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🏗️ Ml Operations

Focuses on the practices and tools for deploying, monitoring, and maintaining machine learning models in production—where data science meets DevOps discipline.
Designing Machine Learning Systems
by Chip Huyen • 2022
Accuracy is not enough. An iterative playbook for machine learning systems that survive production.
4.45
1k+
⚙️ Machine Learning Engineering🔧 Mlops🔄 Iterative Design
Designing Machine Learning Systems Summary
Designing Machine Learning Systems 91%
Accuracy is not enough. An iterative playbook for machine learning systems that survive production.
by Chip Huyen 2022
4.45
1k+ ratings
⚙️ Machine Learning Engineering🔧 Mlops🔄 Iterative Design
The Machine Learning Solutions Architect Handbook
by David Ping • 2022
Most ML models never make it to production. The architecture playbook for the ones that do.
4.06
16
🏢 Enterprise Ml🏗️ Solution Architecture🔄 Ml Lifecycle
The Machine Learning Solutions Architect Handbook Summary
The Machine Learning Solutions Architect Handbook 90%
Most ML models never make it to production. The architecture playbook for the ones that do.
by David Ping 2022
4.06
16 ratings
🏢 Enterprise Ml🏗️ Solution Architecture🔄 Ml Lifecycle
Natural Language Processing with Transformers
by Lewis Tunstall • 2022
Build NLP applications with the open-source stack that turned transformer research practical.
4.39
211
🗣️ Natural Language Processing🧠 Deep Learning🤖 Transformers
Natural Language Processing with Transformers Summary
Natural Language Processing with Transformers 89%
Build NLP applications with the open-source stack that turned transformer research practical.
by Lewis Tunstall 2022
4.39
211 ratings
🗣️ Natural Language Processing🧠 Deep Learning🤖 Transformers
AI and Machine Learning for Coders
by Laurence Moroney • 2021
Turn your code into AI that runs everywhere: train, optimize, deploy with a single toolkit.
4.09
108
🤖 Machine Learning🧠 Deep Learning🔵 Tensorflow
AI and Machine Learning for Coders Summary
AI and Machine Learning for Coders 89%
Turn your code into AI that runs everywhere: train, optimize, deploy with a single toolkit.
by Laurence Moroney 2021
4.09
108 ratings
🤖 Machine Learning🧠 Deep Learning🔵 Tensorflow
Practicing Trustworthy Machine Learning
by Yada Pruksachatkun • 2023
Privacy, fairness, explainability, robustness: the engineering guide for ML models that earn trust.
4.95
111
🤖 Responsible Ai⚙️ Ml Engineering⚖️ Model Fairness
Practicing Trustworthy Machine Learning Summary
Practicing Trustworthy Machine Learning 89%
Privacy, fairness, explainability, robustness: the engineering guide for ML models that earn trust.
by Yada Pruksachatkun 2023
4.95
111 ratings
🤖 Responsible Ai⚙️ Ml Engineering⚖️ Model Fairness
Real World AI
by Alyssa Simpson Rochwerger • 2021
Bring AI into a real business without bias, drift, and wasted millions: the practical playbook.
4.01
88
🏢 Enterprise Ai🎯 Ai Strategy⚖️ Responsible Ai
Real World AI Summary
Real World AI 89%
Bring AI into a real business without bias, drift, and wasted millions: the practical playbook.
by Alyssa Simpson Rochwerger 2021
4.01
88 ratings
🏢 Enterprise Ai🎯 Ai Strategy⚖️ Responsible Ai
Python for Geeks
by Muhammad Asif • 2021
Machine learning from notebook to production: Python libraries, data prep, and deployment paths.
