Tag: machine learning

The Rise of Machine Learning Operations: How MLOps Can Become the Backbone of AI-enabled Enterprises | Blog

We’ve seen enterprises developing and employing multiple disparate AI use cases. But to become a truly AI-enabled enterprise, many standalone use cases need to be developed, deployed, and maintained to solve different challenges across the organization. Machine Learning Operations or MLOps offers this promise to seamlessly leverage the power of AI without hassle.

Everest Group is launching its MLOps Products PEAK® Matrix Assessment 2022 to gain a better understanding of the competitive service provider landscape. Discover how you can be part of the assessment.

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With the rise in digitization, cloud, and internet of things (IoT) adoption, our world generates petabytes of data every day that enterprises want to mine to gain business insights, drive decisions, and transform operations.

Artificial Intelligence (AI) and Machine Learning (ML) insights can help enterprises gain a competitive edge but come with developmental and operational challenges. Machine Learning Operations (MLOps) can provide a solution. Let’s explore this more.

While tools for analyzing historical data to gain business insights have become well-adopted and easier to use, using this information to make predictions or judgment calls is a different ball game altogether.

Tools that can deliver these capabilities based on programming languages such as Python, SAS, and R are known as data science or Machine Learning (ML). Popular deep learning frameworks include Tensorflow, Jupyter, and PyTorch.

Over the past decade, these tools have gained traction and have emerged as attractive options to develop predictive use cases by leveraging vast amounts of data to assist employees in making decisions and delivering consistent outcomes. As a result, enterprises can scale processes without proportionately increasing employee headcount.

Machine Learning varies from traditional IT initiatives as it does not take a one-size-fits-all approach. Earlier data-scientist implementation teams operated in silos, worked on different business processes, and leveraged disparate development tools and deployment techniques with limited adherence to IT policies.

While the benefits promised are real, replicating them across geographies, functions, customer segments, and distribution channels, all with their own nuances, called for a customized approach across these categories.

This led to the development of a plethora of specialized models that individual business teams had to be kept informed of as well as significant infrastructure and deployment costs.

Advances in ML have since driven software providers to offer approaches to democratize model development, making it possible to now create custom ML models for different processes and contexts.

MLOps to the rescue

In today’s world, developing multiple models that serve different purposes is less challenging. Enterprises who want to successfully become AI-enabled and deploy AI at scale need to equip individual business teams with model deployment and monitoring capabilities.

As a result, software vendors have started offering a DevOps-style approach to centralize and support the deployment requirements of a vast number of ML models, with individual teams focusing only on developing models best suited to their requirements.

This new rising methodology called MLOps is a structured approach to scaling ML across organizations that brings together skills, techniques, and tools used in data engineering and machine learning.

What’s needed to make it work

Technical Capabilities Required for MLOps

MLOps assists enterprises in decoupling the development and operational aspects in an ML model’s lifecycle by bringing DevOps-like capabilities into operationalizing ML models. Technology vendors are offering MLOps to enterprises in the form of licensable software with the following capabilities:

  • Model deployment: In this important stage, the ability to deploy models on any infrastructure is important. Other features include storing an ML model in a containerized environment and options to scale compute resources
  • Model monitoring: Tracking the performance of models in production is complex and requires a carefully designed performance measurement matrix. As soon as models start showing signs of declining prediction accuracy, they are sent to the development team for review/retraining
  • Collaboration and platform management: MLOps solutions offer platform-related features such as security, access control, version control, and performance measurement to enhance reusability and collaboration among various stakeholders, including data engineers, data scientists, ML engineers, and central IT functions

Additionally, MLOps vendors offer support for multiple Integrated Development Environments (IDEs) to promote the democratization of the model development process.

While various vendors offer built-in ML development capabilities within their solutions, connectors are being developed and integrated to support a wide array of ML model file formats.

Additionally, the overall ML lifecycle management ecosystem is rapidly converging, with vendors developing end-to-end ML lifecycle capabilities, either in-house or through partner integrations.

MLOps can promote rapid innovation through robust machine learning lifecycle management, increase productivity, speed, and reliability, and reduce risk – making it a methodology to pay attention to.

Everest Group is launching its MLOps Products PEAK® Matrix Assessment 2022 to gain a better understanding of the competitive landscape. Technology providers can now participate and receive a platform assessment.

