Engineering AI Excellence
Hey there, fellow AI enthusiast! Ever feel like the world of AI engineering is moving at warp speed, and it's tough to keep up with the latest tools and techniques? That's exactly why I put together "Engineering AI Excellence" – a no-nonsense guide to building and deploying AI systems that are not just good, but great.
Let's be honest, building AI that works isn't enough anymore. We need AI that's efficient, scalable, private, and fair. That's a tall order, but this book is packed with practical, hands-on advice to help you achieve it.
Chapter 1: GPU Resource Optimization: Getting the Most from Your Hardware
We all know GPUs are the workhorses of AI, but they're not cheap. This chapter dives into clever ways to squeeze every drop of performance out of your hardware, so you can train models faster and deploy them without breaking the bank.
Chapter 2: Orchestrating AI with Kubernetes: Scaling AI Workloads
When your AI project grows from a pet project to a full-blown production system, Kubernetes is your new best friend. Learn how to use this powerful orchestration tool to manage complex AI workloads, scaling them up or down as needed.
Chapter 3: Federated Learning in Practice: Privacy-Preserving AI Deployment
Worried about data privacy? Federated learning might be the answer. This chapter explains how to train models on distributed data without actually moving it around, keeping sensitive information safe and sound.
Chapter 4: Serverless GPUs for AI Inference: Cost-Effective Deployment
Don't let inference costs eat into your profits. Serverless GPUs are a game-changer, providing scalable inference on demand. Discover how to leverage this technology for cost-effective deployment.
Chapter 5: Model Compression for Faster AI: Practical Techniques and Tools
Large models can be slow and cumbersome. Explore techniques like quantization and pruning to shrink your models without sacrificing accuracy, making them faster and more efficient.
Chapter 6: AI Infrastructure as Code: Automating Your AI Pipeline
Automation is the key to efficiency. Learn how to treat your AI infrastructure like code, using tools like Terraform to automate deployment and manage your entire AI pipeline.
Chapter 7: AI Observability in Action: Monitoring and Debugging AI Systems
AI systems can be complex and unpredictable. Get hands-on with monitoring and debugging tools to catch problems early and ensure your AI is running smoothly.
Chapter 8: Mitigating Bias in AI: A Practical Guide
Biased AI is bad news. This chapter digs into the causes of bias in AI models and offers practical strategies to mitigate it, ensuring your AI is fair and equitable.
Chapter 9: A/B Testing for AI Models: Experimentation for Better Results
Don't guess when you can test. A/B testing is your secret weapon for optimizing AI models. Learn how to experiment with different versions to find the ones that perform best.
Chapter 10: Chaos Engineering for AI: Building Resilient Systems
The real world is messy, and so are AI systems. Embrace chaos engineering to stress-test your AI, uncovering weaknesses and building resilience into your systems.
So, whether you're a seasoned AI engineer or just getting started, "Engineering AI Excellence" is your roadmap to building AI that's not just functional, but truly exceptional. Let's build the future of AI together – one that's fast, efficient, private, and fair.
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Let's be honest, building AI that works isn't enough anymore. We need AI that's efficient, scalable, private, and fair. That's a tall order, but this book is packed with practical, hands-on advice to help you achieve it.
Chapter 1: GPU Resource Optimization: Getting the Most from Your Hardware
We all know GPUs are the workhorses of AI, but they're not cheap. This chapter dives into clever ways to squeeze every drop of performance out of your hardware, so you can train models faster and deploy them without breaking the bank.
Chapter 2: Orchestrating AI with Kubernetes: Scaling AI Workloads
When your AI project grows from a pet project to a full-blown production system, Kubernetes is your new best friend. Learn how to use this powerful orchestration tool to manage complex AI workloads, scaling them up or down as needed.
Chapter 3: Federated Learning in Practice: Privacy-Preserving AI Deployment
Worried about data privacy? Federated learning might be the answer. This chapter explains how to train models on distributed data without actually moving it around, keeping sensitive information safe and sound.
