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From virtual assistants to chatbots and automation to smart homes, Artificial Intelligent (AI) and Machine Learning (ML) have become prominent in our daily life.  Machine Learning is a pervasive and powerful sub-field of AI that enables machines to self-learn and imitate intelligent human behavior. Today, ML is the intelligence behind predictive texts, chatbots, Netflix show suggestions, predictive social media feeds and more. Machine Learning can enable organizations to make sense of valuable business data, automate human-intensive business processes, increase productivity and deliver business growth.

Microsoft Azure Machine Learning

Microsoft Azure Machine Learning delivers comprehensive, simple, robust and responsible services enabling businesses to build impactful data driven apps quickly and securely using built-in, drag-and-drop configurations. It is a cloud- based MLOps platform that helps enterprises accelerate the development, operationalization, and governance of AI models. It enables data scientists, data engineers, analysts, and other users to complete each stage of the ML lifecycle from accessing the data, tools, and infrastructure they need to developing models through deployment, monitoring, and retraining. It also provides the data and model lineage capabilities, as well as the enterprise-grade security features, necessary to reduce risk and ensure compliance.

Drivers leading to the Azure Machine Learning investment

Organizations see the potential growth and future in Machine Learning, however, cost, special coding skills, and hardware are the most challenging factors for them. To better understand the benefits, costs, and risks associated with this investment, Microsoft and Forrester interviewed and surveyed data scientists, ML, or AI decision-makers to understand the key challenges organizations were facing before the implementation of Azure ML.

Prior to using Azure ML, organizations either had little-to-no existing machine learning capabilities, leveraged third party and open-source applications, or had disconnected and incomplete legacy, on- premises ML tools. Data scientists and other users struggled to get infrastructure and environment provisioned to undertake AI projects and encountered, at best, delays getting their models into production or, at worst, the inability to deploy their models at all. When models were deployed, they struggled to monitor and retrain the models effectively resulting in wasted manual effort, poorly performing models, and hidden risk. The net result was delayed innovation inefficiency and lower business impact.

Why Microsoft Azure ML

After the investment in Azure ML, organizations have experienced numerous improvements in their ability to develop new ML projects and orchestrate the pipelines necessary to deploy them, leading to greater revenue and lower operating costs. They noted greater productivity across a wide range of users, faster time to value from faster onboarding, and lower costs thanks to their ability to move off legacy solutions. Less tangibly, the investment helped them innovate, accelerating the democratization of AI and access to the latest ML innovations with better security and governance.

The survey highighted three key benefits of Azure ML platform.

• Scalable, pay-as-you go cloud solution. As a cloud solution, Azure ML gives organizations instant access to provision and scale high- capacity resources. This drastically reduces effort provisioning infrastructure, gives data scientists the flexibility to iterate faster, and allows teams to scale more models into production. In addition, organizations only pay for what they consume, leading to additional cost savings and flexibility.
• Operationalize AI with MLOps. MLOps allows organizations to automate the manual processes of deploying models and monitoring and retraining them, thus enabling them to dramatically accelerate the time-to-value of ML projects, as well as operationalize models at a different order of magnitude (from a handful to hundreds/thousands). Simultaneously, it tracks data and model lineage ensuring reproducibility, regulatory compliance, and the ability to retrain models in the future. In addition, nonprofessional and professional data scientists alike are given entry points that were not available in legacy solutions, improving machine learning adoption, usability, and extensibility.
• End-to-end platform with interoperability efficiencies. Interviewees searched for a solution that integrated with their existing Azure stack and open-source technology, such as Azure Synapse Analytics, Power BI, Arc, Data Lake, Data Factory, and Python, to drive efficiencies across the end-to-end machine learning lifecycle.

Softline has significant experinece in all key service capabilities for the full Microsoft Azure Machine Learning lifecycle. If you would like to know more about the solution and its benefits, or you plan to develop your organization with Microsoft Azure ML, contact us through the below form.

If you are interested in the summary of Forrester survey in detail, download it here: https://azure.microsoft.com/en-us/resources/forrester-total-economic-impact-tei-of-azure-machine-learning/