Overcoming Machine Learning (ML) Obstacles
Unleash Machine Learning capabilities throughout your organization with Azure’s ML solutions.
It’s no mystery that data science and machine learning have been around for quite some time. It begs the question, however, why this technology isn’t being used more broadly across organizations today? The answer to this question really boils down to two hurdles that have stymied this, and many other advanced technologies: expertise and money.
In terms of expense – there aren’t many in a data leadership position that could easily justify the expense of hundreds of thousands or even millions of dollars to realize a fully-functioning machine learning solution. For those that were able to rationalize the cost of implementing a solution will produce a positive ROI – there is still the question of ‘how and where do we get started?’
There’s no question that the data management side of Machine Learning can be highly cumbersome. Today, we typically see only a few companies that are actually doing data science work, but many of them are doing it only within individual departments, like finance, operations, or marketing. That means they only have access to siloed data and no connection to the data sources they need in order to get insight into the greater organization. Even if they have access to organization-wide data, they are still often working in a vacuum when it comes to implementation.
If organizations are able to solve all of these challenges, they then often reach a roadblock when it comes time to actually put their model into production. At this stage, models can either become stale because it took so long to develop them or they can grow old in production because the process to get them there was too time consuming to repeat.
Challenges to Machine Learning
Why aren’t more organizations implementing Machine Learning models?
Azure Machine Learning
To help combat some of these common challenges, Microsoft’s Azure Machine Learning solution offers its users an enterprise-grade service to effectively implement and manage a complete end-to-end Machine Learning lifecycle. Azure Machine Learning empowers data scientists and developers to build, deploy, and manage high-quality models faster and with confidence. It accelerates time to value with industry-leading machine learning operations (MLOps), open-source interoperability, and integrated tools. This trusted platform is designed for responsible AI applications in machine learning.
Azure Machine Learning Studio
At the heart of the Azure ML solution is Azure ML Studio. This is a browser-based integrated development environment (IDE) that uses drag-and-drop gestures and simple data flow graphs to let users, that are not expert data scientists, set up ML modeling experiments. For many tasks, users will not have to write a single line of code. ML Studio also features a library of time-saving sample experiments and sophisticated algorithms that have been developed by Microsoft Research, including the same algorithms Microsoft is using right now to run their Bing and Xbox cloud services.
Azure ML Studio
While keeping the novice users in mind, Azure ML Studio also pays attention to the needs of more advanced users, like data scientists. It features a number of modules with which to build comprehensive predictive models, including state of the art ML algorithms such as scalable boosted decision trees, Bayesian Recommendation systems, Deep Neural Networks, and Decision Jungles developed at Microsoft Research.
Azure ML Studio is also critically important when it comes to deploying ML models and experiments. Using the Azure ML API, which, Azure ML Studio can deploy ML web services as an integrated part of other analytical software and dashboards as well as making such services and algorithms available on the Azure Marketplace with just a few clicks. This is important because it lets data scientist customers monetize their experience immediately by allowing them to quickly expose their intellectual property to a broad swath of potential customers.
Browser-based environment supporting general users and data scientists
Immutable library of models including search, discover, and reuse
Wide range of features, machine learning algorithms, and modeling strategies
Ability to quickly deploy models as Azure web services to the ML API service
Azure Machine Learning API
What gives the Azure ML solution so much flexibility is largely due to the Azure ML API. This API allows customers to build powerful ML solutions, customize Azure ML Studio to their particular needs, and integrate Azure ML into other data analysis solutions and software.
And just like Azure ML Studio, the Azure ML API is accessible by users who are not sophisticated when it comes to advanced data analytics, but it also supports the needs of those who are. By enabling the API as a REST-based web service, Microsoft has made using it to run and publish models very easy. But by including richer functionality, including a rules engine, R support with hundreds of included packages, an optimizer, and simulation tools, they have also given it enough depth to address even the most advanced scenarios.
If you’d like help getting started with Machine Learning, the Oakwood Team can help you accelerate your time to value with implementation of your own unique model(s). If you’ve already begun your journey, we can further assist with reinforcement learning, registries, experimentation, security, cost management, auto-scaling compute and more.
Please leave us a note below and let’s have a conversation today!
