Smart speakers, wearables, voice assistants - intelligent applications and smart devices that make our life easier and our work more efficient are increasingly becoming everyday essentials.
Powering all of this digital transformation is machine learning (ML), one of the most exciting information solutions available for data-prolific companies today. ML software has significantly optimised business infrastructure and vastly improved decision-making for multiple companies around the globe.
The International Data Corporation forecasts investment in ML will grow from US$12 billion in 2017 to $57.6 billion by 2021, largely due to emerging software solutions like Azure Machine Learning, which is enabling professionals of all skill levels to build, deploy and share advanced analytic solutions right now.
What is Machine Learning?
Imagine the typical information technology workplace and assess the difference between the humans and machines working there. Humans learn from past experiences, while the machines we use require routine programming to execute the particular tasks we want them to engage in consistently.
Machine learning, then, is when you deploy smart computer software algorithms to do the same thing as their humans users: Learn, adapt and improve future performance over time, without the need for explicit instructions like many of the ‘dumb’ machines and computers we use currently need in our daily jobs.
What and how machine learning works is most eloquently explained by Microsoft:
“Data can hold secrets, especially if you have lots of it. With lots of data about something, you can examine that data in intelligent ways to find patterns. And those patterns, which are typically too complex for you to detect yourself, can tell you how to solve a problem.
This is exactly what machine learning does: It examines large amounts of data looking for patterns, then generates code that lets you recognise those patterns in new data. Your applications can use this generated code to make better predictions. In other words, ML can help you create smarter apps.”
As autonomous self-learning software, machine learning can analyse the big data you gather in your data platform and accumulate information on a scale that surpasses human intelligence and capability. It also improves in performance the longer it is deployed, meaning the possibilities for better insights are endless.
Machine Learning adoption rate is growing
It’s safe to say machine learning as a digital transformation technology is experiencing rapid growth in acquisitions, adoption and investment in 2018. Research and piloting of ML programs is predicted by Deloitte Global to double compared to last year, and double once again by the year 2020.
Recent market forecasts and projections provide intriguing insight into the corporate rat race to establish IP and patents in the machine learning space, and demonstrate the serious piloting and research being invested into ML algorithms and programs - and why you should start paying attention.
- Deloitte Global predicts up to 800,000 machine learning custom chipsets will be used in data centers this year alone
- Research and Markets expect the global machine learning market to grow from US$1.4 billion in 2017 to US$8.81 billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1%
- MemSQL and O’Reilly Media surveyed 1,600 companies and found 61% selected ML as their company’s most important data initiative for the next year
But it’s not all just predictive analytics and surveys: Amazon is already using machine learning extensively in key areas of its business such as product recommendations and fraud detection to improve customer experiences and have documented their successes publicly.
Meanwhile, Microsoft rounds out the biggest investors in ML in its respective industry sectors, pushing business-friendly ML software solutions powered by their Azure cloud platform.
How is Azure Machine Learning different?
A number of Melbourne-based specialist service providers now offer machine learning software, but the most popular and advanced option remains in the hands of Microsoft Azure.
Azure Machine Learning (Azure ML) and its included software Azure ML Studio is a powerful cloud-based analytics tool that can enable your business to utilise machine learning capabilities in your applications and manage analytics and predictive models at scale, with many cost-saving and efficiency benefits.
Instead of having to manually manipulate and pull data from numerous sources and go through the repetitive process of revising parameters until you land the right predictive analysis model, Azure ML uses deep learning algorithms to remember previous inputs and predict more accurate data each time.
We break down six ways Azure ML can help you take advantage of everything machine learning has to offer.
1. Machine Learning as a Service
Machine learning as a service (MLaaS) refers to any service that provides machine learning solutions as part of its broader cloud computing services.
MLaaS is extremely cost effective and reliable for small and mid-sized businesses (SMB) because it handles all infrastructural concerns, like data pre-processing, model evaluation and training and predictive analysis.