Chapter 4: Serverless GPUs for AI Inference: Cost-Effective Deployment
Don't let inference costs eat into your profits. Serverless GPUs are a game-changer, providing scalable inference on demand. Discover how to leverage this technology for cost-effective deployment.
Chapter 5: Model Compression for Faster AI: Practical Techniques and Tools
Large models can be slow and cumbersome. Explore techniques like quantization and pruning to shrink your models without sacrificing accuracy, making them faster and more efficient.
Chapter 6: AI Infrastructure as Code: Automating Your AI Pipeline
Automation is the key to efficiency. Learn how to treat your AI infrastructure like code, using tools like Terraform to automate deployment and manage your entire AI pipeline.
Chapter 7: AI Observability in Action: Monitoring and Debugging AI Systems
AI systems can be complex and unpredictable. Get hands-on with monitoring and debugging tools to catch problems early and ensure your AI is running smoothly.
Chapter 8: Mitigating Bias in AI: A Practical Guide
Biased AI is bad news. This chapter digs into the causes of bias in AI models and offers practical strategies to mitigate it, ensuring your AI is fair and equitable.
Chapter 9: A/B Testing for AI Models: Experimentation for Better Results
Don't guess when you can test. A/B testing is your secret weapon for optimizing AI models. Learn how to experiment with different versions to find the ones that perform best.
Chapter 10: Chaos Engineering for AI: Building Resilient Systems
The real world is messy, and so are AI systems. Embrace chaos engineering to stress-test your AI, uncovering weaknesses and building resilience into your systems.
So, whether you're a seasoned AI engineer or just getting started, "Engineering AI Excellence" is your roadmap to building AI that's not just functional, but truly exceptional. Let's build the future of AI together – one that's fast, efficient, private, and fair.
Engineering AI Excellence
Hey there, fellow AI enthusiast! Ever feel like the world of AI engineering is moving at warp speed, and it's tough to keep up with the latest tools and techniques? That's exactly why I put together "Engineering AI Excellence" – a no-nonsense guide to building and deploying AI systems that are not just good, but great.
Let's be honest, building AI that works isn't enough anymore. We need AI that's efficient, scalable, private, and fair. That's a tall order, but this book is packed with practical, hands-on advice to help you achieve it.
Chapter 1: GPU Resource Optimization: Getting the Most from Your Hardware
We all know GPUs are the workhorses of AI, but they're not cheap. This chapter dives into clever ways to squeeze every drop of performance out of your hardware, so you can train models faster and deploy them without breaking the bank.
Chapter 2: Orchestrating AI with Kubernetes: Scaling AI Workloads
When your AI project grows from a pet project to a full-blown production system, Kubernetes is your new best friend. Learn how to use this powerful orchestration tool to manage complex AI workloads, scaling them up or down as needed.
Chapter 3: Federated Learning in Practice: Privacy-Preserving AI Deployment
Worried about data privacy? Federated learning might be the answer. This chapter explains how to train models on distributed data without actually moving it around, keeping sensitive information safe and sound.
Chapter 4: Serverless GPUs for AI Inference: Cost-Effective Deployment
Don't let inference costs eat into your profits. Serverless GPUs are a game-changer, providing scalable inference on demand. Discover how to leverage this technology for cost-effective deployment.
Chapter 5: Model Compression for Faster AI: Practical Techniques and Tools
Large models can be slow and cumbersome. Explore techniques like quantization and pruning to shrink your models without sacrificing accuracy, making them faster and more efficient.
Chapter 6: AI Infrastructure as Code: Automating Your AI Pipeline
Automation is the key to efficiency. Learn how to treat your AI infrastructure like code, using tools like Terraform to automate deployment and manage your entire AI pipeline.
Chapter 7: AI Observability in Action: Monitoring and Debugging AI Systems
AI systems can be complex and unpredictable. Get hands-on with monitoring and debugging tools to catch problems early and ensure your AI is running smoothly.