Learn how to participate

To share your thoughts on this topic, contact [email protected] and [email protected].

Federated Learning: Privacy by Design for Machine Learning | Blog

With cyberattacks and data breaches at all-time highs, consumers are increasingly skeptical about sharing their data with enterprises, creating a dilemma for artificial intelligence (AI) that needs massive data to thrive. The nascent technology of federated learning offers an ideal growing alternative for healthcare, life sciences, banking, finance, manufacturing, advertising, and other industries to unleash the full potential of AI without compromising the privacy of individuals. To learn how you can have all the data you need while protecting consumers, read on.  

Privacy preservation with federated learning

The infinite number of massive data breaches that have stripped individuals of their privacy has made the public more aware of the need to protect their data. In the absence of strong governance and guidelines, people are more skeptical than ever about sharing their personal data with enterprises.

This new data-conscious paradigm poses a problem for artificial intelligence (AI) that thrives on huge amounts of data. Unless we can figure out a way to train machines on significantly smaller data sets, protecting the privacy and data of users will remain key obstacles to intelligent automation.

Federated learning (FL) is emerging as a solution to overcome this problem. Broadly speaking, Federated learning is a method of training machine learning models in a way that the user data does not leave its location, keeping it safe and private. This differs from the traditional centralized machine learning methods that require the data to be aggregated in a centralized location.

Federated learning is a mechanism of machine learning, wherein the process of learning takes place in a decentralized manner across a network of nodes/edge devices, and the results are aggregated in a central server to create a unified model. It essentially comprises decoupling the activity of model training with centralized data storage.

The Mechanism of Federated Learning

By training the same model across an array of devices, each with its own set of data, we get multiple versions of the model, which, when combined, create a more powerful and accurate global version for deployment and use.

In addition to training algorithms in a private and secure manner, Federated learning provides an array of other benefits such as:

  • Training across data silos
  • Training on heterogeneous data
  • Lower communication costs
  • Reduced liability

Federated learning applicability and use cases

Based on an Everest Group framework, we have found Federated learning is most suitable and being adopted at higher rates in industrials where data is an extremely critical asset that is present across different locations in a distributed fashion and privacy is paramount.

Federated learning is especially beneficial for industries that have strict data residency requirements. This makes the healthcare and life-sciences industries perfect candidates for its adoption. Federated learning can help facilitate multi-institution collaborations between medical institutions by helping them overcome regulatory hurdles that prevent them from sharing patient data by pooling data in a common location.

The next industry ripe for the adoption of Federated learning is the banking and financial sectors. For instance, it can be used to develop a more comprehensive and accurate fraud analytics solution that is based on data from multiple financial entities.

Another industry where we see high applicability of Federated learning is the manufacturing industry. By ensuring collaboration between different entities across the supply chain, using Federated learning techniques, there is a case to build a more powerful model that can help increase the overall efficiency across the supply chain.

Federated learning also might find increased use in interest-based advertising. With the decision to disable third-party cookies by major internet browsers, marketers are at a loss for targeted advertising and engagement. With Federated Learning, marketers can replace individual identifiers with cohorts or group-based identifiers. These cohorts are created by identifying people with common interests based on individual user data such as browsing habits stored on local machines.

An ecosystem on the rise

Since Google introduced the concept of Federated learning in 2016, there has been a flurry of activity. Given that this is a nascent technology, the ecosystem is currently dominated by big tech and open-source players. We see hyperscalers taking the lead with Microsoft and Amazon Web Services (AWS) making investments to activate Federated learning, followed by Nvidia and Lenovo who are looking at the market from a hardware perspective.

Another segment of players working in this arena are startups that are using Federated learning to build industry-specific solutions. AI companies such as Owkin and Sherpa.ai are pioneering this technology and have developed Federated learning frameworks that are currently operational at a few enterprises’ locations.

The adoption and need for Federated learning depend on the industry and vary with the use case. Everest Group has developed a comprehensive framework to help you assess and understand the suitability of Federated learning for your use-case in our Latest Primer for Federated Learning. The framework is built on four key parameters that include data criticality, privacy requirement, regulatory constraint, and data silo/ diversity.

Federated learning provides us with an alternative way to make AI work in a world without compromising the privacy of individuals.

If you are interested in understanding the suitability of federated learning for your enterprise, please share your thoughts with us at [email protected].

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

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.

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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:

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  • 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].

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