Fix broken links in serving documentation

tensorflow_serving/g3doc/api_rest.md

@@ -323,7 +323,7 @@ data between systems. For supported types, the encodings are described on a
 type-by-type basis in the table below. Types not listed below are implied to be
 unsupported.
 
-[TF Data Type](https://www.tensorflow.org/versions/r1.1/programmers_guide/dims_types#data_types) | [JSON Value](http://json.org/) | JSON example                       | Notes
+[TF Data Type](https://www.tensorflow.org/api_docs/python/tf/dtypes/DType) | [JSON Value](http://json.org/) | JSON example                       | Notes
 ------------------------------------------------------------------------------------------------ | ------------------------------ | ---------------------------------- | -----
 DT_BOOL                                                                                          | true, false                    | *true, false*                      |
 DT_STRING                                                                                        | string                         | *"Hello World!"*                   | If `DT_STRING` represents binary bytes (e.g. serialized image bytes or protobuf), encode these in Base64. See [Encoding binary values](#encoding-binary-values) for more info.

tensorflow_serving/g3doc/docker.md

@@ -33,7 +33,8 @@ links here:
 *   [Docker for macOS](https://docs.docker.com/docker-for-mac/install/)
 *   [Docker for Windows](https://docs.docker.com/docker-for-windows/install/)
     for Windows 10 Pro or later
-*   [Docker Toolbox](https://docs.docker.com/toolbox/) for much older versions
+*   [Docker Toolbox](https://docs.docker.com/retired/#docker-toolbox)
+    for much older versions
     of macOS, or versions of Windows before Windows 10 Pro
 
 ## Serving with Docker

tensorflow_serving/g3doc/serving_advanced.md

@@ -9,7 +9,7 @@ server to serve your models, see
 This tutorial uses the simple Softmax Regression model introduced in the
 TensorFlow tutorial for handwritten image (MNIST data) classification. If you
 don't know what TensorFlow or MNIST is, see the
-[MNIST For ML Beginners](http://www.tensorflow.org/tutorials/mnist/beginners/index.html#mnist-for-ml-beginners)
+[TensorFlow 2 quickstart for beginners](https://www.tensorflow.org/tutorials/quickstart/beginner)
 tutorial.
 
 The code for this tutorial consists of two parts:

tensorflow_serving/g3doc/serving_kubernetes.md

@@ -203,7 +203,7 @@ resnet-serving-cluster  us-central1-f  1.1.8           104.197.163.119  n1-stand
 ```
 
 Set the default cluster for gcloud container command and pass cluster
-credentials to [kubectl](http://kubernetes.io/docs/user-guide/kubectl-overview/).
+credentials to [kubectl](https://kubernetes.io/docs/concepts/overview/kubectl/).
 
 ```shell
 gcloud config set container/cluster resnet-serving-cluster
@@ -245,11 +245,11 @@ docker push gcr.io/tensorflow-serving/resnet
 ### Create Kubernetes Deployment and Service
 
 The deployment consists of 3 replicas of `resnet_inference` server controlled by
-a [Kubernetes Deployment](http://kubernetes.io/docs/user-guide/deployments/).
+a [Kubernetes Deployment](https://kubernetes.io/docs/concepts/workloads/controllers/deployment/).
 The replicas are exposed externally by a
-[Kubernetes Service](http://kubernetes.io/docs/user-guide/services/) along with
+[Kubernetes Service](https://kubernetes.io/docs/concepts/services-networking/service/) along with
 an
-[External Load Balancer](http://kubernetes.io/docs/user-guide/load-balancer/).
+[External Load Balancer](https://kubernetes.io/docs/concepts/services-networking/service/#loadbalancer).
 
 We create them using the example Kubernetes config
 [resnet_k8s.yaml](https://github.com/tensorflow/serving/tree/master/tensorflow_serving/example/resnet_k8s.yaml).