4.50
8
🐍 Python Programming⚙️ Machine Learning Engineering📊 Data Science
Python for Geeks Summary
Python for Geeks 89%
Machine learning from notebook to production: Python libraries, data prep, and deployment paths.
by Muhammad Asif 2021
4.50
8 ratings
🐍 Python Programming⚙️ Machine Learning Engineering📊 Data Science
AI and Machine Learning for On-Device Development
by Laurence Moroney • 2021
Privacy-respecting on-device AI: face detection, text analysis, custom models. No cloud, no PhD.
4.17
6
📱 Mobile Development🧠 On-Device Ai🤖 Applied Machine Learning
AI and Machine Learning for On-Device Development Summary
AI and Machine Learning for On-Device Development 89%
Privacy-respecting on-device AI: face detection, text analysis, custom models. No cloud, no PhD.
by Laurence Moroney 2021
4.17
6 ratings
📱 Mobile Development🧠 On-Device Ai🤖 Applied Machine Learning
Low-Code AI
by Gwendolyn Stripling • 2023
Skip Python. Train regression models, engineer features, and explain predictions, all in SQL.
4.33
6
🛠️ Practical Ml📊 Data Exploration🔧 Feature Engineering
Low-Code AI Summary
Low-Code AI 89%
Skip Python. Train regression models, engineer features, and explain predictions, all in SQL.
by Gwendolyn Stripling 2023
4.33
6 ratings
🛠️ Practical Ml📊 Data Exploration🔧 Feature Engineering
Why Data Science Projects Fail
by Douglas Gray • 2024
Eighty percent of AI projects fail. The models work fine; the people, data, and expectations don't.
5.00
19
📊 Data Science Management🤖 Ai Implementation⚙️ Mlops
Why Data Science Projects Fail Summary
Why Data Science Projects Fail 89%
Eighty percent of AI projects fail. The models work fine; the people, data, and expectations don't.
by Douglas Gray 2024
5.00
19 ratings
📊 Data Science Management🤖 Ai Implementation⚙️ Mlops
Hands-On Machine Learning with Scikit-Learn and TensorFlow
by Aurélien Géron • 2017
How neural networks remember: training RNNs, LSTMs, and GRUs for tasks where order matters.
4.55
3k+
🧠 Deep Learning🔗 Neural Networks🛠️ Practical Ml
Hands-On Machine Learning with Scikit-Learn and TensorFlow Summary
Hands-On Machine Learning with Scikit-Learn and TensorFlow 89%
How neural networks remember: training RNNs, LSTMs, and GRUs for tasks where order matters.
by Aurélien Géron 2017
4.55
3k+ ratings
🧠 Deep Learning🔗 Neural Networks🛠️ Practical Ml
Machine Learning For Absolute Beginners
by Oliver Theobald • 2018
A plain English path from zero to a working model: the algorithms, data prep, and code.
3.96
786
🤖 Machine Learning Basics🐍 Python For Data Science🧹 Data Preparation
Machine Learning For Absolute Beginners Summary
Machine Learning For Absolute Beginners 89%
A plain English path from zero to a working model: the algorithms, data prep, and code.
by Oliver Theobald 2018
3.96
786 ratings
🤖 Machine Learning Basics🐍 Python For Data Science🧹 Data Preparation
Machine Learning For Absolute Beginners
by Oliver Theobald • 2017
How machine learning algorithms think, from regression to neural networks, no math required.
4.12
474
🧠 Machine Learning🧹 Data Preparation⚖️ Model Evaluation
Machine Learning For Absolute Beginners Summary
Machine Learning For Absolute Beginners 89%
How machine learning algorithms think, from regression to neural networks, no math required.
by Oliver Theobald 2017
4.12
474 ratings
🧠 Machine Learning🧹 Data Preparation⚖️ Model Evaluation
The AI Playbook
by Eric Siegel • 2024
How to get machine learning out of the lab: sell the operational improvement, not the algorithm.
