One possible approach is to use Azure AutoML to automatically evaluate and tune multiple machine learning models on our data. By feeding it with our training data and target variable, AutoML will iteratively explore different algorithms and hyperparameter combinations, allowing us to find the most accurate model. It saves us time and effort by automating the selection and optimization process.

In addition to using AutoML directly, we can also take advantage of its ability to interpret and explain models. The feature not only provides the predicted outcomes but also offers insights into the most important features and their impact on the predictions. This can help us improve model interpretability and gain a deeper understanding of the underlying patterns in our data.

Another approach to enhance predictive modeling accuracy is to leverage Azure's Machine Learning service to build custom machine learning pipelines. This gives us greater control over the entire modeling process, including feature engineering, algorithm selection, and model evaluation. By using the various components provided by Azure ML, we can experiment with different algorithms, hyperparameters, and feature combinations to optimize our models for accuracy.

An alternative solution would be to utilize Azure Databricks in combination with AutoML. Databricks provides a collaborative environment for data science teams to run large-scale data processing and modeling tasks. By integrating AutoML with Databricks, we can leverage the distributed computing capabilities and Spark-based processing power to accelerate the model training process and handle larger datasets more efficiently.

While AutoML is a powerful tool, it's essential to note that it's not a silver bullet. We should evaluate the impact of using AutoML in terms of model complexity, explainability, and performance requirements. In some cases, a more manual approach may be necessary to fine-tune specific algorithms or include domain-specific knowledge. A combination of AutoML and manual intervention can lead to the best results.