Add AI engineer roadmap content

src/data/roadmaps/ai-engineer/content/googles-gemini@oe8E6ZIQWuYvHVbYJHUc1.md

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 # Google's Gemini
 
-Google's Gemini is a machine learning infrastructural initiative designed by Google to automate the design and optimization of machine learning models. By streamlining the process of model selection and hyper-parameter tuning, Gemini reduces the time and computational resources required to create effective machine learning solutions. For an aspiring AI engineer, mastering tools like Gemini is essential as it automates much of the grunt work and allows engineers to focus on more complex and high-level tasks. Hence, Gemini is a significant tool in the AI Engineer's roadmap to proficiency in machine learning and artificial intelligence.
+Gemini, formerly known as Bard, is a generative artificial intelligence chatbot developed by Google. Based on the large language model of the same name, it was launched in 2023 after being developed as a direct response to the rise of OpenAI's ChatGPT

src/data/roadmaps/ai-engineer/content/hugging-face-hub@YLOdOvLXa5Fa7_mmuvKEi.md

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 # Hugging Face Hub
 
-Hugging Face Hub is a platform where you can share, access and collaborate upon a wide array of machine learning models, primarily focused on Natural Language Processing (NLP) tasks. It is a central repository that facilitates storage and sharing of models, reducing the time and overhead usually associated with these tasks. For an AI Engineer, leveraging Hugging Face Hub can accelerate model development and deployment, effectively allowing them to work on structuring efficient AI solutions instead of worrying about model storage and accessibility issues.
+Hugging Face Hub is a platform where you can share, access and collaborate upon a wide array of machine learning models, primarily focused on Natural Language Processing (NLP) tasks. It is a central repository that facilitates storage and sharing of models, reducing the time and overhead usually associated with these tasks. For an AI Engineer, leveraging Hugging Face Hub can accelerate model development and deployment, effectively allowing them to work on structuring efficient AI solutions instead of worrying about model storage and accessibility issues.
+
+Visit the following resources to learn more:
+
+- [@official@Hugging Face](https://huggingface.co/)

src/data/roadmaps/ai-engineer/content/hugging-face-models@8XjkRqHOdyH-DbXHYiBEt.md

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 # Hugging Face Models
 
-Hugging Face Models are a set of sophisticated AI tools, primarily in Natural Language Processing (NLP), released by the Hugging Face company. They provide development and deploying capabilities for chatbots, translation, language understanding and generation, and have been widely used for research and application development. From an AI Engineer perspective, these pre-trained models can greatly reduce the time and resources necessary for developing AI applications, particularly when dealing with complex NLP tasks. As an AI engineer, understanding and knowing how to implement, fine-tune, and utilize these models is an important skill set to have.
+Hugging Face has a wide range of pre-trained models that can be used for a variety of tasks, including language understanding and generation, translation, chatbots, and more. Anyone can create an account and use their models, and the models are organized by task, provider, and other criteria.
+
+Visit the following resources to learn more:
+
+- [@official@Hugging Face](https://huggingface.co/models)

src/data/roadmaps/ai-engineer/content/hugging-face-models@EIDbwbdolR_qsNKVDla6V.md

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 # Hugging Face Models
 
-Hugging Face is a company that created a highly modular and efficient set of models primarily designed to work with AI tasks involving Natural Language Processing (NLP). These models provide pre-trained solutions that handle complex tasks such as translation, summarization, and conversation, to name a few. AI engineers can utilize these Hugging Face models in their projects to efficiently manage challenging NLP functions. Along the AI engineer roadmap, mastering and integrating such tools becomes indispensable, as NLP is an important pillar of many AI systems, especially those involved in semantic analysis and human-computer interaction.
+Hugging Face has a wide range of pre-trained models that can be used for a variety of tasks, including language understanding and generation, translation, chatbots, and more. Anyone can create an account and use their models, and the models are organized by task, provider, and other criteria.
+
+Visit the following resources to learn more:
+
+- [@official@Hugging Face](https://huggingface.co/models)

