The rapid growth of IT operations and increasing complexity in infrastructure management have given rise to AIOps (Artificial Intelligence for IT Operations) platforms. These platforms combine AI and machine learning to improve observability, automate issue resolution, and ensure seamless IT operations. If you’re exploring AIOps for your organization, here’s a detailed list of some of the leading platforms and their capabilities.

What is AIOps?

AIOps stands for Artificial Intelligence for IT Operations. It leverages machine learning, big data analytics, and automation to analyze and manage IT environments. AIOps platforms are designed to:

• Automate routine tasks: Such as alert prioritization and root cause analysis.
• Enhance observability: Monitor applications, networks, and infrastructure in real time.
• Reduce downtime: Proactively predict and resolve issues before they impact users.
• Simplify complexity: Correlate data across various tools and systems for a unified view of operations.

With businesses adopting multi-cloud architectures, containerized environments, and microservices, AIOps has become an essential tool for modern IT teams.

Top AIOps Platforms

1. Dynatrace

Dynatrace is a market leader in AIOps, known for its full-stack observability and AI-powered analytics.

• Key Features:
  • AI-driven problem detection and root cause analysis.
  • Automatic dependency mapping across applications, services, and infrastructure.
  • Unified observability for cloud, containers, and on-premises systems.
• Ideal For: Large enterprises with hybrid and multi-cloud environments.

2. Splunk IT Service Intelligence (ITSI)

Splunk ITSI is an analytics-driven AIOps platform that provides actionable insights into IT environments.

• Key Features:
  • Event correlation and alert prioritization.
  • Advanced visualizations and predictive analytics.
  • Integration with Splunk’s ecosystem for log analysis and security.
• Ideal For: Teams looking to unify observability and security analytics.

3. Moogsoft

Moogsoft specializes in incident reduction and automated resolution, making it a top choice for IT operations teams.

• Key Features:
  • Noise reduction through AI-driven alert clustering.
  • Root cause analysis with dynamic baselining.
  • Workflow automation and collaboration tools.
• Ideal For: Organizations aiming to streamline incident management processes.

4. Datadog

Datadog is a unified monitoring and observability platform with AIOps capabilities. It integrates seamlessly with cloud services, making it a favorite among DevOps teams.

• Key Features:
  • AI-powered anomaly detection and forecasting.
  • Centralized monitoring for logs, metrics, and traces.
  • Real-time dashboards and automated alerts.
• Ideal For: DevOps teams and cloud-native organizations.

5. ServiceNow IT Operations Management (ITOM)

ServiceNow ITOM uses AI and automation to enhance IT operations and deliver proactive issue resolution.

• Key Features:
  • Predictive analysis for potential outages.
  • Integration with ServiceNow’s ITSM for streamlined workflows.
  • Dependency mapping for better infrastructure insights.
• Ideal For: Enterprises already using ServiceNow for IT service management.

6. BigPanda

BigPanda is known for its focus on event correlation and incident automation to reduce IT noise and improve service uptime.

• Key Features:
  • Noise reduction by correlating alerts from multiple sources.
  • Real-time incident analysis and reporting.
  • Open integrations with popular monitoring and observability tools.
• Ideal For: Mid-sized to large organizations with complex IT environments.

7. AppDynamics Cognition Engine

Part of Cisco’s AppDynamics suite, the Cognition Engine adds AI capabilities for application performance management (APM).

• Key Features:
  • Automated anomaly detection and root cause analysis.
  • Application performance baselining with AI-driven insights.
  • Integration with Cisco’s networking tools for unified visibility.
• Ideal For: Businesses focused on application performance optimization.

8. IBM Watson AIOps

IBM Watson AIOps leverages machine learning and natural language processing (NLP) to improve IT operations.

• Key Features:
  • Predictive analytics for potential issues.
  • AI-driven automation for ticket resolution and escalation.
  • Multi-cloud observability and Kubernetes integration.
• Ideal For: Enterprises looking for advanced AI and hybrid cloud capabilities.

