information is introduced, and, as we noted above, getting the right information and cleaning it properly is likely the most time-intensive and important stage of this process. Not for nothing is the phrase \u201cknowledge is power\u201d a truism\u2014in the age of AI, knowledge is power and good data is king.\u00a0<\/p>\r\n","protected":false},"excerpt":{"rendered":"

Is integrating AI on your company’s roadmap? Read this primer on the AI model training and deployment process to understand how to approach it.<\/p>\n","protected":false},"author":182,"featured_media":111090,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"content-type":"","footnotes":""},"categories":[7,434,438,483],"tags":[489,468],"class_list":["post-111827","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-cloud-storage","category-featured-1","category-featured-cloud-storage","category-tech-lab","tag-ai-ml","tag-b2cloud","entry"],"acf":[],"yoast_head":"\nAI 101: Understanding AI Architecture and AI Agent Deployment<\/title>\n<meta name=\"description\" content=\"Deploying an AI model? 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