Azure ML is part of Microsoft Azure public cloud platform and thus comes under the MLaaS banner, but it also brings more unique advantages of its own that further differentiates itself from other MLaaS tools:
- Azure ML API
- Hundreds of unique machine learning algorithms included
- Support for custom code
- Sample data pre-processing modules
Additionally, Azure Machine Learning is a pay for what you use service. There’s no need to buy hardware or software separately because the Azure infrastructure handles deployment and maintenance of all your ML-powered applications, so you can just simply buy and use what you need.
2. Azure ML Studio makes machine learning accessible
Azure Machine Learning Studio is an integrated development product included with Azure ML and one of the most user-friendly machine learning development solutions on the market.
With this tool, you can build entire data models, ML algorithms, pre-processing modules and other components through drag and drop gestures on an interactive design surface. You can then run training experiments, examine the final results, and link them together graphically.
Once the desired ML model is created using Azure ML Studio, you can deploy their algorithms and predictive models as a web service or to Microsoft Azure directly and allow other users and applications on the cloud to use it seamlessly, without reliance on manual or ad-hoc techniques.
The big advantage of Azure ML Studio over other solutions is there’s nearly zero programming knowledge required. Its streamlined mind map format means you just have to grasp how to visually connect datasets and modules together to construct your desired predictive analysis model.
The workspace environment also includes a number of sample experiments which you can use as a helpful starting point. On the flip-side, advanced users with coding knowledge will appreciate the support for R and the included 350 R packages supported by ML Studio.
3. Cross-platform support for machine learning anywhere
Azure ML’s existence as a service on the cloud means you can access and test your ML algorithms from all over the world across all of your company’s connected devices, with support for a range of programming languages, operating systems and databases.
Just like other business application on the cloud, all you need is a secure Internet connection. Then, you can load up your Web browser and access, share and test your ML models without having to be tied to the office or at a computer desk.
4. Extensive algorithm support
The most common machine learning algorithms are fully supported by Azure ML, such as decision tree, linear regression and logic regression, making it easy to implement forecasts and real-time predictions into your experiments and final modelling.
You don’t have to be an expert in ML algorithms to progress; you just need to know what type you need and when to use them in your models. It’s also as easy as dragging and dropping the type you desire onto the simplified graphical interface - there’s no need for hand calculations.
Azure ML’s accessibility is similar to Microsoft’s Power BI, which also simplifies business intelligence modelling for professionals using visualisations. By lowering coding requirements and streamlining the configuration of algorithm properties, it can save time for testing and deployment of ML solutions.
5. Flexible pricing structure
Microsoft offer a basic version of Azure Machine Learning and the included Azure ML Studio for free, and all you need to do is sign up for a Microsoft account if you haven’t already.
The basic version includes free access to Azure ML without an expiration date or the need for a credit card or Azure Subscription. It also comes with with 10GB in storage, 100 modules per experiment, predictive web services and R and Python support, which is more than enough for businesses seeking to dip their toe in machine learning first to learn what it’s capable of before committing to a larger investment.
The standard enterprise grade work-space is currently priced at US$9.90/month and naturally comes with a Azure subscription and additional support and services, including more storage and modules for more ambitious ML experiments and advanced use.
6. Fast and easy web service deployment
Once you have finalised your ML training experiment to be ready for testing, Azure ML Studio can set up and deploy your predictive models as a web service front end in mere minutes.
Any users with with the right authorisation key can securely access the deployed web service on Azure to then send the ML model new data to get predictions and retrieve back results.
The key differentiation here is that while most other web services need a developer to make the machine learning experiment operational, Azure Machine Learning handles the entire process for you. It can also deploy experiments faster than those using traditional analytics tools can.
Where to find support for Azure Machine Learning?
Managing big data and building machine learning solutions is as approachable and simplified as it can get with Azure Machine Learning, but naturally not everyone will be able to pick it up.
Despite its user-friendly approach, Azure ML does demand a fundamental knowledge of ML, how predictive algorithms work, and the features and capabilities of Microsoft cloud data platform solutions in order to get the most out of the service. The official Microsoft website provides extensive documentation on how and why you should get started if you’re seeking additional answers on Azure ML.