Tag: automation

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].

Democratization of Automation, Post-pandemic | Webinar

Everest Group’s Anil Vijayan and Amardeep Modi will join experts from Microsoft and HCL Technologies to explore how — with the right set of enabling initiatives — enterprises can help enhance the adoption of automation through their business user community, and exponentially scale their automation outcomes.

They will discuss:

  • What are the key impacts of COVID-19?
  • How automation ecosystem has expanded beyond RPA?
  • Understanding methodologies to achieve broader transformation outcomes through automation
  • Insights on how organizations are enabling citizen developers to achieve broader automation goals

When

Join us Wednesday, May 26, 2021, at 11 am CST, 12 pm EST, 4 pm GMT, 9:30 pm IST

Where

Live, virtual event

Presenters

Amardeep Modi
Practice Director, Everest Group

Anil Vijayan
Vice President, Everest Group

Joy Trajano
Director, Marketing Operations, Microsoft

Ashvini Sharma
Group Program Manager, Microsoft

Jason Skelton
Sr. Group Marketing Tech Manager, Microsoft

Siddharth Gandhi
Director, Digital Process Operations, HCL Technologies

Rachit Chawla
Director, Digital Process Operations, HCL Technologies

 

Internet of Things Will Connect the Supply Chain in the “Next Normal” | Blog

Imagine a utopia where minimum human intervention is needed to run an entire shop floor. In this world, manufacturers have total control and visibility of all products, machines predict equipment failures and correct them, shelves count inventory, and customers check themselves out. While such a supply chain model seems improbable and far into the future, the likes of Amazon, Walmart, and Toyota, are already on their way to achieving this vision. At the center of their supply chain initiatives making this possible is the Internet of Things (IoT.)

The supply chain is considered the backbone of a successful enterprise.  However, firms find it increasingly challenging to establish a robust supply chain model. The disruptions caused by COVID-19 have further made matters worse as ‘disconnected enterprises’ struggle to gain complete supply chain visibility. The pandemic has established that supply chain disruptions and uncertainties will become more frequent going forward.

Supply chain challenges

The current supply chain landscape faces numerous challenges that need to be addressed.  These issues are illustrated below:

Challenges in Current Supply chain

 Future-proofing the supply chain using IoT

As enterprises strive to develop a resilient supply chain, IoT will occupy the center stage. An interconnected supply chain will bring together suppliers/vendors, logistic providers, manufacturers, wholesalers and retailers, and customers dispersed by geography. The technology ensures improved efficiency, better risk management, end-to-end visibility, and enhanced stakeholder experience.

A seamlessly connected supply chain provides advantages at every stage of the value chain for each of the stakeholders. The exhibit below showcases a connected supply chain ecosystem:

Connected ecosystem for supply chain

 Let’s look at how some companies are capturing the benefits IoT:

  • Real-time location tracking

Using real-time data (captured from GPS coordinates) tracking the movement of raw materials/finished goods, IoT technology aids firms in determining where and when products get delayed. This helps managers ensure route optimization and better plan the delivery schedule. IoT, in combination with blockchain, helps secure the products against fraud. For example, Novo Surgical leverages IoT for optimally tracking and tracing its ‘smart surgical instruments.’ This has reduced errors, decreased surgical instrument loss, increased visibility and efficiency, and improved forecasting of demand for the firm.

  • Equipment monitoring

Sensors on machines constantly collect information around the functioning of the machine, enabling managers to monitor them in real time. By analyzing parameters such as machine temperature, vibration, etc., manufacturers can better predict machine downtime and take necessary actions to mitigate this. For instance, Toyota partnered with Hitachi to leverage the vendor’s IoT platform and use the data collected to reduce unexpected machine failures and improve the reliability and efficiency of equipment.

  • Smart inventory management

IoT sensors in the warehouse assist in tracking the movement of individual items, providing an efficient way to monitor inventory levels and prevent pilferage. Smart shelves contain weight sensors that monitor the product weight to determine when products are out of stock. Walmart has been leveraging smart shelves in its retail stores to manage its products more efficiently and improve the shopping experience.

  • Warehouse management

IoT technology uses sensors that can monitor and adjust warehouse parameters such as humidity, temperature, pressure, and avoiding spoiling of items. Leading e-commerce players like Amazon and Alibaba have been pioneers in leveraging IoT to optimize warehouse management.

 Charting the journey for a connected supply chain

As enterprises aim to future-proof their supply chain, they will need a structured path following these five steps below:

  1. Develop a business case: Enterprises need to determine the current gaps in their supply chain and identify the extent of digitization of their supply chain to develop the business case for a connected supply chain.
  2. Secure buy-in from supply partners: Successful implementation of IoT in the supply chain requires the various partners to collaborate and adopt the technology together. Securing a buy-in from each member of the value chain – vendors/suppliers, OEM players, logistics operators, and retailers – is imperative for firms to realize the complete benefits. Compatibility of the technology platforms leveraged by the various supply partners is essential to develop a seamless supply chain.
  3. Invest in security: Invest in security and data protection initiatives early on to avoid supply chain breaches. Performing regular security and vulnerability assessments across the value chain and investing in next-generation technology-based security solutions is essential.
  4. Leverage other technologies: While IoT has a plethora of benefits across the supply chain, consider leveraging next-generation technologies such as blockchain, artificial intelligence, and edge computing in confluence with IoT to further enhance the capabilities of the use cases.
  5. Partner for implementation: To overcome concerns around skills and address data reconciliation challenges, consider partnering with IoT providers with expertise in the supply chain arena. Service/solution providers also are instrumental in bringing a security layer that can aid in addressing data security concerns and governance issues.

Since IoT is an interplay of multiple devices and machines, a successful IoT implementation requires firms to invest in sensors, cloud/edge infrastructure, IoT connectivity networks, data management and analytics solutions, and application development and management. Enterprises can accelerate their IoT supply chain journeys by partnering with solution providers with strong expertise in IoT products and services capabilities in the supply chain arena.

Are you embarking on your connected supply chain journey? Please share your thoughts and experiences with us at [email protected] and [email protected].

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