MLOps, or machine learning operations, has become the new buzzword in the industry, is giving rise to new job roles, and businesses are deriving insane results after implementing it.

Currently, we are in a position where every other company is trying to incorporate AI and ML technologies into their products or services. The increased use of disruptive technologies in businesses has led to new developments that ensure better results. One such innovation is this engineering discipline called MLOps.

MLOps is the discipline of AI model delivery. It includes all the capabilities that data science, product teams, and IT operations have to deploy to secure machine learning and other probabilistic models in production. Machine learning operations combine the practice of using AI/ML with the principles of DevOps to represent an ML life cycle that exists alongside the software development life cycle for more efficient workflow and accurate results.

Benefits of using MLOps in business operations

• Ensures a secure business: MLOps can maintain the security of the enterprise through role-based access controls across different platforms for users, data, models, and resources to ensure efficient delivery of results.

• Enhance the productivity of the teams: MLOps integrates the business workflows and tooling systems to provide clear roles and reduce wasted time and hurdles between operations. It allows users to have constant access to monitor and report on current projects to make informed decisions beforehand.

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• Manage infrastructure: It systematically manages computation resources across different models to meet business goals and also ensures cost-effective operations. MLOps can be deployed on-premises, in the cloud, or in a hybrid environment.

• Risk assessment: Assessing the risks and the cost of failures is an important step to consider while doing business. This technology rightfully intercepts the financial damages done or might happen in the future to prevent further losses.

• Bridges communication gaps: A communication gap between the technical and the business teams are a common issue in several companies. The teams find it hard to come to terms with a common language to collaborate forces. MLOps bridges these gaps and ensures efficient communication for timely deliveries.

The best tools to use while deploying MLOps in businesses

• DVC: Data Control Vision, or DVC, is an open-source platform for machine learning projects. It is an experimentation tool that helps the users define their data pipeline, irrespective of the programming language they use. This platform can handle versioning and organizing extensive amounts of data and store them in a structured manner.

• Amazon Sagemaker: This tool enables developers and data scientists to easily build, train, and deploy machine learning models at any level. It is a cloud-based system that eliminates all barriers that slow down developers interested in machine learning practices.

• Pachyderm: It is a platform that combines data lineage with end-to-end data pipelines. It is available in three versions, sufficing the needs of individual users, the ones working in teams, and for large-scale organizational users.

• Polyaxon: It is a platform for producing and managing the entire life cycle of machine learning and deep learning projects. This tool can be deployed into any data center or a cloud provider, which is managed by Polyaxon. When it comes to the orchestration of projects, this tool provides the best services.

• Neptune: Neptune is a metadata store that is built for research and production teams that run ML experiments. Data versioning, experiment tracking, and registry allow this tool to act as a connector between different parts of the MLOps workflow.

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Microsoft reported stronger results for its fourth quarter (ended June 30) that it said were once again helped by continued growth in its Azure cloud computing business.

Revenue in Microsoft’s overall Intelligent Cloud business grew 19% from a year earlier, to $11.4 billion, the company said July 18. Within that business, server products and cloud services revenue increased 22%, “driven by Azure revenue growth of 64%,” it said in an earnings news release.

But the cloud continued to drive growth across other areas of Microsoft’s business also. For example, revenue in Productivity and Business Processes increased 14% to $11 billion. Within that segment: Office Commercial products and cloud services revenue grew 14%, driven by Office 365 Commercial revenue growth of 31%; Office Consumer products and cloud services revenue increased 6% as Office 365 Consumer subscribers increased to 34.8 million; and Dynamics products and cloud services revenue jumped 12%, driven by Dynamics 365 revenue growth of 45%, Microsoft said.

Revenue in the More Personal Computing business segment inched up 4% to $11.3 billion, with standouts in that segment including a 13% jump in Windows Commercial products and cloud services revenue. The one weak area was gaming, where revenue fell 10%, with Xbox software and services revenue dipping 3%.

Total Microsoft Q4 revenue grew 12% from a year earlier, to $33.7 billion, while profit increased to $13.2 billion ($1.71 a share) from $8.9 billion ($1.14 a share).

One positive sign for Microsoft is that “customer commitment to our cloud platform continues to grow,” CFO Amy Hood told analysts on the company’s earnings call. During the fiscal year that just ended, “we closed a record number of multi-million dollar commercial cloud agreements, with material growth in the number of $10 million plus Azure agreements,” she said.

Microsoft CEO Satya Nadella used the call to highlight Azure’s strength, along with several new cloud advancements on multiple fronts, including artificial intelligence (AI).

“Azure is the only cloud that extends to the edge – spanning identity, management, security and infrastructure,” he said. This year, Microsoft introduced new cloud-to-edge services and devices – including Azure Data Box Edge, Azure Stack HCI and Azure Kinect – that he noted bring “the full power of Azure to where data is generated.” While Azure Sphere is a “first-of-a-kind edge solution to secure the more than nine billion MCU-powered endpoints coming online each year,” Internet of Things (IoT) Plug and Play “seamlessly connects IoT devices to the cloud without having to write a single line of embedded code,” he said.

Calling Azure the “most open cloud,” he pointed out that, in Q4, Microsoft “expanded our partnerships” with Oracle, Red Hat and VMware “to make the technologies and tools customers already have first-class on Azure.”

Azure, meanwhile, is “the only cloud with limitless data and analytics capabilities across the customers’ entire data estate,” he said, adding: “The variety, velocity and volume of data is increasing, and we are bringing hyper-scale capabilities to relational database services with Azure SQL Database. New analytics support in Cosmos DB enables customers to build and manage analytics workloads that run real-time over globally distributed data. And we offer the most comprehensive cloud analytics – from Azure Data Factory to Azure SQL Data Warehouse to Power BI.”

The “quintessential characteristic for any application being built in 2019 and beyond will be AI,” he went on to predict, telling analysts: “We are democratizing AI infrastructure, tools and services with Azure Cognitive Services – the most comprehensive portfolio of AI tools – so developers can embed the ability to see, hear, respond, translate, reason and more into their applications. And this quarter we introduced new speech-to-text, search, vision and decision capabilities. New updates to Azure ML streamline the building, training and deployment of machine learning models – bringing a no-code approach to machine learning.”

Microsoft’s “differentiated approach – from developer tools and infrastructure to data and analytics to AI – is driving growth,” he said, boasting “the world’s leading companies trust Azure for their mission-critical workloads, including more than 95 percent of the Fortune 500.” On that front, AT&T recently selected Microsoft’s cloud in “one of our largest cloud commitments to-date,” he noted.

Moving up the stack to business process, he also noted that Microsoft’s Dynamics 365 “uniquely enables any organization to create digital feedback loops that take data from one system and use it to optimize the outcomes of another, enabling any business to become an AI-first business.” The company recently introduced Dynamics 365 AI, a new class of AI applications, he pointed out.

Microsoft is “infusing AI across Microsoft 365 to enable new automation, prediction, translation and insights capabilities,” he went on to say. For example, with Workplace Analytics and Microsoft Search, “we take your relationships, schedules and activities and distill insights and knowledge – to help people work smarter, not longer.”

Microsoft is also “investing in cybersecurity to protect customers in today’s ‘zero trust’ environment,” he said, adding: “Microsoft is the only company that offers end-to-end security – spanning identity, device endpoints, information, cloud applications as well as infrastructure. It starts with Azure Active Directory and builds with three new services we introduced this year”: Microsoft Threat Protection, Azure Sentinel and Azure Confidential Computing.

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