In this era of industrialization for Artificial Intelligence (AI), enterprises are scrambling to embed AI across a plethora of use cases in hopes of achieving higher productivity and enhanced experiences. However, as AI permeates through different functions of an enterprise, managing the entire charter gets tough. Working with multiple Machine Learning (ML) models in both pilot and production can lead to chaos, stretched timelines to market, and stale models. As a result, we see enterprises hamstrung to successfully scale AI enterprise-wide.
To overcome the challenges enterprises face in their ML journeys and ensure successful industrialization of AI, enterprises need to shift from the current method of model management to a faster and more agile format. An ideal solution that is emerging is MLOps – a confluence of ML and information technology operations based on the concept of DevOps.
According to our recently published primer on MLOps, Successfully Scaling Artificial Intelligence – Machine Learning Operations (MLOps), these sets of practices are aimed at streamlining the ML lifecycle management with enhanced collaboration between data scientists and operations teams. This close partnering accelerates the pace of model development and deployment and helps in managing the entire ML lifecycle.
MLOps is modeled on the principles and practices of DevOps. While continuous integration (CI) and continuous delivery (CD) are common to both, MLOps introduces the following two unique concepts:
We are witnessing MLOps gaining momentum in the ecosystem, with hyperscalers developing dedicated solutions for comprehensive machine learning management to fast-track and simplify the entire process. Just recently, Google launched Vertex AI, a managed AI platform, which aims to solve these precise problems in the form of an end-to-end MLOps solution.
MLOps bolsters the scaling of ML models by using a centralized system that assists in logging and tracking the metrics required to maintain thousands of models. Additionally, it helps create repeatable workflows to easily deploy these models.
Below are a few additional benefits of employing MLOps within your enterprise:
Implementing MLOps is complex because it requires a multi-functional and cross-team effort across the key elements of people, process, tools/platforms, and strategy underpinned by rigorous change management.
As enterprises embark on their MLOps journey, here are a few key best practices to pave the way for a smooth transition:
When embarking on the MLOps journey, there is no one-size-fits-all approach. Enterprises need to assess their goals, examine their current ML tooling and talent, and also factor in the available time and resources to arrive at an MLOps strategy that best suits their needs.
For ML to keep pace with the agility of modern business, enterprises need to start experimenting with MLOps now.
Are you looking to scale AI within your enterprise with the help of MLOps? Please share your thoughts with us at [email protected].
The robotic surgery market has surged over the last decade. According to an article published by the JAMA Network Open in early January 2020, robot-assisted surgical procedures accounted for 15.1 percent of all general surgeries in 2018, up from 1.8 percent in 2012. And the market has grown even more since 2018. For example, the utilization rate of Intuitive Surgical’s da Vinci robot in US hospitals has grown more than 400 percent in the last three years.
To capture their piece of the robotic surgery market pie, other MedTech giants, including Johnson & Johnson (J&J), Medtronic, Stryker, and Zimmer Biomet have turned to acquisitions and strategic partnerships. Stryker paid a whopping US$1.65 billion in 2013 to acquire Mako Surgical Corp. Zimmer Biomet bought Medtech for its Rosa Surgical Robot in 2016 for US$132 million. J&J and Medtronic acquired Orthotaxy and Mazor Robotics, respectively, in 2018. And J&J subsequently bought Auris Health and Verb Surgical in 2019.
With all this money being spent on robotic surgery company acquisitions, it is clear that the MedTech giants intended to fight head-on with one another to build the best surgical robot.
But building the best surgical robot does not assure market leadership. Indeed, robotics is only one aspect of the digital surgery ecosystem. In order to excel in the robotic surgery space, companies need to build solutions that complement their surgical robots with digital technology tools and capabilities.
As you see in the following image, the digital surgery ecosystem consists of imaging, visualization, analytics, and interoperability technologies that enhance the capabilities of surgical robots, enabling companies to unlock the full array of potential benefits robotic surgery has to offer – better precision and control, enhanced surgical visibility, remote surgery, better clinician and patient experiences, etc.
Let’s take a quick look at the value each of the digital technologies can bring to robotic surgery.
Realizing the benefits of digital technologies, MedTech companies are starting to make investments in them to augment their surgical robots. For example, Medtronic in 2020 acquired Digital Surgery, a leader in surgical AI, data and analytics, and digital education and training to strengthen its robotic-assisted surgery platform. Similarly, in 2021, Stryker acquired Orthosensor to enhance its Mako surgical robotics systems with smart sensor technologies and wearables, and Zimmer partnered with Canary Medical to develop smart knee implants. MedTech companies are also starting to change their branding to reflect their move to digital. For example, J&J is positioning its new offerings as digital surgery platforms instead of robotic surgery platforms.
Building specialized robots for different surgical procedures requires either a huge capital investment to acquire such individualized capabilities or extensive resources and time to develop them in-house. So, it’s neither feasible nor cost-effective to do so. Therefore, it would be ideal for MedTech organizations to focus on developing one robot that supports the entire breadth of surgical procedures.
With their history of robotic acquisitions over the last three years, MedTech giants should be looking at integrating multiple point solutions to build a single, connected next-generation digital surgery platform. The following image depicts our vision of a truly connected digital surgery ecosystem built around a digital surgery platform. It ensures interoperability among all types of surgical robots so they can continually learn and evolve by sharing best practices, surgical procedures, and associated patient data.
J&J has already shared its vision and roadmap for building a next-generation digital surgery platform. It brings together robotics, visualization, advanced instrumentation, connectivity, and data analytics to enable its digital surgery platform to improve outcomes across a broad range of disease states. It has announced its plans to integrate its recently unveiled Ottava platform with the Monarch platform it gained from its 2019 acquisition of Auris Health to build a strong position in the digital surgery market.
With MedTech giants in the initial phase of building their next-generation connected digital surgery ecosystem, they will need to have the right fit of complementary digital technologies to truly scale their impact – alleviating surgeon workloads, driving productivity, enabling personalization, and better clinical outcomes. Service providers that bring niche talent and a balanced portfolio of engineering and digital services will be a partner of choice for MedTech giants in this journey.
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