Latest Azure ML SDK in the Docker image (microsoft#235)

upgrade to  latest aml sdk for build agent

Add an env var flag to recreate AMLS Environment (microsoft#230)

Added get_latest_model method (microsoft#231)

added abtest.yml to bootstrapping (microsoft#233)

Improve Canary guide and add prerequisites (microsoft#225)

Canary pipeline fixes (microsoft#224)

* Add vars template to canary pipeline

* Enable ACR authentication on AKS using a service principal
- Upgrade helm version to 3.1.1
- Remove ACR secret from the abtest-model deployment

rename config.json to parameters.json (microsoft#223)

AML workspace configuration files are named"config.json" by default, so renaming the file containing training parameters to "parameters.json" to avoid confusion.

README refactor (microsoft#220)

* Minor README fixes
- Whitespace fixes
- Grammar fixes
- Ported some wording from Getting Started

* Factor out architecture details to dedicated architecture article
We don't need to go through the architecture in detail here. There's an
entire article dedicated to it. A separate change will be needed for the
article.

* Remove architecture diagram from README

fix import (microsoft#222)

Fix of importing Dataset, Datastore, Workspace

Proposal: split train.py into train.py and train_aml.py (microsoft#219)

This change splits train.py into two files.

The new train.py is standalone, and has no references to AzureML. It defines three functions, split_data to split a dataframe into test/train data, and train_model which takes the test/train data and a parameter object and trains the model, and get_model_metrics, which evaluates metrics about the model. The script can be run locally, in which case it loads a dataset from a file.

The second file, train_aml.py contains reasonably general AzureML logic. It reads data from a dataset, then calls the split_data function from train.py. It loads input parameters from a config file and logs them, then calls train_model from train.py. It then uploads the model and logs any metrics returned by get_model_metrics.

The hope with these changes is to demonstrate a simple interface for integrating an existing ML script with MLOpsPython, as well as providing an example for how the core ML functionality can be invoked in multiple ways for development purposes.

Co-authored-by: Bryan J Smith <bjcmit@hotmail.com>