3.98
127
🏢 Enterprise Technology🤖 Applied Ai🔄 Change Management
The AI Playbook Summary
The AI Playbook 88%
How to get machine learning out of the lab: sell the operational improvement, not the algorithm.
by Eric Siegel 2024
3.98
127 ratings
🏢 Enterprise Technology🤖 Applied Ai🔄 Change Management
Ansible
by Lorin Hochstein • 2014
No agents required. Automate configuration and deployment with playbooks you can actually read.
3.88
270
🔧 Configuration Management📋 Infrastructure As Code🛠️ Devops Tooling
Ansible Summary
Ansible 88%
No agents required. Automate configuration and deployment with playbooks you can actually read.
by Lorin Hochstein 2014
3.88
270 ratings
🔧 Configuration Management📋 Infrastructure As Code🛠️ Devops Tooling
The Hundred-Page Machine Learning Book
by Andriy Burkov • 2019
The one-sitting machine learning education: a hundred pages, no code, every algorithm that matters.
4.25
1k+
🤖 Machine Learning📊 Data Science📚 Classical Machine Learning
The Hundred-Page Machine Learning Book Summary
The Hundred-Page Machine Learning Book 88%
The one-sitting machine learning education: a hundred pages, no code, every algorithm that matters.
by Andriy Burkov 2019
4.25
1k+ ratings
🤖 Machine Learning📊 Data Science📚 Classical Machine Learning
The Site Reliability Workbook
by Betsy Beyer • 2018
Reliability as math: SLOs, error budgets, canarying, and the team practices that make it stick.
4.36
405
🛠️ Site Reliability Engineering🚀 Devops Practices🔥 Incident Management
The Site Reliability Workbook Summary
The Site Reliability Workbook 88%
Reliability as math: SLOs, error budgets, canarying, and the team practices that make it stick.
by Betsy Beyer 2018
4.36
405 ratings
🛠️ Site Reliability Engineering🚀 Devops Practices🔥 Incident Management
Artificial Intelligence and Machine Learning for Business
by Steven Finlay • 2018
ML projects fail for business reasons, not technical ones. The rules for getting it right.
4.14
207
🤖 Machine Learning Basics📊 Predictive Analytics👔 AI For Managers
Artificial Intelligence and Machine Learning for Business Summary
Artificial Intelligence and Machine Learning for Business 88%
ML projects fail for business reasons, not technical ones. The rules for getting it right.
by Steven Finlay 2018
4.14
207 ratings
🤖 Machine Learning Basics📊 Predictive Analytics👔 AI For Managers
Modern Software Engineering
by David Farley • 2021
Software is not a craft; it's a science. The discipline that turns teams into learning machines.
4.16
1k+
🔬 Scientific Engineering🔄 Continuous Delivery🔁 Feedback Loops
Modern Software Engineering Summary
Modern Software Engineering 88%
Software is not a craft; it's a science. The discipline that turns teams into learning machines.
by David Farley 2021
4.16
1k+ ratings
🔬 Scientific Engineering🔄 Continuous Delivery🔁 Feedback Loops
The DevOps Handbook
by Gene Kim • 2015
Speed and stability without the tradeoff. A practical handbook for unifying your delivery pipeline.
4.29
6k+
🛠️ Devops📦 Software Delivery🔄 Continuous Delivery
The DevOps Handbook Summary
The DevOps Handbook 88%
Speed and stability without the tradeoff. A practical handbook for unifying your delivery pipeline.
by Gene Kim 2015
4.29
6k+ ratings
🛠️ Devops📦 Software Delivery🔄 Continuous Delivery
Deep Learning Design Patterns
by Andrew Ferlitsch • 2021
Data shift crushed your model? Simple design patterns push accuracy from 10% to 98%.
4.67
3
🧠 Deep Learning👁️ Computer Vision🔄 Data Augmentation
Deep Learning Design Patterns Summary
Deep Learning Design Patterns 88%
Data shift crushed your model? Simple design patterns push accuracy from 10% to 98%.
by Andrew Ferlitsch 2021
4.67
3 ratings
🧠 Deep Learning👁️ Computer Vision🔄 Data Augmentation
Python Machine Learning Case Studies
by Danish Haroon • 2017
Five Python case studies in machine learning: from data cleanup to a scored model, step by step.