9. New Relic Applied Intelligence

New Relic Applied Intelligence focuses on proactive incident management and operational efficiency.

• Key Features:
  • AI-powered anomaly detection and automated event correlation.
  • Unified observability for applications, infrastructure, and logs.
  • Insights-driven dashboards for performance monitoring.
• Ideal For: DevOps teams in agile environments.

10. Elastic Observability

Built on the Elastic Stack, this platform provides AIOps capabilities for log analysis and observability.

• Key Features:
  • Anomaly detection with machine learning.
  • Centralized logging and distributed tracing.
  • Scalable architecture for large datasets.
• Ideal For: Teams using Elasticsearch and looking to extend into AIOps.

Key Benefits of AIOps Platforms

1. Faster Incident Resolution: Automated root cause analysis helps resolve issues quickly.
2. Improved System Reliability: Predictive capabilities reduce outages and downtime.
3. Operational Efficiency: Automating routine tasks frees up IT teams to focus on strategic initiatives.
4. Cost Optimization: Optimized resource allocation and reduced manual efforts lead to significant savings.
5. Scalability: AIOps platforms are designed to handle growing IT complexities in modern environments.

How to Choose the Right AIOps Platform

When selecting an AIOps platform, consider the following:

• Integration Needs: Ensure the platform integrates with your existing tools and systems.
• Scalability: Choose a solution that can grow with your organization.
• Ease of Use: Opt for a platform with intuitive dashboards and workflows.
• Specific Features: Evaluate features like noise reduction, anomaly detection, and automation capabilities.
• Budget: Match the platform’s pricing with your organization’s budget constraints.

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Scientists at Duke University and the Wildlife Conservation Society (WCS) have come up with an interesting set of deep learning algorithms that could analyze more than 10,000 drone images of mixed colonies of seabirds in the Falkland Islands off Argentina’s coast. The Falklands are home to the world’s largest colonies of black-browed albatrosses (Thalassarche melanophris) and the second-largest colonies of southern rockhopper penguins (Eudyptes c. chrysocome). Hundreds of thousands of birds breed on the islands in densely interspersed groups.

The deep-learning algorithm made by the scientists has successfully identified and counted the albatrosses with 97% and the penguins with 87% accuracy. Madeline C. Hayes, a remote sensing analyst at the Duke University Marine Lab, who led the study has a view that using drone surveys and deep learning gives them an alternative that is remarkably accurate, less disruptive, and significantly easier. One person, or a small team, can do it, and the equipment they need to do it isn’t all that costly or complicated.

The colonies, located on two rocky, uninhabited outer islands, have been monitored by teams of scientists who count the number of each species they observe on a portion of the islands and extrapolate those numbers to get population estimates for the full colonies until now. Since the colonies are large and densely interspersed and the penguins are much smaller than the albatrosses and, thus, easy to miss, counts often need to be repeated. It’s a laborious process, and the presence of scientists can disrupt the birds’ breeding and parenting behaviors. Using artificial intelligence will easily solve this problem.

The images are analyzed using a convolutional neural network (CNN). It is a type of AI that employs a deep-learning algorithm to analyze an image and differentiate and count the objects visible to it. The counts are then added together to create comprehensive estimates of the total number of birds found in colonies.

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We examine how monitoring and observability help development teams keep a distributed architecture from coming unraveled by individual failures and performance bottlenecks.

Failure is rarely predictable, and detecting the exact cause of complex application errors post-deployment is excruciatingly difficult. Even the most experienced development teams struggle to prepare for all the possible scenarios that could bring down their applications and put data at risk.

For this reason, the ability to detect problems in real time and address them quickly is essential. This is where observability and monitoring come into play, and architects who approach these two tasks diligently will reap the rewards of a more resilient software architecture. Let’s explore more about the specifics of observability and monitoring, including how they differ and the fundamental practices that each one dictates.

What is observability?

