# Making Sense of Machine Learning: A Review of Interpretation Techniques and Their Applications
## 5 Implementation Strategies for Explainable AI Using Cloud-Based Solutions  
While various techniques for explainable AI have been explored extensively, the successful implementation and deployment of these techniques in real-world applications require careful consideration of numerous factors. This section delves into the crucial aspects of implementing and deploying explainable AI solutions, focusing on the integration of different interpretation techniques with cloud-based solutions. By understanding the intricacies involved in the implementation process, organizations can leverage the power of explainable AI to enhance decision-making, foster trust, and ensure regulatory compliance within their respective domains.  
The implementation of XAI is significantly facilitated by the integration of various cloud-based solutions, which have emerged as pivotal platforms for deploying and managing ML models. Notably, major cloud computing providers such as Amazon, Google, Microsoft, and IBM offer comprehensive toolsets and services tailored to address the growing demand for model transparency and interpretability. This section explores how leading cloud platforms, including Amazon SageMaker, Google Cloud AI Platform, Microsoft Azure Machine Learning, and IBM Watson Studio, have incorporated dedicated features and functionalities to enable users to comprehend and elucidate the decision-making processes of complex ML models. By leveraging these cloud-based solutions, organizations can not only enhance the interpretability of their AI models but also ensure transparency, fairness, and accountability in their AI-driven applications.  
Amazon SageMaker[67] is Amazon's machine learning service that supports building, training and deploying ML models. It offers SageMaker Clarify for model explainability and interpretability, which detects and measures potential bias in ML models using a variety of metrics and enables ML developers to address potential bias and explain model predictions.  
Google Cloud AI Platformoffers model explainability features to explain the predictions of ML models. It enables users to generate feature attributions, visualise explanations and understand why a particular prediction was made. Specifically, it provides Vertex Explainable AI [68] for feature-based and example-based explanations and a What-If Tool for visually investigating model behavior.  
Microsoft Azure Machine Learning [69] provides an Azure ML Interpret package and a Responsible AI dashboard for interpreting and explaining ML models in terms of their fairness, reliability, safety, transparency and accountability. The Azure ML Interpret package provides various explainable AI methods such as SHAPs and LIMEs, as well as feature importance metrics. It is integrated with Azure Machine Learning Studio, and can also be accessed through the Python SDK for Azure Machine Learning.  
IBM Watson Studio v8.0.0 [70] is IBM's software platform for data science, which empowers data scientists and analysts to build, run and manage AI models. It brings together open-source frameworks like PyTorch, TensorFlow and scikit-learn and supports programming languages such as Python, R and Scala. Particularly to interpret ML models and make them more transparent, it offers a toolkit named AI Explainability 360 (AIX360) [71], providing various algorithms that cover different dimensions of explanations and explainability metrics.