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Key takeaways

Foundation models are a type of machine learning model trained on large data sets, which you can then adapt for various applications. Here are some important things to know:

• Ninety-six percent of North American employers expect artificial intelligence and information processing technologies to transform their business from 2025 to 2030 [].

• Common foundation models include computer vision models, natural language processing, and generative AI.聽

• You can use artificial intelligence skills in careers such as a machine learning engineer, data scientist, and natural language processing (NLP) engineer.

Discover how AI foundation models can help you save time and money when developing machine learning applications. If you鈥檙e ready to learn how to build machine learning models, consider enrolling in Stanford and DeepLearning.AI鈥檚 Machine Learning with Scikit-learn, PyTorch & Hugging Face Specialization. You鈥檒l have the opportunity to practice training neural networks and developing AI applications in as little as two months.

What is a foundation model?

A foundation model is a machine learning model trained on large and diverse datasets that can be adapted depending on the task or application for which it is being used. Rather than developing a new machine learning model for each application you make, you can instead utilize the same pre-trained foundation model and just do some fine-tuning along the way. Foundation models can adapt to various environments through transfer learning, where the model leverages knowledge gained in one task to apply it to a different but related task.

Read more: What are Deep Learning Models? Types, Uses, and More

How to build with foundation models

Building with a foundation model requires training through self-supervised learning, where the model trains itself by assigning labels to unlabeled data. To optimize a foundation model for a specific task, you will fine-tune the model by adjusting parameters and identifying the most relevant data to focus on.聽

Another technique you can use to optimize your foundation model is regularization, which prevents overfitting 鈥攁 problem where machine learning models understand the training data but fail to make accurate predictions with new data. By limiting the amount of variance in a highly complex model through regularization, you can enhance the performance of your foundation model as you apply it to different environments.

Use cases for foundation models

Foundation models can apply to many different types of artificial intelligence systems, allowing you to adapt the foundation model for your specific use case, such as natural language processing, generative AI, computer vision, and audio processing:

• Natural language processing: Using a foundation model for natural language processing allows you to customize your model to find the valuable insights you鈥檙e looking for within text, such as sentiment from social media posts.

• Generative AI: OpenAI鈥檚 ChatGPT is an example of foundation models in generative AI, where a GPT-3.5, a large language model (LLM), served as the foundation model for the earliest version of ChatGPT.聽

• Computer vision: You can use foundation models to detect and identify objects within images, classify images, and edit photos and videos. This is useful in developing self-driving vehicles, as well as facial recognition technology.聽

• Audio processing: With foundation models, you can train them to handle your audio input for a range of tasks, such as language translation, video captioning, voice commands, virtual assistants, and speech-to-text transcription.

What is a foundation model vs. LLM?

A foundation model and LLM differ because an LLM represents one type of foundation model while the term 鈥渇oundation model鈥� embodies a broader category describing AI models that you can adapt to perform a wide range of tasks. While foundation models utilize various data types for training, LLMs are an ideal foundation model for training with text data.聽

Who uses foundation models?

Foundation models are present in a wide range of industries, such as manufacturing, autonomous vehicles, and health care, that are looking to harness the power of artificial intelligence, where data scientists, NLP engineers, and machine learning engineers can use them to develop highly flexible applications. In the health care industry, for example, you can use foundation models to analyze medical images and literature, while in the software development sector, programmers can use this technology to generate or debug code.聽

Leading AI companies like Amazon, Google, and OpenAI all use foundation models. Amazon Nova foundation models work for software development, customer service, task automation, and more. For Google, BERT is an early example of a foundation model that you can fine-tune to complete NLP tasks. OpenAI鈥檚 ChatGPT was originally made possible through the use of the GPT-3.5 foundation model.

When to use a foundation model

If you鈥檙e in a situation where you have limited access to quality data and lack the necessary time to build complex AI models, using a foundation model can help you overcome these challenges. Businesses and organizations that implement foundation models benefit from the increased efficiency of AI development, as well as the cost benefit of using a pre-trained foundation model, rather than making the significant investment in building their own. However, when using foundation models, it鈥檚 important to make certain considerations pertaining to the data your model uses. For example, you will want to ensure training data doesn鈥檛 include biased information, as this can negatively impact the quality of your model's output.聽

Where does AI get its information?

AI gets its information from analyzing large amounts of data, where it learns to identify patterns and trends within the data. This training data can come from a variety of sources, such as publicly available information on the internet and internal data sources directly from your organization or a third-party provider.聽

Get started in AI foundation models

To start working with foundation models, look to learn fundamental AI skills. Key skills for a career in artificial intelligence include programming languages like Python and Java, knowledge of big data tools such as Apache Spark, as well as cloud computing and AI ethics. Now is a great time to pursue a career in artificial intelligence, with 96 percent of employers expecting AI to transform their business from 2025 to 2030 [], along with a high demand for positions like data scientists and machine learning engineers.

You can also begin a career in AI by earning a bachelor鈥檚 degree in statistics, mathematics, data science, engineering, or computer science. When pursuing your degree, make sure you take courses in ML, AI, data processing, and algorithms because establishing this foundation can help prepare you for a master鈥檚 degree.

Explore our free artificial intelligence career resources

If you鈥檙e considering a career in artificial intelligence, check out some free resources, such as our LinkedIn newsletter, . Then, explore these resources to learn more:

• Watch on YouTube:

• Hear from an industry expert: 6 Questions with an IBM Data Scientist and AI Engineer

• Grow your programming knowledge: Python Glossary: Essential Terms and Definitions

Whether you want to develop a new skill or get comfortable with an in-demand technology, keep growing with a 糖心vlog官网观看 Plus subscription, where you鈥檒l get access to over 10,000 flexible courses.聽

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