Observability in microservices largely revolves around making sure development teams have access to the data they need to identify problems and detect failures. For example, an observable system can help developers understand why a specific service call failed, or determine the source of bottlenecks in a particular application workflow.

With the surge in microservices adoption, it is imperative that a system is observable for effective debugging and diagnostics. Since services can span across multiple systems and run operations independently, tracing the source of a failure is a grueling and time-consuming task — if even possible.

Observability consists of three fundamental components:

• Logs are timestamped records that provide comprehensive information about an application’s behavior as it executes functions and communications. These logs are particularly useful when things go wrong in a microservices architecture, because architects can use this information to better identify specific defects and debug code.
• Metrics are numeric records of an application’s resource use, performance and stability. For example, metrics will show the number of requests a service can handle per second, or the total amount of resources an activity consumes.
• Traces keep track of IDs, names and other values and help architects monitor application transactions that cross multiple systems. This makes tracing particularly useful for microservices-based, serverless and containerized applications that rely on multitudes of integrations and asynchronous communication.

What is monitoring?

Monitoring is a process that tracks performance and identifies problems and anomalies. Overall, it describes the health, performance, efficiency and other essential features relative to the internal state.

Much like observability, monitoring can help detect and identify failures, but it does so with a focus on qualitative information. For example, you might want to monitor an application for issues such as excessive data consumption, service messaging failures or breaking changes. To use monitoring effectively, architects must determine core sets of metrics that provide a benchmark for the overall health of the system, such as acceptable latency times and call failure rates.

When monitoring microservices-based applications, architects must gain a comprehensive understanding of the various calls an application and its related services make. Don’t forget to monitor APIs and containerized services, and map monitoring processes and responsibilities based on team structure. Everyone should know who owns what service, and who needs to address a certain failure.

Microservices monitoring and observability tools

Some organizations try to adopt a manual, do-it-yourself approach to observability and monitoring by stringing homegrown monitoring solutions into their architecture. However, this takes up a lot of time, and is not likely to meet the needs of large, distributed systems.

Before attempting to do it yourself, you might want to look into existing tools designed to provide the essential aspects of monitoring and observability in microservices. Here are a few notable tools and platforms worth consideration.

Sentry

Sentry is an open source monitoring system designed with a focus on real-time, code-level error tracking that pinpoints failures and allows developers to address issues quickly. Part of Sentry’s appeal rests in its ability to analyze the scope of a failure, allowing developers to easily prioritize errors based on severity. It also features ready-made integrations with most popular development languages and frameworks, such as JavaScript, Python, Objective-C and iOS, as well as services like GitHub and Splunk.

Sensu

Sensu is another open source observability and monitoring tool that excels at monitoring applications, services, servers and containers deployed across large software ecosystems and cloud environments. Some of Sensu’s spotlight features include role-based service identification, its alignment with publish-subscribe messaging patterns and an interface that provides quick visuals of code workflows.

Sumo Logic

Thanks to this platform’s notable proficiency in data aggregation and analysis, Sumo Logic is a very useful tool for gleaning continuous metrics from application logs in real time and quickly spotting performance and stability issues in service workflows. Sumo Logic boasts a number of microservices-specific observability features, such as distributed tracing for services, transactions and application data.

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In today’s modern software world, applications and infrastructure are melding together in different ways. Nowhere is that more apparent than with microservices, delivered in containers that also hold infrastructure configuration code.

That, combined with more complex application architectures (APIs, multiple data sources, multicloud distributions and more), and the ephemeral nature of software as temporary and constantly changing, is also changing the world of monitoring and creating a need for observability solutions.

First-generation application monitoring solutions struggle to provide the same level of visibility into today’s more virtual applications – i.e., containerized and/or orchestrated environments running Docker and Kubernetes. Massively distributed microservices-based applications create different visibility issues for legacy tools. Of course, application monitoring is still important, which has driven the need to add observability into the applications running in those environments. 