2.60
5
📊 Data Science🤖 Machine Learning🔍 Exploratory Data Analysis
Python Machine Learning Case Studies Summary
Python Machine Learning Case Studies 88%
Five Python case studies in machine learning: from data cleanup to a scored model, step by step.
by Danish Haroon 2017
2.60
5 ratings
📊 Data Science🤖 Machine Learning🔍 Exploratory Data Analysis
Seeking SRE
by David N. Blank-Edelman • 2018
The SRE playbook for organizations without a Google-sized reliability team.
4.17
116
🌐 Site Reliability🔄 Devops Practices⚙️ Production Engineering
Seeking SRE Summary
Seeking SRE 88%
The SRE playbook for organizations without a Google-sized reliability team.
by David N. Blank-Edelman 2018
4.17
116 ratings
🌐 Site Reliability🔄 Devops Practices⚙️ Production Engineering
Site Reliability Engineering
by Betsy Beyer • 2016
Google's operations playbook: why perfect uptime is the wrong target, and what to build instead.
4.21
3k+
🧰 Site Reliability🔄 Devops Culture🌐 Distributed Systems
Site Reliability Engineering Summary
Site Reliability Engineering 88%
Google's operations playbook: why perfect uptime is the wrong target, and what to build instead.
by Betsy Beyer 2016
4.21
3k+ ratings
🧰 Site Reliability🔄 Devops Culture🌐 Distributed Systems
Algorithms of the Intelligent Web
by Haralambos Marmanis • 2009
Search, recommend, cluster, classify: the algorithms that make web applications intelligent.
3.62
130
🤖 Machine Learning🌐 Web Applications🎯 Recommendation Systems
Algorithms of the Intelligent Web Summary
Algorithms of the Intelligent Web 88%
Search, recommend, cluster, classify: the algorithms that make web applications intelligent.
by Haralambos Marmanis 2009
3.62
130 ratings
🤖 Machine Learning🌐 Web Applications🎯 Recommendation Systems
Python Data Science Handbook
by Jake VanderPlas • 2016
Four libraries, one workflow: the Python path from raw data to validated predictions.
4.29
676
📊 Data Science🤖 Machine Learning🐍 Scientific Python
Python Data Science Handbook Summary
Python Data Science Handbook 88%
Four libraries, one workflow: the Python path from raw data to validated predictions.
by Jake VanderPlas 2016
4.29
676 ratings
📊 Data Science🤖 Machine Learning🐍 Scientific Python
What Are ChatGPT and Its Friends?
by Mike Loukides • 2023
A field guide to large language models: surprising strengths, dangerous blind spots, and real costs.
4.00
7
🧬 Generative Ai🤖 Large Language Models⚖️ Ai Ethics
What Are ChatGPT and Its Friends? Summary
What Are ChatGPT and Its Friends? 88%
A field guide to large language models: surprising strengths, dangerous blind spots, and real costs.
by Mike Loukides 2023
4.00
7 ratings
🧬 Generative Ai🤖 Large Language Models⚖️ Ai Ethics
Kubernetes in Action
by Marko Luksa • 2017
Why restart containers manually? One declarative system handles deployment, scaling, and recovery.
4.59
681
🐳 Container Orchestration☁️ Cloud Native⚙️ Infrastructure As Code
Kubernetes in Action Summary
Kubernetes in Action 88%
Why restart containers manually? One declarative system handles deployment, scaling, and recovery.
by Marko Luksa 2017
4.59
681 ratings
🐳 Container Orchestration☁️ Cloud Native⚙️ Infrastructure As Code
Introduction to Machine Learning with Python
by Andreas C. Müller • 2015
Build models that generalize, not just memorize: a Python data scientist's guide.