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 # Hugging Face Tasks
 
-Hugging Face Tasks refer to a suite of activities or problems in natural language processing (NLP) that AI engineers tackle using Hugging Face, a transformative open-source library designed for NLP. This library intents to democratize NLP by providing straightforward and user-friendly solutions to some of the most complex NLP tasks. Hugging Face Tasks include, but are not limited to, sentiment analysis, question answering, summarization, translation, and language modeling. These tasks are heavily reliant on machine learning algorithms and models, such as transformers. As an aspiring AI Engineer, harnessing the efficiency and capabilities of Hugging Face for NLP tasks is a critical milestone to chart.
+Hugging face has a section where they have a list of tasks with the popular models for that task.
+
+Visit the following resources to learn more:
+
+- [@official@Hugging Face](https://huggingface.co/tasks)

src/data/roadmaps/ai-engineer/content/hugging-face@v99C5Bml2a6148LCJ9gy9.md

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 # Hugging Face
 
-Hugging Face is a technology company that specializes in the field of natural language processing, developing both open-source libraries and applications to help researchers, developers, and businesses leverage the latest advancements in AI technologies in their projects. Its primary product, the Transformers library, is recognized and widely used in the AI community for tasks related to language understanding, translation, summarization, and more. As an AI engineer, mastery of Hugging Face resources provides a strong foundation in navigating the complexities and nuances of natural language processing, a subfield of AI that focuses on the interaction between computers and humans.
+Hugging Face is the platform where the machine learning community collaborates on models, datasets, and applications.
+
+Visit the following resources to learn more:
+
+- [@official@Hugging Face](https://huggingface.co/)

src/data/roadmaps/ai-engineer/content/impact-on-product-development@qJVgKe9uBvXc-YPfvX_Y7.md

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 # Impact on Product Development
 
-The Impact on Product Development in an AI Engineer's roadmap refers to how the incorporation of Artificial Intelligence (AI) can transform the process of creating, testing, and delivering new products. This could range from utilizing AI for enhanced data analysis to inform product design, use of AI-powered automation in production processes, or even AI as a core feature of the product itself. By understanding this impact, AI Engineers can establish an effective roadmap to incorporate AI features and processes into their product development strategy. They could thus create more innovative, efficient, and customer-focused products.
+Incorporating Artificial Intelligence (AI) can transform the process of creating, testing, and delivering products. This could range from utilizing AI for enhanced data analysis to inform product design, use of AI-powered automation in production processes, or even AI as a core feature of the product itself.

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 # Indexing Embeddings
 
-Indexing embeddings is a technique often used in search systems, which allows for quick and effective retrieval of elements that are similar to a provided query. Embeddings represent high-dimensional data, such as text or images, in a lower-dimensional space, making it easier for comparison and analysis. As per the AI engineer's roadmap, developing a strong understanding of indexing embeddings is essential, since it is often integral to building models that deal with high-dimensional data and require effective computation methods. Learning how indexing embeddings work will enable an AI engineer to build efficient systems involving similarity searches and recommendation engines.
+This step involves converting data (such as text, images, or other content) into numerical vectors (embeddings) using a pre-trained model. These embeddings capture the semantic relationships between data points. Once generated, the embeddings are stored in a vector database, which organizes them in a way that enables efficient retrieval based on similarity. This indexed structure allows fast querying and comparison of vectors, facilitating tasks like semantic search, recommendation systems, and anomaly detection.

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 # Inference SDK
 
-Inference SDK, also known as Software Development Kit, is a collection of software tools and libraries that aid in the development of AI applications, particularly focusing on inference tasks. These tasks involve using a previously trained AI model to predict the output for a new data input. Essential for AI Engineers, the Inference SDK provides pre-compiled libraries, optimized functions, and graphical interfaces that help in running the AI algorithms efficiently. It also allows AI Engineers to focus more on developing and deploying the AI application rather than getting bogged down with manual optimization procedures.
+Inference is the process of using a trained model to make predictions on new data. As this process can be compute-intensive, running on a dedicated server can be an interesting option. The huggingface_hub library provides an easy way to call a service that runs inference for hosted models. There are several services you can connect to:
+
+Visit the following resources to learn more:
+
+- [@official@Hugging Face Inference Client](https://huggingface.co/docs/huggingface_hub/en/package_reference/inference_client)
+- [@official@Hugging Face Inference API](https://huggingface.co/docs/api-inference/en/index)

src/data/roadmaps/ai-engineer/content/inference@KWjD4xEPhOOYS51dvRLd2.md

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-# Inference 
+# Inference
 
 Inference involves using models developed through machine learning to make predictions or decisions. As part of the AI Engineer Roadmap, an AI engineer might create an inference engine, which uses rules and logic to infer new information based on existing data. Often used in natural language processing, image recognition, and similar tasks, inference can help AI systems provide useful outputs based on their training. Working with inference involves understanding different models, how they work, and how to apply them to new data to achieve reliable results.