While legacy application monitoring tools have deep visibility into Java and .NET code, new tools are emerging that are focused on modern application and infrastructure stacks. According to Chris Farrell, technical director and APM strategist at monitoring solution provider Instana, one of the important things about a microservice monitoring tool is that it has to recognize and support all the different microservices. “I think of it like a giant T where the vertical bar represents visibility depth and the horizontal bar represents visibility breadth,” he explained. “Legacy APM tools do great on the vertical line with deep visibility for code they support;  meanwhile, microservices tools do well on the horizontal line, supporting a broad range of different technologies. Here’s the thing – being good on one axis doesn’t necessarily translate to value along the other because their data model is built a certain way. When I hear microservices APM, I think, ‘That’s what we do.’ [Instana has] both the depth of code-level visibility and the breadth of microservices support because that’s what we set out to do, solve the problem of ephemeral, dynamic, complex systems built around microservices.”

When talking about observability and application monitoring, it’s important to think about the different kinds of IT operations individuals and teams you have to deal with. According to Farrell “whether you’re talking about SREs, DevOps engineers or traditional IT operators, each has their own specific goals and data needs. Ultimately, it’s why a monitoring solution has to be flexible in what data it gathers and how it presents that data. 

Even though it’s important for modern monitoring solutions to recognize and understand complexity, it’s not enough. They must also do so programmatically, Farrell said, because today’s systems are simply too complex for a person to understand. “You add in the ephemeral or dynamic aspect, and by the time a person could actually create a map or understand how things are related, something will change, and your knowledge will be obsolete,” he said.

Modern solutions also have to be able to spot problems and deliver data in context. Context is why it’s practically impossible for even a very good and knowledgeable operations team to understand exactly everything that’s going on inside their application themselves. This is where solutions that support both proprietary automatic visibility and manually injected instrumentation can be valuable. Even if you have the ability to instrument an application with an automated solution, there still is room for an observability piece to add some context. “Maybe it’s a parameter that was passed in; maybe it’s something to do with the specific code that the developer needs to understand the performance of their particular piece of code,” Farrell said of the need for contextual understanding.

“That’s why a good modern monitoring tool will have its own metrics and have the ability to bring in metrics from observability solutions like OpenTracing, for example,” Farrell added. “Tracing is where a lot of this nice context comes out.  Like Instana, it’s important to have the ability to do both. That way, you provide the best of both worlds.”

To make the ongoing decisions and take the right actions to deliver proper service performance, modern IT operations teams really require that deep context. It’s valuable for ongoing monitoring, deployment or rollback verification, troubleshooting and reporting. While observability on its own can provide information to an individual or a few individuals. It is the monitoring tool that provides understanding into how things work together; that can shift between a user-centric or an application-centric view, and that can give you a framework to move from monitoring to decision-making to troubleshooting and then, when necessary, moving into reporting or even log analysis.

Farrell pointed out that “the APM piece is the part that ties it all together to provide that full contextual visibility that starts with individual component visibility and ultimately ties it all together for application-level performance and service-level performance.”

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Even experienced DevOps teams can struggle with the nuances of managing microservices in production–where a greater spotlight on network communications can introduce new operational challenges beyond those common to traditional architectures. But, by being smart about your software-based routing components, it’s possible to avoid or reduce the impact of roadblocks in the following areas.

Implementing Tracing and Monitoring

Even applications that are rigorously tested during development will exhibit anomalies in production. To address these, DevOps teams require effective tracing and monitoring tools to gain visibility into what’s happening within clusters at runtime.

As a strategy, developers should select routing solutions that integrate well with their existing monitoring and tracing backends. Remember that while a microservice may be developed by a certain internal team, once deployed it becomes available for others. If the application is instrumented to be traceable, compatible tracing tools can dynamically identify the source of service invocations and their call flows–enabling other teams to utilize the microservice. When the application is also designed to provide metrics, monitoring tools can usefully track the microservice’s key resource consumption metrics to assess the scalability of its subcomponents under load, and recognize any performance limitations that require attention.