4.33
600
🤖 Applied Machine Learning📊 Data Science🏷️ Supervised Learning
Introduction to Machine Learning with Python Summary
Introduction to Machine Learning with Python 88%
Build models that generalize, not just memorize: a Python data scientist's guide.
by Andreas C. Müller 2015
4.33
600 ratings
🤖 Applied Machine Learning📊 Data Science🏷️ Supervised Learning
Deep Learning with Python
by François Chollet • 2017
The Keras creator teaches you to build neural networks that see, read, and create in Python.
4.57
1k+
🧠 Deep Learning🐍 Python Programming🏋️ Model Training
Deep Learning with Python Summary
Deep Learning with Python 88%
The Keras creator teaches you to build neural networks that see, read, and create in Python.
by François Chollet 2017
4.57
1k+ ratings
🧠 Deep Learning🐍 Python Programming🏋️ Model Training
Observability Engineering
by Charity Majors • 2022
Your dashboards only know yesterday's failures. Here's how to debug the ones nobody predicted.
3.72
319
🔍 Observability🏗️ Site Reliability Engineering🌐 Distributed Systems
Observability Engineering Summary
Observability Engineering 88%
Your dashboards only know yesterday's failures. Here's how to debug the ones nobody predicted.
by Charity Majors 2022
3.72
319 ratings
🔍 Observability🏗️ Site Reliability Engineering🌐 Distributed Systems
Accelerate
by Nicole Forsgren • 2018
Elite software teams ship fast and break nothing. Four metrics from years of research explain how.
4.05
8k+
⚙️ Devops👷 Engineering Management🚀 Continuous Delivery
Accelerate Summary
Accelerate 88%
Elite software teams ship fast and break nothing. Four metrics from years of research explain how.
by Nicole Forsgren 2018
4.05
8k+ ratings
⚙️ Devops👷 Engineering Management🚀 Continuous Delivery
Machine Learning
by Dr Ruchi Doshi • 2021
From training models with labeled data to finding hidden patterns: the algorithms behind modern AI.
5.00
1
🧠 Machine Learning📊 Supervised Learning🧬 Deep Learning
Machine Learning Summary
Machine Learning 88%
From training models with labeled data to finding hidden patterns: the algorithms behind modern AI.
by Dr Ruchi Doshi 2021
5.00
1 ratings
🧠 Machine Learning📊 Supervised Learning🧬 Deep Learning
Java Deep Learning Essentials
by Yusuke Sugomori • 2016
Python demos deep learning; Java deploys it. Architectures and libraries to close the gap.
3.20
5
🧠 Deep Learning☕ Java Machine Learning🔧 Practical Deep Learning
Java Deep Learning Essentials Summary
Java Deep Learning Essentials 88%
Python demos deep learning; Java deploys it. Architectures and libraries to close the gap.
by Yusuke Sugomori 2016
3.20
5 ratings
🧠 Deep Learning☕ Java Machine Learning🔧 Practical Deep Learning
AI Engineering
by Chip Huyen • 2025
Stop training models from scratch. The new discipline for building AI that ships.
4.37
1k+
🏗️ Llm Application Development📝 Prompt Engineering🔍 Rag Systems
AI Engineering Summary
AI Engineering 88%
Stop training models from scratch. The new discipline for building AI that ships.
by Chip Huyen 2025
4.37
1k+ ratings
🏗️ Llm Application Development📝 Prompt Engineering🔍 Rag Systems
Leading the Transformation
by Gary Gruver • 2015
HP halved development costs and tripled innovation. It was not the code. It was the leadership.
