AI/ML

You don’t need to use a cloud provider to build a machine learning solution. After all, there are plenty of open source machine learning frameworks, such as TensorFlow, MXNet, and CNTK that companies can run on their own hardware. However, companies building sophisticated machine learning models in-house are likely to run into issues scaling their workloads, because training real-world models typically requires large compute clusters.

The barriers to entry for bringing machine learning capabilities to enterprise applications are high on many fronts. The specialized skills required to build, train, and deploy machine learning models and the computational and special-purpose hardware requirements add up to higher costs for labor, development, and infrastructure. These are problems that cloud computing can solve and the leading public cloud platforms are on a mission to make it easier for companies to leverage machine learning capabilities to solve business problems without the full tech burden. As AWS CEO Andy Jassy highlighted in his 2017 re:Invent keynote, his company has to “solve the problem of accessibility of everyday developers and scientists” to enable AI and machine learning in the enterprise.

There are many good reasons for moving some, or all, of your machine learning projects to the cloud. The cloud’s pay-per-use model is good for bursty AI or machine learning workloads, and you can leverage the speed and power of GPUs for training without the hardware investment. The cloud also makes it easy for enterprises to experiment with machine learning capabilities and scale up as projects go into production and demand for those features increases. Perhaps even more importantly, the cloud makes intelligent capabilities accessible without requiring advanced skills in artificial intelligence or data science—skills that are rare and in short supply. A survey by Tech Pro Research found that just 28% of companies have some experience with AI or machine learning, and 42% said their enterprise IT personnel don’t have the skills required to implement and support AI and machine learning.

Benefits of Machine Learning in the Cloud

  • The cloud’s pay-per-use model is good for bursty AI or machine learning workloads.

  • The cloud makes it easy for enterprises to experiment with machine learning capabilities and scale up as projects go into production and demand increases.

  • The cloud makes intelligent capabilities accessible without requiring advanced skills in artificial intelligence or data science.

  • AWS, Microsoft Azure, and Google Cloud Platform offer many machine learning options that don’t require deep knowledge of AI, machine learning theory, or a team of data scientists.


AWS, Microsoft Azure, and Google Cloud Platform offer many options for implementing intelligent features in enterprise applications that don’t require deep knowledge of AI or machine learning theory or a team of data scientists.

Benefits of AI in Cloud Computing

AI has changed the cloud landscape in following ways:

  • Lower Costs

    A big advantage of cloud computing is that it eliminates costs related to on-site data centers, such as hardware and maintenance. Those upfront costs can be prohibitive with AI projects, but in the cloud enterprises can instantly access these tools for a monthly fee, making research- and development-related costs more manageable. Additionally, AI tools can gain insights from the data and analyze it without human intervention.

  • Intelligent Automation

    Enterprises use the power of AI-driven cloud computing to be more efficient, strategic and insight-driven. AI can automate complex and repetitive tasks to boost productivity, as well as perform data analysis without any human intervention. IT teams can also use AI to manage and monitor core workflows. IT teams can focus more on strategic operations while AI performs the mundane tasks. For example, IBM Cloud Pak for Automation provides prebuilt workflows for AI-powered automation.

  • Deeper Insights

    AI can identify patterns and trends in vast data sets. It uses historical data and compares it to the most recent data, which provides IT teams with well-informed, data-backed intelligence. On top of that, AI tools can perform data analysis fast so enterprises can rapidly and efficiently address customer queries and issues. The observations and valuable advice gained from AI capabilities result in quicker and more accurate results. For example, an app developer can use Amazon Personalize to give customers real-time personalized recommendations.

  • Improved Data Management

    AI plays a significant role in processing, managing and structuring data. AI can significantly boost marketing, customer care and supply chain data management with more reliable real-time data. AI tools streamline how data is ingested, modified and managed. For example, IT teams can imbed AI tools into Google Cloud Stream analytics to get real-time personalization, detect anomalies and predict maintenance scenarios.

  • Increased Security

    As enterprises deploy more applications in the cloud, intelligent data security is crucial to keep data safe. IT teams can use AI-powered network security tools to track and evaluate network traffic. AI-powered systems can raise a flag as soon as they find an anomaly. This proactive approach helps prevent any damage to critical data. For example, Amazon GuardDuty is an intelligent threat detection tool that uses AI and machine learning to find potential risks.