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Azure AI & ML

Optimize Language Models for AI Applications

Whizlabs Instructor

The course progresses to managing and evaluating models, covering key concepts such as performance monitoring, retraining strategies, and best practices for ensuring model accuracy. Learners will gain expertise in Azure AutoML workflows, from data preparation to model selection and evaluation, ensuring automated yet effective ML development. Additionally, the course covers key aspects of MLOps, enabling seamless integration with Azure services for scalable and secure machine learning operations.

This course is structured into multiple modules, each featuring lessons and video lectures that provide theoretical insights and hands-on practice. Participants will engage with approximately 3:00–4:00 hours of instructional content, ensuring both conceptual understanding and practical application. To reinforce learning, graded and ungraded assignments are included within each module to test the ability of learners in real-world scenarios.

Module 1: Azure AI Foundry: End-to-End Model Development & Optimization

Module 2: Optimize model training with Azure Machine Learning

By end of this course, you will be able to learn

Understand the concepts of Azure AI Foundry, including its role in model optimization, fine-tuning, and retrieval-augmented generation (RAG) strategies.

Learn how to explore and manage the Model Catalog and Collections within Azure AI Foundry and ML, and use compute resources effectively.

Gain practical experience testing and manually evaluating prompts in the Azure AI Foundry portal playground, including tracking prompt variants.

Discover how to create and configure search indexes in the Azure portal, using Azure AI Search for enhanced data retrieval and model deployment.

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What's inside

Syllabus

Azure AI Foundry: End-to-End Model Development & Optimization

This module provides a comprehensive understanding of Azure AI Foundry and its capabilities, equipping learners with the skills to leverage AI models for advanced applications. Participants will explore key concepts such as Retrieval Augmented Generation (RAG) for enhancing AI-driven responses, fine-tuning strategies for optimizing model performance, and best practices for deploying AI models in production environments. The module covers the Azure AI Foundry model catalog, compute considerations, and how to test and refine language models using the interactive playground. Learners will gain expertise in manually evaluating prompts, defining and tracking prompt variants, and utilizing Azure AI Search to create efficient search indexes. By the end of this module, participants will be prepared to work with Azure AI Foundry and ML tools, ensuring scalable and high-performing AI solutions for various enterprise applications.

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Career center

Learners who complete Azure AI & ML: Optimize Language Models for AI Applications will develop knowledge and skills that may be useful to these careers:

MLOps Engineer

An MLOps Engineer specializes in streamlining the machine learning lifecycle, from development to deployment and monitoring, ensuring scalability and reliability. The "Azure AI & ML: Optimize Language Models for AI Applications" course is a direct path for aspiring MLOps Engineers, as it explicitly covers key aspects of MLOps. Learners gain expertise in seamless integration with Azure services for scalable and secure machine learning operations. The course details preparing machine learning workflows for production, transitioning from notebooks to scripts, executing command jobs, and integrating MLflow for model tracking and evaluation. Furthermore, participants learn pipeline creation, custom components, and prebuilt workflows to automate and optimize ML processes, alongside managing resources, tracking ML models, and refining training workflows for real-world applications. This comprehensive approach ensures learners can build and manage robust ML pipelines within Azure Machine Learning effectively.

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Machine Learning Engineer

A Machine Learning Engineer focuses on designing, building, deploying, and maintaining machine learning systems in production. The "Azure AI & ML: Optimize Language Models for AI Applications" course is exceptionally well-suited for aspiring Machine Learning Engineers. It provides a comprehensive foundation in operationalizing machine learning solutions in production environments, managing and evaluating models for performance, and ensuring model accuracy through robust retraining strategies. Learners gain expertise in Azure AutoML workflows, from data preparation to model selection, and delve into MLOps practices, which are critical for scalable and secure machine learning operations. Specifically, the course covers deploying and optimizing AI models in production with Azure AI Foundry, understanding compute resources, and refining training workflows for real-world applications using Azure Machine Learning. This ensures learners are equipped not only to develop but also to successfully deploy and maintain advanced ML systems.

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Natural Language Processing Engineer

A Natural Language Processing Engineer develops systems that enable computers to understand, interpret, and generate human language. The "Azure AI & ML: Optimize Language Models for AI Applications" course is highly relevant for this career path, as its very title highlights "Optimize Language Models for AI Applications." Learners gain a comprehensive understanding of leveraging AI models for advanced applications, specifically focusing on Retrieval Augmented Generation (RAG) for enhancing AI-driven responses and fine-tuning strategies critical for optimizing language model performance. The course covers testing and refining language models using the interactive playground, manually evaluating prompts, and deploying AI models in production environments through Azure AI Foundry and Azure Machine Learning. This practical expertise in managing and operationalizing language models is essential for any aspiring Natural Language Processing Engineer.

