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Associate AI Engineer for Data Scientists

Train and fine-tune the latest AI models for production, including LLMs like Llama 3. Start your journey to becoming an AI Engineer today!
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PythonArtificial Intelligence48 hours

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Track Description

Associate AI Engineer for Data Scientists

Begin building AI solutions and gain the career-building skills you need to succeed as an AI Engineer, from model development to deployment into production! In this track, you’ll train and evaluate robust predictive models on real-world datasets across a variety of domains. Starting with the fundamentals of machine learning, you’ll get hands-on with some of the most popular Python libraries for machine learning and deep learning, including scikit-learn, PyTorch, and many more. As you progress, you’ll get hands-on with Large Language Models (LLMs) for a variety of natural language tasks. You'll learn to fine-tune Llama 3 on custom data and integrate this into a LangChain application to begin surfacing predictions to end-users. Finally, you'll discover what it takes to move an AI model from a notebook into production. You'll build foundational skills in MLOps, including testing and version control for production.

Prerequisites

There are no prerequisites for this track
  • Course

    1

    Supervised Learning with scikit-learn

    Grow your machine learning skills with scikit-learn in Python. Use real-world datasets in this interactive course and learn how to make powerful predictions!

  • Course

    Learn how to cluster, transform, visualize, and extract insights from unlabeled datasets using scikit-learn and scipy.

  • Course

    Gain the essential skills using Scikit-learn, SHAP, and LIME to test and build transparent, trustworthy, and accountable AI systems.

  • Project

    bonus

    Developing Multi-Input Models For OCR

    Develop a multi-input model to classify characters from scanned documents.

  • Course

    10

    Working with Llama 3

    Explore the latest techniques for running the Llama LLM locally, fine-tuning it, and integrating it within your stack.

  • Course

    Discover how MLOps can take machine learning models from local notebooks to functioning models in production that generate real business value.

  • Course

    Discover the fundamentals of Git for version control in your software and data projects.

Associate AI Engineer for Data Scientists
12 courses
Track
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