3.80
418
🖥️ Software Development🏢 Enterprise Agile🔄 Devops Practices
Leading the Transformation Summary
Leading the Transformation 88%
HP halved development costs and tripled innovation. It was not the code. It was the leadership.
by Gary Gruver 2015
3.80
418 ratings
🖥️ Software Development🏢 Enterprise Agile🔄 Devops Practices
Production-Ready Microservices
by Susan J. Fowler • 2016
Your microservice isn't production-ready by default. Here's the org-wide framework to change that.
3.82
460
🧱 Microservices🏗️ Software Architecture🌐 Distributed Systems
Production-Ready Microservices Summary
Production-Ready Microservices 88%
Your microservice isn't production-ready by default. Here's the org-wide framework to change that.
by Susan J. Fowler 2016
3.82
460 ratings
🧱 Microservices🏗️ Software Architecture🌐 Distributed Systems
Continuous delivery
by Jez Humble • 2010
Your releases are a nerve-wracking ordeal. The pipeline that turns them into a routine button-push.
4.20
3k+
🚚 Software Delivery🔗 Continuous Integration✅ Automated Testing
Continuous delivery Summary
Continuous delivery 88%
Your releases are a nerve-wracking ordeal. The pipeline that turns them into a routine button-push.
by Jez Humble 2010
4.20
3k+ ratings
🚚 Software Delivery🔗 Continuous Integration✅ Automated Testing
Applied Artificial Intelligence
by Mariya Yao • 2018
AI that doesn't pay for itself is overhead. The business leader's framework for picking what wins.
3.57
410
🧠 Ai Strategy🏢 Enterprise Ai⚖️ Ai Ethics
Applied Artificial Intelligence Summary
Applied Artificial Intelligence 88%
AI that doesn't pay for itself is overhead. The business leader's framework for picking what wins.
by Mariya Yao 2018
3.57
410 ratings
🧠 Ai Strategy🏢 Enterprise Ai⚖️ Ai Ethics
The Little Book of Deep Learning
by François Fleuret • 2023
The machinery of deep learning, compressed: gradient descent, backprop, and attention.
4.32
151
🧠 Deep Learning🔧 Model Architectures⚡ Hardware Acceleration
The Little Book of Deep Learning Summary
The Little Book of Deep Learning 87%
The machinery of deep learning, compressed: gradient descent, backprop, and attention.
by François Fleuret 2023
4.32
151 ratings
🧠 Deep Learning🔧 Model Architectures⚡ Hardware Acceleration
The Master Algorithm
by Pedro Domingos • 2015
Machine learning's rival schools share a hidden core. Finding it could unlock general intelligence.
3.73
6k+
🤖 Machine Learning📚 Popular Science🧠 Superintelligence
The Master Algorithm Summary
The Master Algorithm 87%
Machine learning's rival schools share a hidden core. Finding it could unlock general intelligence.
by Pedro Domingos 2015
3.73
6k+ ratings
🤖 Machine Learning📚 Popular Science🧠 Superintelligence
Everyday Chaos
by David Weinberger • 2019
A medical AI diagnoses illness but can't explain how. The age of demanding explanations is over.
3.78
202
🔬 Complexity Science🔄 Systems Thinking🤖 Machine Learning
Everyday Chaos Summary
Everyday Chaos 87%
A medical AI diagnoses illness but can't explain how. The age of demanding explanations is over.
by David Weinberger 2019
3.78
202 ratings
🔬 Complexity Science🔄 Systems Thinking🤖 Machine Learning
Practical Time Series Analysis
by Aileen Nielsen • 2019
When ARIMA beats deep learning, and when it doesn't: the pipeline from cleaning to prediction.
3.77
64
📈 Time Series Analysis🔧 Feature Engineering🧹 Data Preparation
Practical Time Series Analysis Summary
Practical Time Series Analysis 87%
When ARIMA beats deep learning, and when it doesn't: the pipeline from cleaning to prediction.
by Aileen Nielsen 2019
3.77
64 ratings
📈 Time Series Analysis🔧 Feature Engineering🧹 Data Preparation
Machine Learning for Dummies
by John Paul Mueller • 2016
Machine learning demystified: pick the right algorithm, train it well, and know when it works.