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Prompt Engineer

A Prompt Engineer is critical in optimizing the performance and output of large language models by crafting, testing, and refining input prompts. This role is directly supported by the "Azure AI & ML: Optimize Language Models for AI Applications" course. Learners gain practical experience testing and manually evaluating prompts in the Azure AI Foundry portal playground, including tracking prompt variants. The course provides a comprehensive understanding of leveraging AI models for advanced applications, including Retrieval Augmented Generation (RAG) for enhancing AI-driven responses and fine-tuning strategies for optimizing model performance, which are integral to prompt engineering. By learning to refine language models and deploy them efficiently in production, participants develop the precise skills needed to excel as a Prompt Engineer, ensuring high-performing, accurate, and contextually relevant AI model responses.

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AI Engineer

An AI Engineer focuses on developing, integrating, and deploying artificial intelligence solutions, often involving advanced models and specialized platforms. For those looking to become an AI Engineer, the "Azure AI & ML: Optimize Language Models for AI Applications" course provides a robust foundation. It covers leveraging AI models for advanced applications, including Retrieval Augmented Generation (RAG) for enhancing AI-driven responses and fine-tuning strategies for optimizing model performance. Participants gain practical experience deploying AI models in production environments using Azure AI Foundry, exploring its model catalog, and understanding compute considerations crucial for scalable AI solutions. The course also emphasizes testing and refining language models through interactive playgrounds, manually evaluating prompts, and utilizing Azure AI Search for efficient data retrieval, all vital skills for building high-performing AI applications.

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Applied Scientist - Machine Learning

An Applied Scientist Machine Learning role bridges the gap between fundamental research and practical engineering, applying advanced ML techniques to solve complex problems. This position often requires an advanced degree. The "Azure AI & ML: Optimize Language Models for AI Applications" course is highly relevant, providing learners with skills to deploy, manage, and optimize ML models efficiently. Participants gain expertise in Azure AutoML workflows, covering data preparation to model selection, and delve into MLOps for scalable and secure operations. Crucially, the course explores Azure AI Foundry for end-to-end model development and optimization, including Retrieval Augmented Generation (RAG) and fine-tuning strategies. This precise blend of theoretical understanding in model optimization and practical application, particularly in refining language models and deploying them in production environments, is ideal for an Applied Scientist Machine Learning.

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Deep Learning Engineer

A Deep Learning Engineer designs, implements, and optimizes neural network architectures for various AI tasks, including natural language processing and computer vision. The "Azure AI & ML: Optimize Language Models for AI Applications" course provides excellent preparation for this specialized role. With its focus on optimizing language models, fine-tuning strategies for model performance, and leveraging AI models for advanced applications like Retrieval Augmented Generation (RAG), the course directly addresses core deep learning concepts. Participants learn to deploy and manage these complex models in production environments using Azure Machine Learning and Azure AI Foundry. The curriculum on model evaluation, performance monitoring, and retraining strategies is crucial for ensuring the accuracy and efficiency of deep learning models in real-world scenarios, equipping learners with the skills to build and deploy sophisticated deep learning solutions.

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Technical Lead Machine Learning

A Technical Lead Machine Learning guides teams in the design, development, and deployment of machine learning systems, ensuring technical excellence and strategic alignment. This role often requires an advanced degree. The "Azure AI & ML: Optimize Language Models for AI Applications" course provides a comprehensive understanding crucial for a Technical Lead Machine Learning. It equips learners with expertise in deploying, managing, and optimizing ML models efficiently, operationalizing solutions in production, and applying MLOps best practices for scalable and secure operations. The course's detailed coverage of Azure AI Foundry for end-to-end model development, including RAG and fine-tuning, alongside optimizing model training with Azure Machine Learning, provides the breadth and depth required to make informed architectural decisions, mentor team members, and troubleshoot complex production issues within advanced AI systems.

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Solutions Architect Data and AI

A Solutions Architect Data and AI designs overarching data and artificial intelligence strategies and solutions, integrating various platforms and services. This role often requires an advanced degree. The "Azure AI & ML: Optimize Language Models for AI Applications" course helps build a foundation for a Solutions Architect Data and AI by providing a deep understanding of operationalizing machine learning solutions and AI applications within the Azure ecosystem. Learners explore key concepts such as Retrieval Augmented Generation (RAG), fine-tuning strategies, and deploying AI models in production environments using Azure AI Foundry. The course's emphasis on MLOps, managing compute resources, and utilizing Azure AI Search for enhanced data retrieval and model deployment, provides critical insights into designing scalable, secure, and performant data and AI architectures, ensuring successful enterprise-level implementations.

