AI Specialist
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LLM Models, Prompt Management, Code and Automation
LLama
The open-source model allows AI Specialists to run AI directly on the company’s servers. This ensures maximum security and privacy of sensitive data. This provides full control over the code, enabling deep customization and fine-tuning of the model to meet specific business needs. Furthermore, with no subscription fees for queries or technical optimizations (e.g., quantization), this solution drastically reduces costs for large-scale operations.
Mistral AI
A European artificial intelligence platform specializing in delivering advanced open-source language models with a particular emphasis on Sovereign AI (data sovereignty). The tool allows companies to fully control the technology they deploy: models can be downloaded, customized with internal data (using Forge), and run in a completely secure, private environment (on-premises or in a VPC cloud). Sensitive information never leave its structures.
Hugging Face
It’s a leading platform and global community focused on artificial intelligence, considered the “GitHub for machine learning.” It serves as a central place for collaboration, development, and deployment of modern AI solutions. The service offers direct access to over 2 million pre-built models, 500,000 datasets, and demo applications (Spaces) spanning text, image, audio, and video. Key free programming libraries provides a unified API for integrating models.
MLFlow
An open-source platform designed to manage the full lifecycle of AI projects, machine learning models (MLOps), and LLM agents and applications (LLMOps). It helps engineers automate and control the software development process from experiment to production deployment. The tool is divided into two main areas: for traditional ML, it offers experiment tracking, model evaluation, and a central registry (Model Registry). For generative AI (LLM), it provides advanced monitoring (observability/tracing), quality evaluation tools, a prompt registry, and a security gateway (AI Gateway) for controlling costs and API request limits.
Weight Baises
It is an advanced development platform for AI engineers for tracking experiments, visualizing machine learning processes, and optimizing models. The platform (part of CoreWeave Forge) acts as a “black box” and control panel for training neural networks.
Kubeflow
Cloud Native AI is a platform specifically designed to run and automate full AI processes directly in Kubernetes. It’s a powerful tool for engineering teams that connects data and AI ecosystems with cloud infrastructure. Kubeflow’s primary goal is to enable seamless code portability (from laptops, to on-premises servers, to hyper-scalable cloud computing) and manage massive training jobs for AI models and agents.
VS Code
Developed by Microsoft, this free, open-source code editor is the absolute standard for developers and AI specialists worldwide. The modern version of the editor has been transformed into an advanced AI-powered development center, natively supporting the AI-driven development paradigm:
Weight Baises
It is an advanced development platform for AI engineers for tracking experiments, visualizing machine learning processes, and optimizing models. The platform (part of CoreWeave Forge) acts as a “black box” and control panel for training neural networks.
Kubeflow
Cloud Native AI is a platform specifically designed to run and automate full AI processes directly in Kubernetes. It’s a powerful tool for engineering teams that connects data and AI ecosystems with cloud infrastructure. Kubeflow’s primary goal is to enable seamless code portability (from laptops, to on-premises servers, to hyper-scalable cloud computing) and manage massive training jobs for AI models and agents.
A Day in the Life of an AI Specialist:
The AI ​​Specialist starts the day in Jupyter Notebook, analyzing the purity of a new training dataset using the Pandas library. Next, they run a fine-tuning process on an open-source model (e.g., the latest Llama) on the AWS SageMaker cloud, monitoring the progress and declines of the loss function in Weights & Biases graphs. Once the model is running successfully, they containerize it using Docker and configure a Pinecone vector database to connect the model to the company's internal documents, creating a secure and fast infrastructure.