Remove MLDataset document in README

README.md

@@ -1,6 +1,6 @@
 # RayDP
 
-RayDP is a distributed data processing library that provides simple APIs for running Spark on [Ray](https://github.com/ray-project/ray) and integrating Spark with distributed deep learning and machine learning frameworks. RayDP makes it simple to build distributed end-to-end data analytics and AI pipeline. Instead of using lots of glue code or an orchestration framework to stitch multiple distributed programs, RayDP allows you to write Spark, PyTorch, Tensorflow, XGBoost code in a single python program with increased productivity and performance. You can build an end-to-end pipeline on a single Ray cluster by using Spark for data preprocessing, RaySGD or Horovod for distributed deep learning, RayTune for hyperparameter tuning and RayServe for model serving.
+RayDP is a distributed data processing library that provides simple APIs for running Spark on [Ray](https://github.com/ray-project/ray) and integrating Spark with distributed deep learning and machine learning frameworks. RayDP makes it simple to build distributed end-to-end data analytics and AI pipeline. Instead of using lots of glue code or an orchestration framework to stitch multiple distributed programs, RayDP allows you to write Spark, PyTorch, Tensorflow, XGBoost code in a single python program with increased productivity and performance. You can build an end-to-end pipeline on a single Ray cluster by using Spark for data preprocessing, Ray Train or Horovod for distributed deep learning, RayTune for hyperparameter tuning and RayServe for model serving.
 
 ## Installation
 
@@ -85,7 +85,7 @@ raydp.stop_spark()
 
 ## Machine Learning and Deep Learning With a Spark DataFrame
 
-RayDP provides APIs for converting a Spark DataFrame to a Ray Dataset or Ray MLDataset which can be consumed by XGBoost, RaySGD or Horovod on Ray. RayDP also provides high level scikit-learn style Estimator APIs for distributed training with PyTorch or Tensorflow.
+RayDP provides APIs for converting a Spark DataFrame to a Ray Dataset which can be consumed by XGBoost, Ray Train or Horovod on Ray. RayDP also provides high level scikit-learn style Estimator APIs for distributed training with PyTorch or Tensorflow.
 
 
 ***Spark DataFrame <=> Ray Dataset***
@@ -109,29 +109,9 @@ df2 = ds2.to_spark(spark)
 ```
 Please refer to [Spark+XGBoost on Ray](./examples/xgboost_ray_nyctaxi.py) for a full example.
 
-***Spark DataFrame => Ray MLDataset***
-
-RayDP provides an API for creating a Ray MLDataset from a Spark dataframe. MLDataset can be converted to a PyTorch or Tensorflow dataset for distributed training with Horovod on Ray or RaySGD. MLDataset is also supported by XGBoost on Ray as a data source.
-
-```python
-import ray
-import raydp
-from raydp.spark import RayMLDataset
-
-ray.init()
-spark = raydp.init_spark(app_name="RayDP Example",
-                         num_executors=2,
-                         executor_cores=2,
-                         executor_memory="4GB")
-
-df = spark.range(0, 1000)
-ds = RayMLDataset.from_spark(df, num_shards=10)
-```
-Please refer to [Spark+Horovod on Ray](./examples/horovod_nyctaxi.py) for a full example.
-
 ***Estimator API***
 
-The Estimator APIs allow you to train a deep neural network directly on a Spark DataFrame, leveraging Ray’s ability to scale out across the cluster. The Estimator APIs are wrappers of RaySGD and hide the complexity of converting a Spark DataFrame to a PyTorch/Tensorflow dataset and distributing the training. RayDP provides `raydp.torch.TorchEstimator` for PyTorch and `raydp.tf.TFEstimator` for Tensorflow. The following is an example of using TorchEstimator.
+The Estimator APIs allow you to train a deep neural network directly on a Spark DataFrame, leveraging Ray’s ability to scale out across the cluster. The Estimator APIs are wrappers of Ray Train and hide the complexity of converting a Spark DataFrame to a PyTorch/Tensorflow dataset and distributing the training. RayDP provides `raydp.torch.TorchEstimator` for PyTorch and `raydp.tf.TFEstimator` for Tensorflow. The following is an example of using TorchEstimator.
 
 ```python
 import ray