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 # Introduction
 
-The emergence of Artificial Intelligence (AI) and its related fields has been rapid and far-reaching, spanning across numerous industries and sectors. Becoming an AI engineer entails a comprehensive understanding and ability to apply various concepts, algorithms, and technologies that are fundamental to AI. This includes programming, mathematics, machine learning, deep learning, neural networks, natural language processing and many more. A roadmap for an AI engineer is a detailed plan that depicts the requisite skills, knowledge, and steps to be followed in order to effectively navigate this exciting field. Exploring different sections will provide insight into these key areas that need to be learnt and mastered for becoming a successful AI engineer.
+An AI Engineer uses pre-trained models and existing AI tools to improve user experiences. They focus on applying AI in practical ways, without building models from scratch. This is different from AI Researchers and ML Engineers, who focus more on creating new models or developing AI theory.

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 # Know your Customers / Usecases
 
-In the landscape of Artificial Intelligence (AI) engineering, understanding your target customers and use-cases is a fundamental milestone. This knowledge informs the decisions made during the development process to ensure that the final AI solution appropriately meets the relevant needs of the users. The term 'use-case' typically refers to a list of actions or event steps necessary to achieve a particular goal. Reflecting its overall significance, this early comprehension of customers and use-cases plays a pivotal role in shaping the direction of AI solutions, defining their scope and objectives, and ultimately determining their success or failure in the market.
+Understanding your target customers and use-cases helps making informed decisions during the development to ensure that the final AI solution appropriately meets the relevant needs of the users. You can use this knowledge to choose the right tools, frameworks, technologies, design the right architecture, and even prevent abuse.

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 # LangChain for Multimodal Apps
 
-LangChain is an application development platform that enables the design and implementation of multimodal applications - applications that use a combination of different modes to interact with users, such as text, voice, visual content, and more. As an AI Engineer, understanding how to leverage LangChain in constructing multimodal applications is crucial, given the varied and complex nature of human-computer interaction. The knowledge of LangChain and its utilization can facilitate AI engineers to build sophisticated multimodal apps and empower them to take the user experience to the next level.
+LangChain is a software framework that helps facilitate the integration of large language models into applications. As a language model integration framework, LangChain's use-cases largely overlap with those of language models in general, including document analysis and summarization, chatbots, and code analysis.

src/data/roadmaps/ai-engineer/content/langchain@ebXXEhNRROjbbof-Gym4p.md

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 # Langchain
 
-Langchain is a tool designed to leverage blockchain technologies in the field of linguistics, language processing and machine learning. It's basically a language processing chain, essentially a system that deals with input in the form of a natural language and then performs various transformations on it. As part of the AI Engineer Roadmap, Langchain is essential as it brings a new angle to how artificial intelligence can be used to understand and process human languages. This tool allows the creation of language models which can be an integral part of developing AI systems with ability of natural language understanding and processing.
+LangChain is a software framework that helps facilitate the integration of large language models into applications. As a language model integration framework, LangChain's use-cases largely overlap with those of language models in general, including document analysis and summarization, chatbots, and code analysis.

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 # Llama Index
 
-Llama Index is a customizable barcode indexing system commonly used in bioinformatics, and particularly in DNA sequencing. It operates on the FASTQ format for raw sequence data, creating a sample identification index that enables easy tracking of the origin of each file. In AI engineer's journey, the knowledge of Llama Index is relevant to those operating in AI applications focused in genomics or biological research. Mastering this topic can help in architecting AI systems for these specific domains due to its efficiency in managing large raw DNA sequence files, which is essential in training machine learning models for tasks such as DNA sequence analysis, prediction and other relevant computational biology tasks.
+LlamaIndex is a simple, flexible data framework for connecting custom data sources to large language models.
+
+Visit the following resources to learn more:
+
+- [@official@LlamaIndex Official Website](https://llamaindex.ai/)

src/data/roadmaps/ai-engineer/content/llamaindex-for-multimodal-apps@akQTCKuPRRelj2GORqvsh.md

