Future trends in Spark ML and distributed ML
As the field of ML continues to evolve, there are several future trends and advancements that we can expect in Spark ML and distributed ML. Here are a few key areas to watch:
- Deep learning integration: Spark ML is likely to see deeper integration with deep learning frameworks such as TensorFlow and PyTorch. This will enable users to seamlessly incorporate deep learning models into their Spark ML pipelines, unlocking the power of neural networks for complex tasks such as image recognition and natural language processing.
- Automated ML: Automation will play a significant role in simplifying and accelerating the machine learning process. We can anticipate advancements in automated feature engineering, hyperparameter tuning, and model selection techniques within Spark ML. These advancements will make it easier for users to build high-performing models with minimal manual effort.
- Explainable AI: As the demand for transparency and...