3.53
139
🧠 Machine Learning Basics📊 Data Science Basics🏷️ Supervised Learning
Machine Learning for Dummies Summary
Machine Learning for Dummies 87%
Machine learning demystified: pick the right algorithm, train it well, and know when it works.
by John Paul Mueller 2016
3.53
139 ratings
🧠 Machine Learning Basics📊 Data Science Basics🏷️ Supervised Learning
Artificial Intelligence in Practice
by Bernard Marr • 2019
Robots patrol Walmart aisles. Netflix guesses your next watch. How 50 companies already run on AI.
3.41
251
🤖 Ai In Business📋 Corporate Case Studies🧠 Applied Machine Learning
Artificial Intelligence in Practice Summary
Artificial Intelligence in Practice 87%
Robots patrol Walmart aisles. Netflix guesses your next watch. How 50 companies already run on AI.
by Bernard Marr 2019
3.41
251 ratings
🤖 Ai In Business📋 Corporate Case Studies🧠 Applied Machine Learning
Artificial Intelligence Basics
by Tom Taulli • 2019
AI without the math: what machine learning does and where automation fits in your business.
3.50
210
🧠 Ai Fundamentals🏭 Ai Adoption🔄 Digital Transformation
Artificial Intelligence Basics Summary
Artificial Intelligence Basics 87%
AI without the math: what machine learning does and where automation fits in your business.
by Tom Taulli 2019
3.50
210 ratings
🧠 Ai Fundamentals🏭 Ai Adoption🔄 Digital Transformation
AI Value Creators
by Paul Zikopoulos • 2025
Most AI adopters stay users. Value creators build on proprietary data and governed models instead.
3.66
44
🏢 Enterprise AI🔄 Digital Transformation📊 Data Strategy
AI Value Creators Summary
AI Value Creators 87%
Most AI adopters stay users. Value creators build on proprietary data and governed models instead.
by Paul Zikopoulos 2025
3.66
44 ratings
🏢 Enterprise AI🔄 Digital Transformation📊 Data Strategy
Data Science for Business
by Foster Provost • 2013
Why overfitting sinks most data projects, and the conceptual tools that stop it.
4.13
3k+
📊 Business Analytics⛏️ Data Mining📏 Model Evaluation
Data Science for Business Summary
Data Science for Business 87%
Why overfitting sinks most data projects, and the conceptual tools that stop it.
by Foster Provost 2013
4.13
3k+ ratings
📊 Business Analytics⛏️ Data Mining📏 Model Evaluation
Release It!
by Michael T. Nygard • 2007
QA says your software is ready. Production disagrees. The patterns that close the gap.
4.25
3k+
🏗️ Software Architecture🌐 Distributed Systems🛡️ Fault Tolerance
Release It! Summary
Release It! 87%
QA says your software is ready. Production disagrees. The patterns that close the gap.
by Michael T. Nygard 2007
4.25
3k+ ratings
🏗️ Software Architecture🌐 Distributed Systems🛡️ Fault Tolerance
Artificial Intelligence
by Harvard Business Review • 2019
Thirteen trillion dollars in AI-driven growth by 2030. The strategy for capturing it.
3.93
449
♟️ Ai Strategy🤖 Machine Learning🤝 Human-Ai Collaboration
Artificial Intelligence Summary
Artificial Intelligence 87%
Thirteen trillion dollars in AI-driven growth by 2030. The strategy for capturing it.
by Harvard Business Review 2019
3.93
449 ratings
♟️ Ai Strategy🤖 Machine Learning🤝 Human-Ai Collaboration
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⭐
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We have a special gift for you
Open
38% OFF
DISCOUNT FOR YOU
⭐
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⭐
$79.99
$49.99/year
only $4.16 per month
Continue
2 taps to start, super easy to cancel