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Associate AI Engineer for Data Scientists
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Associate AI Engineer for Data Scientists
Become an AI Engineer: From Data Science to Production-Ready AI
Start your journey to becoming an AI Engineer by building the essential skills to take AI models from development to deployment. In this Track, you'll gain hands-on experience with the latest AI technologies and best practices, enabling you to create robust, production-ready AI solutions.Master the AI Development Lifecycle
Progress through the key stages of the AI development process, including:- Training and evaluating machine learning models using Python libraries like scikit-learn and PyTorch
- Working with real-world datasets to solve practical problems across various domains
- Fine-tuning state-of-the-art Large Language Models (LLMs) like Llama 3 for natural language tasks
- Integrating AI models into applications using frameworks like LangChain
- Applying MLOps principles to ensure reliable and scalable AI deployments
Gain Hands-on Experience with Cutting-Edge AI Technologies
Explore the tools and techniques driving the AI revolution through practical experience with deep learning architectures, including CNNs, RNNs, LSTMs, and GRUs. You'll also work with transformer-based models and their applications in natural language processing, gaining insight into their impact on modern AI. Additionally, you'll learn explainable AI methods to build transparent and accountable AI systems while applying responsible AI practices to manage data effectively throughout the AI lifecycle.From LLMs to Production: Putting AI into Practice
Apply your skills to real-world scenarios that mirror the challenges faced by AI Engineers. You'll learn to fine-tune LLMs like Llama 3 on custom datasets, integrate them into applications using LangChain, and deploy these solutions into production environments. Discover how MLOps principles like testing, version control, and continuous integration can help you build reliable and scalable AI systems.Designed for Data Scientists Transitioning to AI Engineering
This Track is ideal for data scientists looking to expand their skill set and take on AI engineering roles. Building on your existing knowledge of machine learning and Python, you'll acquire the additional skills needed to design, develop, and deploy production-grade AI solutions. No prior experience with AI engineering or MLOps is required.Launch Your Career as an AI Engineer
Upon completing this Track, you'll have the confidence and the portfolio to:- Apply for AI Engineer positions across industries
- Collaborate with cross-functional teams to deliver end-to-end AI solutions
- Implement responsible AI practices and build trustworthy AI systems
- Stay at the forefront of the rapidly evolving AI landscape
Prerequisites
There are no prerequisites for this trackCourse
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
Navigate and use the extensive repository of models and datasets available on the Hugging Face Hub.
Course
Learn how to build your first neural network, adjust hyperparameters, and tackle classification and regression problems in PyTorch.
Course
Gain the essential skills using Scikit-learn, SHAP, and LIME to test and build transparent, trustworthy, and accountable AI systems.
Course
Learn about fundamental deep learning architectures such as CNNs, RNNs, LSTMs, and GRUs for modeling image and sequential data.
Project
Develop a multi-input model to classify characters from scanned documents.
Course
Learn the theory behind responsibly managing your data for any AI project, from start to finish and beyond.
Course
Learn the nuts and bolts of LLMs and the revolutionary transformer architecture they are based on!
Project
Use LLMs to solve diverse language tasks for a car dealership company.
Course
Explore the latest techniques for running the Llama LLM locally 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
Learn about modularity, documentation, and automated testing to help you solve data science problems more quickly and reliably.
Course
Discover the fundamentals of Git for version control in your software and data projects.
Course
Master Python testing: Learn methods, create checks, and ensure error-free code with pytest and unittest.
Complete
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