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-# LlamaIndex for Multimodal Apps
+# Llama Index
 
-LlamaIndex is an open source database solution that allows multimodal applications to efficiently access information. Multimodal applications utilize different modes of input and output to provide a more interactive and user-friendly experience. These modes can include text, images, audio, and more. In these applications, LlamaIndex's role comes into play, as it is designed to handle complex, heterogeneous data including multi-format information. The understanding, utilization and efficient handling of such database solutions can contribute to the toolset of an AI Engineer, furthering analyzation, application and system-building capabilities.
+LlamaIndex is a simple, flexible data framework for connecting custom data sources to large language models.
+
+Visit the following resources to learn more:
+
+- [@official@LlamaIndex Official Website](https://llamaindex.ai/)

src/data/roadmaps/ai-engineer/content/llms@wf2BSyUekr1S1q6l8kyq6.md

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 # LLMs
 
-LLMs, or Logic and Learning Models, are a facet of artificial intelligence that focuses on both logical representation of data and machinery and statistical learning. They exploit logic's structure and expressiveness along with learning's ability to handle uncertainty and noise. LLMs allow AI engineers to create complex models that can learn from data while integrating a wide range of prior knowledge in the form of logical constraints. These models can predict outcomes or behaviors based on the input data, paving the way for more robust and flexible AI solutions.
+LLM or Large Language Models are AI models that are trained on a large amount of text data to understand and generate human language. They are the core of applications like ChatGPT, and are used for a variety of tasks, including language translation, question answering, and more.

src/data/roadmaps/ai-engineer/content/manual-implementation@6xaRB34_g0HGt-y1dGYXR.md

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 # Manual Implementation
 
-Manual Implementation in the field of Artificial Intelligence (AI) involves coding algorithms, data structures, and mechanisms from scratch without the help of pre-built functions or libraries. It provides a deeper understanding of how AI algorithms work, how data structures are built, and how mechanisms execute. Although frameworks, libraries, and tools simplify and speed up AI development, knowing how to implement AI models manually helps an AI engineer to customize and optimize models to achieve specific project results.
+You can build the AI agents manually by coding the logic from scratch without using any frameworks or libraries. For example, you can use the OpenAI API and write the looping logic yourself to keep the agent running until it has the answer.

src/data/roadmaps/ai-engineer/content/maximum-tokens@qzvp6YxWDiGakA2mtspfh.md

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 # Maximum Tokens
 
-Maximum Tokens refer to the highest possible number that a machine learning model or program can process in a single training example. This limit directly influences the complexity of the data the model can manage. As an AI engineer, understanding the implications and limitations of maximum tokens is part of being able to effectively design and manage deep learning architectures. For linguistics-based AI efforts, like Natural Language Processing (NLP), maximum tokens can dictate the length of text that can be effectively processed by the model, and thus informs how the input data needs to be prepared.
+Number of Maximum tokens in OpenAI API depends on the model you are using.
+
+For example, the `gpt-4o` model has a maximum of 128,000 tokens.
+
+Visit the following resources to learn more:
+
+- [@official@OpenAI API Documentation](https://platform.openai.com/docs/api-reference/completions/create)

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 # Mistral AI
 
-Mistral AI is a creative software solution that uses state-of-the-art artificial intelligence algorithms for automating performance analysis tasks. It's primarily used for simulation data analysis and model calibration by integrating into existing codes and tools. It enables AI Engineers to extract useful insights from large and varied data sets, a critical skill needed to build and improve AI systems. Its key advantage lies in enabling efficient processing and interpretation of complex data sets, reducing the time taken for data analysis and thereby accelerating the AI development process.
+Mistral AI is a French startup founded in 2023, specializing in open-source large language models (LLMs). Created by former Meta and Google DeepMind researchers, it focuses on efficient, customizable AI solutions that promote transparency. Its flagship models, Mistral Large and Mixtral, offer state-of-the-art performance with lower resource requirements, gaining significant attention in the AI field.

