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Big Data LDN
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Deep Learning
Model Deployment
Safety-Critical Systems
Explainable AI
Model Testing
Register for this course and see more details by visiting:
OpenCourser.com/course/vjhs22/research
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Taught by Big Data LDN, who are industry experts in deploying deep learning models
Reviews best practices for model testing, hardware selection, and team collaboration
Stresses the importance of explainable models, ensuring AI systems can be understood and trusted in critical applications
Applicable to various industry verticals where AI safety is paramount, such as autonomous vehicles, medical devices, and aerospace
May require prior knowledge in deep learning and model deployment
Assumes familiarity with specialized hardware, including GPUs and FPGAs
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Activities
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Research vs. Reality in AI: Would You Trust Your Model with Your Life?.
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Career center
Learners who complete Research vs. Reality in AI: Would You Trust Your Model with Your Life? will develop knowledge and skills
that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers create and maintain models that solve real-world problems. They help build the systems that power self-driving cars, facial recognition software, and medical diagnosis tools. This course would be particularly helpful for Machine Learning Engineers who are working on safety-critical applications, as it will help them to understand the importance of model accuracy, justification, and testing.
Data Scientist
Data Scientist
Data Scientists use data to solve business problems. They collect, clean, and analyze data to identify trends and patterns. This course would be helpful for Data Scientists who are working on developing AI systems for safety-critical applications, as it will help them to understand the importance of model accuracy, justification, and testing.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software systems. They work on a variety of projects, from small personal apps to large enterprise systems. This course would be helpful for Software Engineers who are working on developing AI systems for safety-critical applications, as it will help them to understand the importance of model accuracy, justification, and testing.
Product Manager
Product Manager
Product Managers are responsible for the development and launch of new products. They work with engineers, designers, and marketers to bring new products to market. This course would be helpful for Product Managers who are working on developing AI systems for safety-critical applications, as it will help them to understand the importance of model accuracy, justification, and testing.
Business Analyst
Business Analyst
Business Analysts help businesses to identify and solve problems. They work with stakeholders to gather requirements, analyze data, and develop solutions. This course would be helpful for Business Analysts who are working on developing AI systems for safety-critical applications, as it will help them to understand the importance of model accuracy, justification, and testing.
Project Manager
Project Manager
Project Managers plan, execute, and close projects. They work with stakeholders to define project scope, budget, and timeline. This course would be helpful for Project Managers who are working on developing AI systems for safety-critical applications, as it will help them to understand the importance of model accuracy, justification, and testing.
Systems Engineer
Systems Engineer
Systems Engineers design, develop, and maintain complex systems. They work on a variety of projects, from small embedded systems to large-scale enterprise systems. This course would be helpful for Systems Engineers who are working on developing AI systems for safety-critical applications, as it will help them to understand the importance of model accuracy, justification, and testing.
Quality Assurance Analyst
Quality Assurance Analyst
Quality Assurance Analysts test software to ensure that it meets requirements. They work with developers to identify and fix bugs. This course would be helpful for Quality Assurance Analysts who are working on testing AI systems for safety-critical applications, as it will help them to understand the importance of model accuracy, justification, and testing.
Information Security Analyst
Information Security Analyst
Information Security Analysts protect computer systems and networks from unauthorized access, use, disclosure, disruption, modification, or destruction. This course would be helpful for Information Security Analysts who are working on developing AI systems for safety-critical applications, as it will help them to understand the importance of model accuracy, justification, and testing.
Data Architect
Data Architect
Data Architects design and implement data management solutions. They work with stakeholders to identify data requirements, develop data models, and create data pipelines. This course would be helpful for Data Architects who are working on developing AI systems for safety-critical applications, as it will help them to understand the importance of model accuracy, justification, and testing.
Database Administrator
Database Administrator
Database Administrators manage and maintain databases. They work with developers to ensure that databases are running smoothly and efficiently. This course would be helpful for Database Administrators who are working on developing AI systems for safety-critical applications, as it will help them to understand the importance of model accuracy, justification, and testing.
Network Administrator
Network Administrator
Network Administrators manage and maintain computer networks. They work with users to ensure that networks are running smoothly and efficiently. This course would be helpful for Network Administrators who are working on developing AI systems for safety-critical applications, as it will help them to understand the importance of model accuracy, justification, and testing.
Systems Analyst
Systems Analyst
Systems Analysts analyze and design computer systems. They work with users to identify needs and develop solutions. This course would be helpful for Systems Analysts who are working on developing AI systems for safety-critical applications, as it will help them to understand the importance of model accuracy, justification, and testing.
Computer Programmer
Computer Programmer
Computer Programmers write and maintain computer programs. They work with users to identify needs and develop solutions. This course would be helpful for Computer Programmers who are working on developing AI systems for safety-critical applications, as it will help them to understand the importance of model accuracy, justification, and testing.
