Empowering data analytics is one of the mainstays of modern Cloud Computing. Approaches that may themselves be tried and tested are applied at internet scale with the co-ordinated, distributed computation of hundreds or thousands of machines over terabytes or petabytes of data, leading to new challenges and innovations. These scale capabilities provided by leading cloud providers make them the ideal place to watch for trends in the technology.
In 2019 we have seen a significant trend towards increased ease of operations, productivity and experimentation of data on Azure, with new tools and service offerings across many product lines. In this article we’ll highlight some of the key innovations to illustrate these trends.
Machine Learning has emerged as a mainstream area of software, with product owners in many companies looking to improve user experience through its application. Typically, companies have struggled to adopt Machine Learning because its process hasn’t been well understood and it has been seen as alien to existing software development practices.
So far in 2019, there has been a trend towards the normalisation of Machine Learning as a process of software creation and an integration of Data Scientists - those skilled statisticians and data modellers who create Machine Learning models - into standard and common software development lifecycles. This trend is given the term “MLOps” and describes how to apply DevOps for Machine Learning. We see this trend as incredibly important, as it removes the biggest barrier to success for models that make the quality threshold – deployment in an enterprise setting.
In a nutshell, MLOps includes management tools for Code, Data Set, Environment, Deployment and Monitoring and help DevOps professionals to work alongside Data Scientists to bring rigor and stability to Machine Learning platforms. One major aspect of this is the Azure Machine Learning service CLI, which provides command-line access to create and manage Machine Learning activities, from creating environments to executing code. An extension to the standard Azure ‘az’ CLI, the Machine Learning service CLI is well suited to DevOps workloads and can be automated using Azure Pipelines.
In this simple example, we can create the necessary Azure compute resources to train a ML model:
az ml computetarget create amlcompute -n cpu --min-nodes 1 --max-nodes 1 -s STANDARD_D3_V2
These new MLOps features in the Azure Machine Learning service aim to enable users to bring their ML scenarios to production by supporting reproducibility, auditability, and automation of the end-to-end ML lifecycle.
Jordan Edwards Senior Program Manager, Azure MLOps
For Data Scientists themselves, significant strides have been made in the auditing and ‘bookkeeping’ of the process of Data Science. Data Scientists will routinely create a number of models in the process of tuning, using different algorithms and hyperparameters to those algorithms. I have seen many approaches to managing these models, from Excel spreadsheets with catalogues of results to huge python scripts with comments holding the point in time results. Ultimately all of these become unwieldy and promising avenues of research as missed in the noise of results, models are executed multiple times unnecessarily and the process becomes personalised to the method used by the experimenting data scientist, reducing collaboration potential across a team.
In a new development, Microsoft Azure’s data teams have been working to integrate with the open source project MLflow; software that provides an innovative solution to this challenge. Whilst MLFlow remains cloud agnostic, the Azure teams provide model tracking and deployment interface that can target multiple services on Azure; Databricks, Azure Machine Learning Compute, Virtual Machines and the container services ACI and AKS.
There was a great session that introduces this concept recorded at the Spark Summit and available online here: https://databricks.com/session/accelerating-the-machine-learning-lifecycle-with-mlflow-1-0
Read more here by the great Roope Astara : https://azure.microsoft.com/en-gb/blog/make-your-data-science-workflow-efficient-and-reproducible-with-mlflow/
The most creative part of solving data science challenges is the experimentation, the rapid application of algorithms to solve data challenges. A lot of Data Scientists approach this on their local machines because getting cloud based resources from the IT team is difficult; there is often a disconnection between the knowledge sets of the Data Science team and the IT Cloud Services team – they are not used to speaking the same language and this causes friction. Whilst using local machines is a valid starting point for many situations, sooner rather than later the Data Scientists will want to look at real data, and this brings the Data Scientists into conflict with organisational compliance standards; how can a Data Scientist guarantee that their local machine is secure? In the age of BYOD and portable compute, valid concerns emerge regarding information security.
This battle between Data Scientist, Cloud Services provision and Compliance officers can be bypassed by using templated cloud resources that meet enterprise compliance standards and are provisioned at the click of a button and managed inside the Azure Portal. These are called Notebook VMs and include a system that builds a new environment drawn from the bloodline of the Data Science Virtual Machines (DSVMs) so are configured ready to go for a Data Scientist (GPU and all!), but are wrapped inside the Azure Machine Learning service so they can share the data stores and compute resources of the Workspace that they are created inside.
Long time Azure Data maestro Maxim Lukiyanov (I first worked with him in 2014 on the trailblazing C# Streaming Map Reduce SDK!) introduces us to this concept in a great blog post:
Data scientists have a dynamic role. They need environments that are fast and flexible while upholding their organization’s security and compliance policies.
Maxim Lukiyanov Principal Program Manager, Azure Machine Learning