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-# Models on Hugging Face
+# Hugging Face Models
 
-Hugging Face is a company that developed a platform for natural language processing (NLP) tasks. It primarily hosts a vast array of pre-trained models that are designed to understand and generate human-like text. Within the context of an AI Engineer's path, learning how to navigate the Hugging Face model repository is critical. It provides access to state-of-the-art models like BERT, GPT-2, GPT-3, and their own invention - DistilBERT, which can be fine-tuned for custom tasks. Understanding how these models work and how to implement them can substantially boost the capabilities of any AI solution you're working on, expedite project turnaround, and improve overall performance.
+Hugging Face has a wide range of pre-trained models that can be used for a variety of tasks, including language understanding and generation, translation, chatbots, and more. Anyone can create an account and use their models, and the models are organized by task, provider, and other criteria.
+
+Visit the following resources to learn more:
+
+- [@official@Hugging Face](https://huggingface.co/models)

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 # MongoDB Atlas
 
-MongoDB Atlas is a cloud-based database service that's fully managed by MongoDB. As a NoSQL database program, it uses JSON-like documents with optional schemas and offers the benefits of an elastic, on-demand infrastructure platform that simplifies data as a service. MongoDB Atlas is used in the AI Engineer roadmap to manage, process and analyze big and complex data. It provides scalability, geographic distribution and data recovery, essential capabilities for AI engineers when dealing with significant volumes of information needed for machine learning models and AI applications.
+MongoDB Atlas is a fully managed cloud-based NoSQL database service by MongoDB. It simplifies database deployment and management across platforms like AWS, Azure, and Google Cloud. Using a flexible document model, Atlas automates tasks such as scaling, backups, and security, allowing developers to focus on building applications. With features like real-time analytics and global clusters, it offers a powerful solution for scalable and resilient app development.
+
+Visit the following resources to learn more:
+
+- [@official@MongoDB Atlas Vector Search](https://www.mongodb.com/products/platform/atlas-vector-search)

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 # Multimodal AI Usecases
 
-Multimodal AI Usecases refer to the application of Multimodal Artificial Intelligence in different spheres. Essentially, Multimodal AI is a subfield of AI that combines different types of data input (such as visual images, sonic waveforms, and unstructured text) to improve system efficiency, performance, and output. For an AI Engineer's roadmap, understanding these use-cases not only provides a perspective on how AI can be utilized in multi-faceted ways, but also opens up novel avenues for innovation. From healthcare, where it can help in better diagnosis by analyzing medical reports and scans, to the automotive industry, where it can work to enhance self-driving technologies by processing live images, sounds etc., Multimodal AI has myriad potential applications, making it a vital learning area for any aspiring AI engineer.
+Multimodal AI integrates various data types for diverse applications. In human-computer interaction, it enhances interfaces using speech, gestures, and facial expressions. In healthcare, it combines medical scans and records for accurate diagnoses. For autonomous vehicles, it processes data from sensors for real-time navigation. Additionally, it generates images from text and summarizes videos in content creation, while also analyzing satellite and sensor data for climate insights.

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 # Multimodal AI 
 
-Multimodal AI is a subset of artificial intelligence that combines data from different sources or modes — such as text, image, and sound — to make more accurate predictions. For instance, a multimodal AI system could use a combination of text and image data to generate a description of a scene. The multimodal approach to AI brings an extra level of sophistication to machine learning models. As an aspiring AI engineer, understanding multimodal AI can enhance your data processing skills, equip you to design more complex AI systems, and offer more versatile solutions. Eventually, the ability to integrate and interpret data from multiple sources opens up a plethora of opportunities and greatly broadens the AI application spectrum.
+Multimodal AI refers to artificial intelligence systems capable of processing and integrating multiple types of data inputs simultaneously, such as text, images, audio, and video. Unlike traditional AI models that focus on a single data type, multimodal AI combines various inputs to achieve a more comprehensive understanding and generate more robust outputs. This approach mimics human cognition, which naturally integrates information from multiple senses to form a complete perception of the world. By leveraging diverse data sources, multimodal AI can perform complex tasks like image captioning, visual question answering, and cross-modal content generation.

src/data/roadmaps/ai-engineer/content/ollama-models@ro3vY_sp6xMQ-hfzO-rc1.md

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 # Ollama Models
 
-Ollama Models refer to a statistical model designed for analyzing and predicting event data. These models can capture relationships in the sequence of events and determine whether one event influences the occurrence of another. Primarily used in social science, they've found considerable application in the field of artificial intelligence. In the AI Engineer's Roadmap, understanding and implementing Ollama Models can help build robust machine learning systems. They can be used to generate probable future events based on an existing sequence, playing an instrumental role in areas like predictive analytics and recommendation systems.
+Ollama supports a wide range of language models, including but not limited to Llama, Phi, Mistral, Gemma and more.
+
+Visit the following resources to learn more:
+
+- [@official@Ollama Models](https://ollama.com/library)