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AI Solutions Architect

An AI Solutions Architect designs and oversees the implementation of complex AI and machine learning systems within an organization, focusing on scalability, security, and integration. This role often requires an advanced degree. The "Azure AI & ML: Optimize Language Models for AI Applications" course helps build a foundation for an AI Solutions Architect by providing a comprehensive understanding of deploying, managing, and optimizing ML models efficiently within Azure. Learners explore operationalizing machine learning solutions in production environments and key aspects of MLOps, enabling seamless integration with Azure services for scalable and secure machine learning operations. The course also covers Azure AI Foundry, its role in model optimization, fine-tuning, and RAG strategies, along with managing the Model Catalog and compute resources effectively, which are all crucial for designing robust AI architectures.

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Cloud Engineer AI Focus

A Cloud Engineer AI Focus specializes in designing, implementing, and managing cloud infrastructure specifically tailored for artificial intelligence and machine learning workloads. The "Azure AI & ML: Optimize Language Models for AI Applications" course is exceptionally relevant for this specialization. Learners gain a comprehensive foundation in Azure Machine Learning, understanding how to operationalize machine learning solutions in production environments using Azure services. The course covers key aspects of MLOps, enabling seamless integration with Azure services for scalable and secure machine learning operations. Participants explore managing compute resources effectively within Azure AI Foundry and Machine Learning, and learn to create and configure search indexes using Azure AI Search for enhanced data retrieval and model deployment. This expertise in leveraging Azure's ecosystem for AI and ML is central to the role of a Cloud Engineer AI Focus.

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Data Scientist (Machine Learning)

A Data Scientist Machine Learning analyzes complex datasets to uncover insights and builds predictive models, often contributing to their deployment. The "Azure AI & ML: Optimize Language Models for AI Applications" course helps build a foundation for a Data Scientist Machine Learning by emphasizing comprehensive model management and evaluation. Participants learn to monitor performance, understand retraining strategies, and apply best practices for ensuring model accuracy. The course also covers Azure AutoML workflows, from data preparation to model selection and evaluation, which are core data science activities. Furthermore, the modules on optimizing model training with Azure Machine Learning, including working with metrics, hyperparameters, and data transformation techniques, are directly applicable. While the course extends into engineering aspects, a strong understanding of robust deployment and MLOps principles is increasingly valuable for data scientists to ensure their models make it to production.

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Machine Learning Researcher

A Machine Learning Researcher explores new algorithms, models, and techniques to advance the state of the art in artificial intelligence. This role typically requires an advanced degree. The "Azure AI & ML: Optimize Language Models for AI Applications" course may be useful for an aspiring Machine Learning Researcher by providing a strong practical grounding in optimizing language models and advanced AI applications. Learners gain an understanding of concepts like Retrieval Augmented Generation (RAG) and fine-tuning strategies for model performance, which are active areas of research. The course's focus on evaluating models, tracking prompt variants, and understanding the nuances of model optimization within Azure AI Foundry and Machine Learning platforms can inform research directions and provide tools for testing novel approaches. While primarily focused on application, the specific aspects of model optimization and performance analysis are relevant to research.

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AI Product Manager

An AI Product Manager defines the vision, strategy, and roadmap for AI-powered products, translating complex technical capabilities into user value. The "Azure AI & ML: Optimize Language Models for AI Applications" course may be useful for an aspiring AI Product Manager. While not a coding role, understanding the technical depth of AI model deployment, optimization, and management is paramount for making informed product decisions. The course covers operationalizing machine learning solutions in production environments, key aspects of MLOps for scalable AI operations, and the capabilities of Azure AI Foundry, including fine-tuning and Retrieval Augmented Generation (RAG) strategies. This knowledge of how models are built, deployed, and maintained, along with understanding prompt engineering and model evaluation, helps a product manager effectively communicate with engineering teams and define feasible and impactful AI products.

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Data Engineer AI Pipelines

A Data Engineer AI Pipelines focuses on building and optimizing data pipelines and infrastructure to support machine learning models, ensuring data quality and availability. The "Azure AI & ML: Optimize Language Models for AI Applications" course may be useful for an aspiring Data Engineer AI Pipelines. It covers aspects of Azure AutoML workflows, from data preparation to model selection, highlighting the importance of robust data inputs for ML. The course also delves into pipeline creation, custom components, and prebuilt workflows within Azure Machine Learning, which are directly relevant to automating data and model processes. Understanding how ML models consume and transform data, as well as the production readiness aspects related to managing resources and refining training workflows, provides a valuable perspective for data engineers specializing in AI, enabling them to design more effective and integrated data solutions.

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Reading list

We've selected 50 books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Azure AI & ML: Optimize Language Models for AI Applications.

Designing Machine Learning Systems

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Definitive industry reference covering the end-to-end architecture of production ML systems: streaming versus batch feature pipelines, training-serving skew, continual learning, data distribution shifts, shadow deployment, and infrastructure orchestration.

Azure AI & ML

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