title: specify an environment in an Inference config
body: <p>I am having an issue deploying a model using a custom docker image.
      The deployment is failing because the packages needed are not in the “default” environment and I need to specify a custom one. ("/miniconda/envs/py37/bin/python")</p>
  
  <pre><code>I am using the same image to run the training, and with the estimator class I do have a way to specify the environment. Is it possible to something similar with the InferenceConfig?
  
      est = Estimator(source_directory=script_folder,
                      script_params=script_params,
                      inputs=inputs,
                      compute_target=gpu_compute_target,
                      entry_script='train.py',
                      image_registry_details = my_registry,
                      custom_docker_image='omr:latest',
                      use_gpu=True,
                      user_managed=True)
      est.run_config.environment.python.user_managed_dependencies = True
      est.run_config.environment.python.interpreter_path = "/miniconda/envs/py37/bin/python"
  
  
  
      #establish container configuration using custom base image
      privateRegistry = aml_utils.getContainerRegistryDetails()
      inference_config = InferenceConfig(source_directory="./detect",
                                         runtime= "python", 
                                         entry_script="aml_score.py",
                                         enable_gpu=False,
                                         base_image="amlworkspaceom3760145996.azurecr.io/omr:latest",
                                         base_image_registry = privateRegistry)
  
      #deploy model via AKS deployment target
      deployment_config = AksWebservice.deploy_configuration(gpu_cores = 1, memory_gb = 1, auth_enabled = False)
      targetcluster = aml_utils.getGpuDeploymentTarget(ws)
  
      model_service = Model.deploy(workspace = ws,
                                 name = "model_name,
                                 models = [model],
                                 inference_config = inference_config, 
                                 deployment_config = deployment_config,
                                 deployment_target = targetcluster)
  </code></pre>