Because all inter-service communications travel through software routing elements, you can strategically design your application to enable microservices tracing and monitoring data to be collected. Teams can then put this data to work to more easily overcome challenges when building out new capabilities.

Mitigating Communication Failures

Considering the myriad communications taking place within production microservices-based environments, it only makes sense that this architecture will be prone to communication failures in which microservices simply cannot be reached. Whether these failures occur as the result of an error by the container, host, a network partition, or a short-term interruption in the availability of the service itself, resilient safeguards must be in place to mitigate any impact to an application’s user base.

For example: When an instance fails, the load balancer can be used to automatically reroute requests to healthy instances, and then return traffic to the failed instance upon detecting that it’s again available. In scenarios where an entire microservice becomes unavailable, client services should leverage circuit breaking to stop requests, return an error and initiate a fallback response. Doing so avoids dangerous repetition of retry requests, which only eat up resources and can lead to cascading failures.

Each of these mitigation strategies could be accomplished by placing the necessary logic with each client. However, this technique would be both cumbersome to implement and prone to errors. The better approach to these challenges is to leverage routing solutions to perform instance health checks and circuit breaking, allowing DevOps teams to control how their applications respond to faults, rather than relying on logic at the network layer.

Handling Unanticipated Load Spikes

In production environments, loads can spike unexpectedly and for any number of reasons–ranging from simple sudden popularity with users to malicious distributed denial-of-service (DDoS) attacks. Whatever the root cause, applications must be well-prepared to withstand these events without failing.

To ready applications to handle load spikes, rate limiting is a simple (but powerful) technique that allows operators to specify the request rates to front-end services. Implementing this safeguard through a routing solution answers the challenge of avoiding downtime due to surprise changes in load intensity.

Securing Microservices Communications and Permissions

Security is always a constant concern when it comes to operating production deployments. However, microservices environments often have absolutely zero enforcement of internal network communications permissions. They also face the challenge of implementing proper encryption to secure sensitive data within both internal and external traffic.

While allowing any service to access any other offers flexibility, the risks are substantial. In order to safeguard microservices that access and expose sensitive data, it’s wise to limit access to only those client services with a vetted business justification. Routing technologies make it possible to enforce these secure policies using network segmentation.

At the same time, routing solutions can help businesses encrypt the in-flight transport of critical data, which is not only a best practice, but in many industries a necessity from a regulatory compliance perspective. Service meshes can offer the benefit of providing secure TLS encryption for all internal east-west traffic, while edge routers offer to encrypt external north-south using a provided certificate. Also of note for north-south traffic: combining routing technology with services such as Let’s Encrypt can serve to fully automate lifecycle management of trusted certificates, eliminating the need for human intervention entirely.

Achieving High Availability

Leveraging software routing technology to achieve high availability is more cost-effective and efficient than traditional hardware failover solutions. It offers the opportunity to build production microservices environments that are quite resilient. For example: Routing implementation should utilize a highly-available architecture that includes a horizontally scalable data plane and a separate fault tolerant control plane. While the data plane allows for adding instances as necessary to meet capacity and resilience needs, the control plane is prepared to tolerate failures and ensure that users experience seamless and uninterrupted uptime.

Deploying Across Multi-Cloud and Heterogeneous Environments

Larger enterprises often utilize deployments across multiple cloud and/or on-prem environments, each of which may rely on disparate container orchestration technologies. Highly portable routing technologies capable of supporting a range of orchestration tools can vastly reduce the challenges developers face in supporting multiple environments. This allows them to deploy the same familiar solution and a common routing layer model across deployments.

Routing technology is a key variable in determining the capabilities and ease with which DevOps teams are able to overcome challenges to the success of their production microservices deployments. By making a carefully considered choice in selecting a routing solution, teams can better prepare for these challenges–or even avoid them altogether.

The post Six Routing Challenges When Managing Microservices in Production and How to Avoid Them appeared first on Artificial Intelligence.

