Whether in the cloud or hybrid environments, AI Engineers develop and deploy cognitive services, machine learning, and knowledge mining solutions to help their organization stay ahead of the game.
Artificial intelligence (AI) engineers are responsible for developing, programming and training the complex networks of algorithms that make up AI so that they can function like a human brain. This role requires combined expertise in software development, programming, data science and data engineering. Though this career is related to data engineering, AI engineers are rarely required to write the code that develops scalable data sharing. Instead, artificial intelligence developers locate and pull data from a variety of sources, create, develop and test machine learning models and then utilize application program interface (API) calls or embedded code to build and implement AI applications.
I have a certificate Microsoft Certified: Azure AI Fundamentals, that confirm my knowledge of machine learning (ML) and artificial intelligence (AI) concepts and related Microsoft Azure services (Azure, Bot Service, Cognitive Search, Cognitive Services, Computer Vision, Form Recognizer, Language Understanding, Machine Learning, QnA Maker, Speech to Text, Speech Translation, Text Analytics, Text to Speech, Translator Speech, Translator Text, AI Engineer, Developer, Data Scientist).
As a AI Engineer I can:
- designing, developing, and deploying Azure-based AI solutions, including machine learning models, cognitive services, and data analytics solutions,
- collaborating with cross-functional teams, such as data scientists, business analysts, and developers, to design and implement AI solutions that meet business requirements,
- building and training machine learning models using Azure Machine Learning, and tuning models for optimal performance,
- developing and deploying custom AI models using Azure Cognitive Services, such as speech recognition, language understanding, and computer vision,
- creating data pipelines to collect, process, and prepare data for analysis and modeling using Azure data services, such as Azure Data Factory and Azure Databricks,
- implementing data analytics solutions using Azure Synapse Analytics or other Azure data services,
- deploying and managing Azure services and resources using Azure DevOps or other deployment tools,
- monitoring and troubleshooting deployed solutions to ensure optimal performance and reliability,
- ensuring compliance with security and regulatory requirements related to AI solutions,
- staying up-to-date with the latest Azure AI technologies and industry developments, and sharing knowledge and best practices with the team.
In this track I learned the core concepts of productionizing and monitoring machine learning models to add business value. This skill track covered the complete life-cycle of a machine learning application, ranging from the gathering of business requirements to the design, development, deployment, operation, and maintenance stages.
In this course I learned what MLOps is, understand the different phases in MLOps processes, and identify different levels of MLOps maturity. I also learned about systems and tools to better scale and automate machine learning operations, including feature stores, experiment tracking, CI/CD pipelines, microservices, and containerization.
In this course I learned best practices for packaging and serializing both models and environments for production to ensure that models will last as long as possible. I also learned how to be able to design and develop machine learning models that are ready for production and continuously to improve them over time.
In this course I learned to write ML code that minimizes technical debt, discover the tools I’ll need to deploy and monitor my models, and examine the different types of environments and analytics I’ll encounter. I also learned how to develop models for deployment and how to write effective ML code, leverage tools, and train ML pipelines. I learned about drift monitoring in production, as well as model feedback, updates, and governance and understood how I can use MLOps lifecycle to deploy my own models in production.
In this course I learned how to use automation in MLOps to deploy ML systems that can deliver value over time. I also learned how hidden technical debt affects ML systems and the value they produce. I understood how automating and streamlining the stages of the ML lifecycle can help the operation and scaling of ML systems.
In this track I gained actionable knowledge on popular AI topics like ChatGPT, large language models, generative AI, and more.
In this course I learned about machine learning, deep learning, explainable AI, natural language processing, generative models, and more. I also explored a variety of AI applications in everyday life and the AI techniques behind the scenes, how can organizations become AI-driven, and what are the key lessons learned, challenges, and societal implications of AI’s unstoppable progress.
In this course I dived headfirst into the exciting world of generative artificial intelligence (AI) and discover how to wield ChatGPT like a pro. From text summarization, explaining complex concepts, drafting engaging marketing content, and generating and explaining code, I learned about the most common applications of ChatGPT. Finally, I explored the legal and ethical considerations that come with implementing ChatGPT in various situations.
In this course I learned how this exciting technology powers everything from self-driving cars to my personal Amazon shopping suggestions.
In this course I embarked on an exhilarating journey through the world of Large Language Models (LLMs) and discovered how they are reshaping the AI landscape. I explored the factors fueling the LLM boom, such as the deep learning revolution, vast data availability, and cutting-edge computing power.
In this course I learned about the exciting concepts in this emerging field and how to prepare for a future where AI becomes widely adopted. I started by comprehending how these models create content, where they fit into the machine learning landscape, and how they are developed.
In this course I learned about the core principles of ethical AI and expand your understanding of common challenges and opportunities in the field of AI ethics. Through hands-on exercises, I built your skills to craft ethical AI by design.
This course gave me a broad overview of how machine learning works, how to train neural networks, and how to deploy those networks to microcontrollers, which is known as embedded machine learning or TinyML.
In this course I learned the basics of a machine learning system, how to deploy a machine learning model to a microcontroller, and how to use machine learning to make decisions and predictions in an embedded system.
Edge Impulse is the leading development platform for machine learning on edge devices, free for developers and trusted by enterprises. Founded in 2019 by Zach Shelby and Jan Jongboom, we are on a mission to enable developers to create the next generation of intelligent devices. We believe that machine learning can enable positive change in society, and we are dedicated to support applications for good.
⇒ Verify at: Coursera
In this course I learned what Generative AI is, how it is used, and how it differs from traditional machine learning methods. I learned how to define Generative AI, how Generative AI works, how describe Generative AI Model types and Generative AI Applications.
⇒ Verify at: Coursera
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