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 # Ollama SDK
 
-Ollama SDK is a software development kit specifically designed to create powerful and efficient machine learning applications. It provides developers with access to cutting-edge tools, libraries, programming languages, APIs, and other resources that can help them create, test, and deploy artificial intelligence (AI) models. As a part of the AI Engineer Roadmap, understanding and learning to work with Ollama SDK can be instrumental in developing robust AI solutions, assisting both in model training and the conversion of models into a format that can be used in different applications.
+Ollama SDK can be used to develop applications locally.
+
+Visit the following resources to learn more:
+
+- [@official@Ollama SDK](https://ollama.com)

src/data/roadmaps/ai-engineer/content/ollama@rTT2UnvqFO3GH6ThPLEjO.md

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 # Ollama
 
-Ollama is not a recognizable term in the world of AI engineering. It could potentially be a typo or a misunderstood term. In accordance with the context provided, if we are referring to Llama - it is an open-source Python Machine Learning Library constructed to simplify complex routines, but it really doesn't fall in the roadmap of an AI engineer. Instead, algorithms - such as linear regression, decision trees and more established libraries like Tensorflow, PyTorch, and Keras are more commonly identified in an AI engineer's path. However, a better understanding of the term 'Ollama' in this context is necessary to offer a precise definition or introduction.
+Ollama is an open-source tool for running large language models (LLMs) locally on personal computers. It supports various models like Llama 2, Mistral, and Code Llama, bundling weights, configurations, and data into a single package. Ollama offers a user-friendly interface, API access, and integration capabilities, allowing users to leverage AI capabilities while maintaining data privacy and control. It's designed for easy installation and use on macOS and Linux, with Windows support in development.
+
+Visit the following resources to learn more:
+
+- [@official@Ollama](https://ollama.com)

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 # Open AI Embedding Models
 
 Open AI embedding models refer to the artificial intelligence variants designed to reformat or transcribe input data into compact, dense numerical vectors. These models simplify and reduce the input data from its original complex nature, creating a digital representation that is easier to manipulate. This data reduction technique is critical in the AI Engineer Roadmap because it paves the way for natural language processing tasks. It helps in making precise predictions, clustering similar data, and producing accurate search results based on contextual relevance.
+
+Visit the following resources to learn more:
+
+- [@official@Open AI Embedding Models](https://platform.openai.com/docs/guides/embeddings)

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 # Performing Similarity Search
 
-Performing similarity search is a technique often utilized in information retrieval and machine learning. Essentially, this process involves identifying and retrieving the data points that most closely match a given query. Often, this is implemented using distance or other mathematical metrics. In the roadmap to becoming an AI engineer, mastering similarity search becomes crucial as it's a key methodology in recommendation systems, image or speech recognition, and natural language processing - all important aspects of AI and machine learning. This understanding will equip AI engineers to create sophisticated AI models capable of creating associations and understanding nuances in the data.
+This step involves querying the vector database to find the most similar embeddings to a given input vector. When a query is made, the system computes the distance between the input vector and stored embeddings using metrics like cosine similarity or Euclidean distance. The closest matches—those with the smallest distances—are retrieved as results, allowing for accurate semantic search, recommendations, or content retrieval based on similarity in the embedded space. This process enables highly efficient and relevant searches across large datasets.

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 # What is an AI Engineer?
 
-An AI Engineer is a technical professional who specialises in the development and maintenance of systems and platforms that utilise artificial intelligence. Utilizing the advances in machine learning and data science, the AI Engineers are primarily responsible for creating, testing and implementing AI models. Their work revolves around developing solutions and algorithms that enable machines to mimic human intelligence. In the roadmap of becoming an AI Engineer, understanding their role, duties, and skills required is of paramount importance, as it creates a foundational understanding of the journey ahead.
+An AI Engineer uses pre-trained models and existing AI tools to improve user experiences. They focus on applying AI in practical ways, without building models from scratch. This is different from AI Researchers and ML Engineers, who focus more on creating new models or developing AI theory.