Today, artificial intelligence-powered machine learning (ML) and data analytics solutions are high in demand by companies in almost every area, whether it’s the financial sector, power industry, retail, healthcare, technology, or telecommunications. ML allows companies to work through a massive amount of raw data to extract actionable information. It equips businesses to understand their targeted audience better, automate their operations and production, align customer demand, and predict future business development with reliable results to make conversant decisions.

However, implementing ML technologies and algorithms like decision trees, logistic/linear regression, KNN, etc., remained a big challenge for businesses. Given that it is a costly affair with elaborate infrastructures, subject matter experts, high computing and processing power systems, etc., to leverage ML technologies and solutions in the business infrastructure. 

But with "Intelligent Cloud" – ML assimilation in the Cloud, the public cloud providers offer several ML services and make this technology more affordable for SMEs that can't meet the expense of building, testing, and implementing their algorithms from scratch. ML integration is no longer out of the reach of small and medium-sized enterprise budgets.

What is Intelligent Cloud or ML as a Service (MLaaS)

MLaaS is an umbrella term used for cloud platforms that cover ML infrastructure issues such as data pre-processing, model training, and evaluation with supplementary prediction connected with the company's internal IT set-up through REST APIs. Intelligent Cloud facilitates ML applications by providing massive processing power to analyze the hefty amount of data through computing, networking, and storage resources, resulting in overall low operational cost and consistent business scalability.

The four leading cloud MLaaS service providers include Amazon ML services, Azure ML, Google Cloud AI, and IBM Watson. These providers are on a mission to enable swift model training and deployment for businesses to yield valuable insights from predictions with even small teams in place.

As AWS CEO Andy Jassy said in his re:invent keynote, "his company has to solve the problem of accessibility of everyday developers and scientists to enable AI and ML in the enterprise." And true to the claim, today, businesses are actively looking to build ML working models to get better value to their enterprises by using ML cloud services.

So, what are the best ML platforms on the market that can help your business infrastructural decisions? Let's take a look.

Best Cloud Computing Platforms for ML

The Cloud is a starting and endpoint for your ML projects, but how do you determine which platform is the right fit for you? Here is an overview and service comparison of the leading MLaaS platforms, including Amazon, Google, Microsoft, and IBM that support wide-ranging algorithms, various types of regression, classification, anomaly detection, etc.

Figure 1 Source: AltexSoft

Note: This overview is only intended to instruct on what to look for in Cloud ML providers.

1. Amazon Web Services 

AWS is currently one of the most popular cloud computing platforms for ML. AWS provides various products for ML like:

• Amazon SageMaker – to create and train ML models.
• Amazon Augmented AI – to implement a human review of the ML models.
• Amazon Forecast – to increase the forecast accuracy
• Amazon Translate – to translate language through ML & natural language processing.
• Amazon Personalize – to build personal recommendations in ML systems
• AWS Deep Learning AMI's – to create Deep Learning solutions.
• Amazon Polly – to convert text into life-like speech.

2. Microsoft Azure 

Azure was released in 2010 to become the top cloud computing platform for ML and data analytics. Some of the Microsoft Azure products for ML are:

• Microsoft Azure Cognitive Service – to lend smart cognitive services to applications.
• Microsoft Azure Azure Databricks – to run Apache Spark-based analytics.
• Microsoft Azure Bot Service – to give intelligent and scalable bot services.
• Microsoft Azure Cognitive Search – ML service for mobile/web applications.
• Microsoft Azure ML – to build and deploy ML models in the Cloud.

3. The Google Cloud Platform (GCP) 

GCP provides a similar infrastructure for companies as is used in its internal products. The ML products provided by GCP are:

• Google Cloud AutoML – to develop and train an AutoML model.
• Google Cloud AI Platform – to build, train, and manage various ML models.
• Google Cloud Speech-to-Text – to translate speech into text with 120 languages support.
• Google Cloud Vision AI – to build ML models for text detection.
• Google Cloud Text-to-Speech – to transmit text to speech.
• Google Cloud Natural Language – to process natural language processing for text analysis and classification.

4. The IBM Cloud 

IBM Cloud provides public, private, and hybrid cloud delivery models with ML products including:

• IBM Watson Studio – to build ML/AI models and analyze data.
• IBM Watson Speech-to-Text – to convert audio into written text.
• IBM Watson Text-to-Speech – to convert text into natural-sound audio.
• IBM Watson Natural Language Understanding – to process natural language for text analysis and classification.
• IBM Watson Visual Recognition – to visual image search and classification.
• IBM Watson Assistant – to create and manage virtual assistants. 

Benefits of Machine Learning in the Cloud

ML technology is a dream solution for companies to automate their business operations, reduce overall cost, and upturn profits. However, many barriers are adding up to labor, structure, and development overheads when adopting ML capabilities to enterprise applications. The most common hurdles include expertise to build, train, and deploy ML models, costly computational and special-purpose hardware, intricate infrastructure, and a specialized workforce. 

ML hardware is one of the most important considerations due to the model training being an extremely compute-intensive task. ML requires parallel computing resources to speed up the traditional CPU-based processors. 

But with the public cloud development of Intelligent Cloud or MLaaS, it is now feasible for enterprises to experiment and leverage ML capabilities. Be it AWS, Microsoft Azure, Google Cloud Platform, or IBM, all these cloud providers offer businesses many options to apply intelligent features in enterprise applications for their maximum benefit. 

Here are some of the core benefits of ML in the Cloud:

Cost Efficiency

• Cloud providers offer virtual servers with Powerful graphics processing units (GPUs) that completely cut hardware investment.
• The Cloud's pay-per-use model is ideal for distributed ML workloads because it requires you to pay only a few dollars an hour to enterprise machine-learning applications.
• Cloud provides cheap data storage where you can utilize real-time databases. 

No Expertise Required

• Cloud makes specialized skills and intelligent capabilities accessible without requiring you to have advanced AI or data science knowledge.
• All cloud providers offer SDKs (software developer kits), programming support, and APIs to embed machine-learning functionalities into applications directly. 

Easy Scale-up

• MLaaS enables enterprises to experiment with ML capabilities and scale up on the go with production demand.
• With the Intelligent cloud, businesses can access complex ML capabilities without buying and deploying unfamiliar hardware to test and implement small projects.

Conclusion

MLaaS platforms' demand is consistently increasing as businesses recognize the potential and benefits of ML in the Cloud. Not only does Cloud expand the horizon for ML applications but ML itself makes cloud computing more efficient and scalable. That makes Cloud and ML interrelated to be used in a symbiotic manner for tremendous business growth.

If correctly applied, enterprises looking for ML applications may find that Intelligent Cloud integration provides real value for their business without any major investment.