Computer Support Specialist
Computer Support Specialist
Computer Support Specialists provide technical support to users. They work with users to resolve problems and provide training. This course may be helpful for Computer Support Specialists who are working on supporting AI systems for safety-critical applications, as it will help them to understand the importance of model accuracy, justification, and testing.
For more career information including salaries, visit:
OpenCourser.com/course/vjhs22/research
Reading list
We haven't picked any books for this reading list yet.
Provides a practical guide to deep learning, focusing on the design and implementation of deep learning models. It is written by a leading researcher in the field and is known for its clear explanations and hands-on approach.
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Provides a hands-on introduction to deep learning using the Python programming language. It is written by the creator of the Keras deep learning library and is known for its practical examples and clear explanations.
Provides a comprehensive overview of deep learning for natural language processing, covering the fundamental concepts, algorithms, and applications. It is written by a leading researcher in the field and is considered one of the most authoritative resources on deep learning for NLP.
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Provides a practical guide to deep learning for computer vision, focusing on the design and implementation of deep learning models for image and video processing. It is written by a leading researcher in the field and is known for its clear explanations and hands-on approach.
Provides a comprehensive overview of deep learning for finance, covering the fundamental concepts, algorithms, and applications. It is written by a leading researcher in the field and is known for its clear explanations and hands-on approach.
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Provides a comprehensive overview of deep learning for robotics, covering the fundamental concepts, algorithms, and applications. It is written by a leading researcher in the field and is known for its clear explanations and hands-on approach.
Provides a comprehensive overview of deep learning for materials science, covering the fundamental concepts, algorithms, and applications. It is written by a leading researcher in the field and is known for its clear explanations and hands-on approach.
Provides a comprehensive overview of deep learning for climate science, covering the fundamental concepts, algorithms, and applications. It is written by a leading researcher in the field and is known for its clear explanations and hands-on approach.
Provides a comprehensive overview of deep learning for transportation, covering the fundamental concepts, algorithms, and applications. It is written by a leading researcher in the field and is known for its clear explanations and hands-on approach.
Provides a comprehensive overview of deep learning for genomics, covering the fundamental concepts, algorithms, and applications. It is written by a leading researcher in the field and is known for its clear explanations and hands-on approach.
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作为一本中文著作，深入浅出地讲解了深度学习的原理、算法和应用，适合作为入门或进阶的学习教材。
Provides a hands-on guide to model deployment. It includes step-by-step instructions on how to deploy models to different platforms.
Offers an intuitive introduction to machine learning concepts. It helps build a foundational understanding of how ML models work, which is beneficial before exploring how to deploy them effectively. It's suitable for beginners looking for a less technical entry point.
Provides a hands-on guide to model deployment. It includes step-by-step instructions on how to build and deploy models in production.
Offers a broad view of the entire field of production machine learning, covering the ML lifecycle from data to deployment and monitoring. It helps identify key topics and provides references for deeper dives. It's a good starting point for understanding the scope of production ML.
Provides a comprehensive overview of the entire machine learning lifecycle, with a strong emphasis on the engineering aspects required for production. It covers best practices and design patterns for building reliable, scalable, and maintainable ML systems. This book is highly valuable as a core reference for anyone serious about putting ML into production.
Offers a holistic approach to designing ML systems, considering the entire process from data engineering to monitoring in production. It uses an iterative framework with case studies, making it practical for understanding the complexities of real-world ML system design. It's an excellent resource for deepening understanding and is often referenced by practitioners.
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Focusing on the practical aspects of MLOps, this book guides readers through applying DevOps principles to machine learning. It covers building, deploying, monitoring, and maintaining ML systems using various cloud platforms. is particularly useful for gaining hands-on knowledge and good reference for operationalizing ML models.
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Serves as a solid introduction to MLOps, explaining its concepts, the problems it addresses, and key principles for scaling machine learning in an enterprise setting. It's valuable for gaining a broad understanding of MLOps and its importance in the ML lifecycle.
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Provides a comprehensive overview of deep learning, covering the fundamental concepts, algorithms, and applications. It is written by three leading researchers in the field and is considered one of the most authoritative resources on deep learning.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/vjhs22/research
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Big Data LDN
This course belongs to a
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Big Data LDN 2019 . It's comprised of 101 courses:
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- Modernise Your IT Infrastructure for AI
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- Full Stack Metadata Driven Thinking to Drive Business Agility
- Enhance Developer Experience Using OpenShift on Azure
- Fast Data for Faster Business
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- Introduction to Adept and How Big Data and Analytics Is Transforming the Connected Car Ecosystem
- Bridge to Cloud
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