Chapter 8: Mitigating Bias in AI: A Practical Guide
Biased AI is bad news. This chapter digs into the causes of bias in AI models and offers practical strategies to mitigate it, ensuring your AI is fair and equitable.
Chapter 9: A/B Testing for AI Models: Experimentation for Better Results
Don't guess when you can test. A/B testing is your secret weapon for optimizing AI models. Learn how to experiment with different versions to find the ones that perform best.
Chapter 10: Chaos Engineering for AI: Building Resilient Systems
The real world is messy, and so are AI systems. Embrace chaos engineering to stress-test your AI, uncovering weaknesses and building resilience into your systems.
So, whether you're a seasoned AI engineer or just getting started, "Engineering AI Excellence" is your roadmap to building AI that's not just functional, but truly exceptional. Let's build the future of AI together – one that's fast, efficient, private, and fair.
Let's be honest, building AI that works isn't enough anymore. We need AI that's efficient, scalable, private, and fair. That's a tall order, but this book is packed with practical, hands-on advice to help you achieve it.
Chapter 1: GPU Resource Optimization: Getting the Most from Your Hardware
We all know GPUs are the workhorses of AI, but they're not cheap. This chapter dives into clever ways to squeeze every drop of performance out of your hardware, so you can train models faster and deploy them without breaking the bank.
Chapter 2: Orchestrating AI with Kubernetes: Scaling AI Workloads
When your AI project grows from a pet project to a full-blown production system, Kubernetes is your new best friend. Learn how to use this powerful orchestration tool to manage complex AI workloads, scaling them up or down as needed.
Chapter 3: Federated Learning in Practice: Privacy-Preserving AI Deployment
Worried about data privacy? Federated learning might be the answer. This chapter explains how to train models on distributed data without actually moving it around, keeping sensitive information safe and sound.
Chapter 4: Serverless GPUs for AI Inference: Cost-Effective Deployment
Don't let inference costs eat into your profits. Serverless GPUs are a game-changer, providing scalable inference on demand. Discover how to leverage this technology for cost-effective deployment.
Chapter 5: Model Compression for Faster AI: Practical Techniques and Tools
Large models can be slow and cumbersome. Explore techniques like quantization and pruning to shrink your models without sacrificing accuracy, making them faster and more efficient.
Chapter 6: AI Infrastructure as Code: Automating Your AI Pipeline
Automation is the key to efficiency. Learn how to treat your AI infrastructure like code, using tools like Terraform to automate deployment and manage your entire AI pipeline.
Chapter 7: AI Observability in Action: Monitoring and Debugging AI Systems
AI systems can be complex and unpredictable. Get hands-on with monitoring and debugging tools to catch problems early and ensure your AI is running smoothly.
Chapter 8: Mitigating Bias in AI: A Practical Guide
Biased AI is bad news. This chapter digs into the causes of bias in AI models and offers practical strategies to mitigate it, ensuring your AI is fair and equitable.
Chapter 9: A/B Testing for AI Models: Experimentation for Better Results
Don't guess when you can test. A/B testing is your secret weapon for optimizing AI models. Learn how to experiment with different versions to find the ones that perform best.
Chapter 10: Chaos Engineering for AI: Building Resilient Systems
The real world is messy, and so are AI systems. Embrace chaos engineering to stress-test your AI, uncovering weaknesses and building resilience into your systems.
So, whether you're a seasoned AI engineer or just getting started, "Engineering AI Excellence" is your roadmap to building AI that's not just functional, but truly exceptional. Let's build the future of AI together – one that's fast, efficient, private, and fair.
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Engineering AI Excellence
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Product Details
| ISBN-13: | 9783384333070 |
|---|---|
| Publisher: | Azhar ul Haque Sario and Tammy Aikens Co. |
| Publication date: | 08/24/2024 |
| Pages: | 152 |
| Product dimensions: | 6.00(w) x 9.00(h) x 0.35(d) |
About the Author
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