Technology as a Strategy: Why Future Business Leaders Must Master Cloud, AI and MLOps

For today’s business students and young professionals, interacting with cloud computing, artificial intelligence (AI) and machine learning operations (MLOps) is an absolute requirement – no longer a select niche skill affordable but a handful. These technologies are transforming the way organizations run, compete and expand. And only business leaders cognizant of this will be most able to transform the industry in the decade that comes.

The New Business Operating System: Cloud

Cloud computing, as a means of data storage or web site hosting, has come a long long way since its inception. It is now the digital foundation of most modern enterprises. Consulting giant Gartner predicted 14% of all enterprise IT spend will be on cloud services in 2024. And, as a vertical with rapid potential, the public cloud is expected to drive its revenues forward at the rate of over 20% CAGR in coming years.

It’s quite easy to understand its essence: cloud is more flexible, scalable and cost-effective. Businesses no longer have to invest a large amount of finances in building physical infrastructure or long-term capital expenditure, which often proves more difficult anyway. Cloud services such as Amazon Web Services (AWS), Microsoft Azure and the Google Cloud Platform (GCP) have enabled businesses to rent computing resources as needed – scaling, up or down on the fly to pay only a fraction of their use. This was a revolutionary step – from startups to large enterprises alike.

There are three large layers of services offered by cloud platforms. The managers of tomorrow need to know the difference and how each fits into their business model to be able to accelerate their businesses into the future:

• Infrastructure as a Service (IaaS): A platform that allows on-demand rent of the basic elements of computing – such as network and storage or virtual machines on the cloud. Businesses are able to scale infrastructure and reposition it according to their demands without being required to own physical servers. It provides IT teams with the greatest flexibility to maintain the control of their applications, operating systems and configuration.
  
  
• Platform as a service (PaaS): PaaS provides an end-to-end cloud computing atmosphere where developers can create, test and release programs quickly. It is a form of abstraction of infrastructure that supplies tools, abstraction frameworks and runtime environments. The platform requires no scaling, patching and maintenance – all the developers need to do is to write their code and be innovative.
  
  
• Software as a Service (SaaS): SaaS is a software solution that provides software that is ready to use via the internet and therefore accessed through a browser. The user does not have to install or even manage anything locally, it all works on the servers of the provider. Typical examples of it are Gmail, Salesforce, and Microsoft365. It is easily implemented, economical and automatically upgrades itself.

From Data to Decisions: AI and ML

Machine learning (ML) and Artificial Intelligence (AI) are the focal points of digital innovation. Be it in the customisation of customer experiences, demand forecasting, logical optimisation, fraud detection – you name it – businesses are turning to ML models to make quicker, more intelligent decisions.

However, ML model building is not only about good code. The real challenge is scaling said models to production – it’s one thing to work out a model using a laptop, but an entirely different experience altogether to achieve good performance on a platform serving thousands (or millions) of users.

This is where the cloud comes in. Large neural networks require computation power in the form of large GPUs and TPUs, which are supplied by cloud platforms. Often, they also supply unified ML platforms.

• The AWS SageMaker is a fully managed cloud service that assists in the entire ML lifecycle, from data preparation and model training to deployment and monitoring. It makes infrastructure management easy, expands automatically and fits in well within the overall AWS environment, making it among the best tools when it comes to creating and spewing ML models promptly and consistently.
  
  
• Azure Machine Learning provides a simplified platform to develop, train and implement ML models and has high connectivity with Microsoft applications such as Power BI and Excel. Low-code interface, AutoML capabilities and strong enterprise-level security are reasons it serves organizations already entrenched within the Microsoft ecosystem well.
  
  
• Google Vertex AI is an end-to-end platform that aids data preparation, training, deployment and monitoring. It can work with AutoML, as well as other custom models. It is natively integrated with BigQuery, Google’s scalable cloud data warehouse and Looker, its modern BI and visualization platform, offering expanded, sophisticated capabilities in MLOps and pipeline-based development and management. This makes it an excellent choice when a team with experience is interested in carrying out more complicated machine learning tasks.

Data scientists and business analysts of today leverage these platforms to transfer directly to implementation and avoid being bogged down with infrastructure.

Turning Experiments Into Enterprise-Grade Solutions: MLOps

Since ML is increasingly becoming business-central, it becomes essential to ensure that there is discipline in deployment and management of the models. This is when MLOps, a term coined to put in practice the DevOps experience within the software engineering domain, comes in. MLOps emphasizes optimizing the ML model lifecycle, especially when managing the lifecycle of ML models in production. Key elements of MLOps are:

• Version Control tracks changes to code, datasets and models. It allows working together with cross-team collaboration, makes outputs more reproducible and enables returning to the past versions in case of necessity. Managing such artifacts across the ML lifecycle is typically performed with the use of tools such as Git, DVC, or MLflow.
  
  
• Continuous integration and continuous deployment (CI/CD) pipelines allow machine learning models to be tested, validated and released automatically. They eliminate human error, speed up development and guarantee environment scalability. CI/CD can enable teams to transition between experimenting and making things available in production highly effectively making deployments quicker, safer, and more scalable.
  
  
• Model monitoring using performance measures under real-world conditions, triggering alerts or retraining workflows when anomalous or rising data-drift values are identified. Active monitoring is vital in ensuring deployed models are accurate, in line with fairness guidelines and relevant over time, key to maintaining business value creation and preventing degradation in high stakes environments.
  
  
• Security and Governance in ML implies that data and models are shared and accessed in a secure manner, according to the regulations and standards of use. This involves encryption, control of access, an audit trail and tracing. In particular, especially in regulated fields, effective governance can maximize privacy and instill confidence in decision-making made through AI.

In the absence of MLOps, even the most effective model can fail to be valuable and safe in terms of performance. Some cloud platforms are already offering greater MLOps capabilities. Out-of-the-box leaders, however, must figure out how to integrate and deploy it in the most productive manner.

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This article is the second of the two-part series, ‘Technology as a Strategy’. Read more on the Praxis Business School blog.