ML Archives - Everest Group

Lenders Must Invest in ABCDE Technologies to Meet Consumers’ Expectations | Market Insights™

June 25, 2021

ABCDE technologies

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Payments Firms Are Adopting Digital Technologies to Modernize and Simplify IT Systems | Market Insights™

June 25, 2021

Recharge Your AI initiatives with MLOps: Start Experimenting Now | Blog

May 24, 2021

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.

MLOps to the rescue

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:

Continuous Training (CT): Seeks to automatically and continuously retrain the MLOps models based on incoming data
Continuous Monitoring (CM): Aims to monitor the performance of the model in terms of its accuracy and drift

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.

Advantages of using MLOps

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:

Repeatable workflows: Saves time and allows data scientists to focus on model building because of the automated workflows for training, testing, and deployment that MLOps provides. It also aids in creating reproducible ML workflows that accelerate fractionalization of the model
Better governance and regulatory compliance: Simplifies the process of tracking changes made to the model to ensure compliance with regulatory norms for particular industries or geographies
Improved model health: Helps continuously monitor ML models across different parameters such as accuracy, fairness, biasness, and drift to keep the models in check and ensure they meet thresholds
Sustained model relevance and RoI: Keeps the model relevant with regular training based on new incoming data so it remains relevant. This helps to keep the model up to date and provide a sustained Return on Investment (RoI)
Increased experimentation: Spurs experimentation by tracking multiple versions of models trained with different configurations, leading to improved variations
Trigger-based automated re-training: Helps set up automated re-training of the model based on fresh batches of data or certain triggers such as performance degradation, plateauing or significant drift

Starting your journey with MLOps

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:

Build a cross-functional team – Engage team members from the data science, operations, and business front with clearly defined roles to work collaboratively towards a single goal
Establish common objectives – Set common goals for the cross-functional team to cohesively work toward, realizing that each of the teams that form an MLOps pod may have different and competing objectives
Construct a modular pipeline – Take a modular approach instead of a monolithic one when building MLOps pipelines since the components built need to be reusable, composable, and shareable across multiple ML pipelines
Select the right tools and platform – Choose from a plethora of tools that cater to one or more functions (management, modeling, deployment, and monitoring) or from platforms that cater to the end-to-end MLOps value chain
Set baselines for monitoring – Establish baselines for automated execution of particular actions to increase efficiency and ensure model health in addition to monitoring ML systems

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].

Customer Experience Is at the Center of Key Automotive ER&D Investments | Market Insights™

April 13, 2021

Automotive ER&D

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The Evolution from Robotic Surgery to Digital Surgery | Blog

April 1, 2021

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.

Transforming from robotic surgery to digital surgery

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.

AI/ML and data analytics will help the robots learn from past procedures and ensure better surgery planning and reasoning. They will also help support cognitive functions such as decision making, problem solving, and speech recognition. One real-world example of AI/ML is Stryker’s Mako robot, which learns from past procedures to ensure better positioning of surgical implants for stability
Strong network and connectivity will enable real-time data sharing of patient outcomes, best practices, and support remote surgery at a global level
Augmented Reality/Virtual Reality (AR/VR) and advanced visualization technologies enhance surgical visibility beyond the naked eye and improve anatomical education and surgeon training modalities through interactive simulations
Remote monitoring, sensors, and wearables can assist in intra-operative and post-operative surgical care through real-time data exchange for better clinical outcomes and reduced care costs

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 a single, connected next-generation digital surgery platform

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.

Please share your views on robotic surgery and the digital surgery ecosystem with us at [email protected] and [email protected].

Next-Gen Technologies Have Huge Potential in Talent Acquisition | Market Insights™

April 16, 2018

In the next five years, over half of all talent acquisition tasks can be automated using a variety of technologies including RPA, cognitive & AI, and analytics, among others, either on their own or in combination with one another

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