Chicago – January 29, 2020 – Instana, the leading provider of automatic Application Performance Management (APM) solutions for microservice applications, today announced new capabilities for monitoring the VMware vSphere Suite, as well as applications running on vSphere infrastructure.

Known for the ability to correlate infrastructure and application performance metrics and deliver actionable information to all stakeholders from development to operations, Instana’s latest release includes the ability to discover, map and monitor components running on VMware’s vSphere suite. Like the other supported infrastructure components Instana supports, application performance metrics are analyzed along with the new vSphere metrics.

“As organizations evolve their application environment to leverage the latest advancements in application and infrastructure, it’s critical that their operational tools provide the broadest flexibility and intelligent analysis, regardless of the infrastructure chosen,” said Chris Farrell, Technical Director and APM Strategist at Instana. “The addition of vSphere support to our cloud, container, orchestration and microservice platform monitoring allows users to understand how different architectural and infrastructure choices impact overall service levels and application performance.”

The vSphere announcement continues Instana’s legacy of excellence in monitoring applications and their underlying infrastructure together. Whether organizations run hosts physically, virtually or in the cloud, Instana enables them to quickly and easily see exactly how applications are performing and how the infrastructure is impacting those applications. With the ability to trace distributed requests end-to-end, the ability to see any and every possible infrastructure stack provides a complete picture of performance to Instana users.

Unlike other APM solutions, Instana fully automates the entire lifecycle of application monitoring including application discovery and mapping, monitoring sensor and agent deployment, and application infrastructure health monitoring. Whenever an application or infrastructure change occurs within dynamic applications, Instana recognizes the change in real time, instantly adjusting its application service maps, monitoring thresholds and health dashboards.

“Application migration is one particular use case for which Instana’s broad infrastructure and architectural support are a perfect combination to add value,” continued Farrell. “Whether migrating from monolith to microservices, physical to virtual hosts, or private to hybrid clouds, Instana’s automated discovery and performance monitoring provides the absolute quickest way to capture and compare different deployment options.”

The vSphere support and monitoring capabilities are available today as part of Instana’s automated APM solution. Learn more about Instana and their application monitoring solution at https://instana.com.

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The supply chain industry is facing data flooding at an accelerated rate. And this is hampering the organization’s ability to keep up with the upcoming insights and the inflows. 

The bottlenecks are becoming too prominent in the network.

While, decision-makers are trying to seek out effective ways to manage humongous amounts of data. In the end, they want the power of their data should benefit the company in a significant way.

The leaders want their supply chain to leverage the capabilities of advanced analytics that can streamline the process, make it more responsive, customer centric, and demand driven.

Why Risk Management has become a key factor in Supply Chain?

According to KPMG.US: 

1. 61 percent of leaders (compared with 37 percent of others) consider supply chain risk management very important. As they recognize the importance of capabilities that can enable them to gain greater visibility and predictability across their network. 
2. Secondly, supply chain leaders are nearly three times as likely as other companies to boost their investments in risk management by 20 percent or more in the next two years.

So, how is machine learning (ML) helping the industry in breaking through the bottlenecks? 

One of the major issues that the process is facing is its over-the-top reactive approach to the risks, which most of the time disrupts the overall operations of the network.

For it a more proactive and predictive approach is required to identifying and mitigating risk before it affects operations. And also has the power to eliminate many unnecessary financial and operational losses.

Some more problems that the industry is facing

According to KPMG.US:

• No real-time reporting – 56% of supply chain executives do not have access to real-time reporting. 
• Risk and compliance issues – 50% have limited knowledge of risk and compliance issues.
• End-to-end visibility – 13% do not have complete end-to-end visibility of supply chains.
• Cyber breaches – 80% of all cyber breaches occur in the supply chain.  

1) Machine Learning helps gain visibility into the supply chain to determine where forthcoming bottlenecks can occur.

This implies the workforce getting visibility into the process, equipment, and inventory that comprises of an operations phase.

A lot of information can be driven to improve productivity out of supply chain, inventory management, manufacturing process, distribution, and fulfillment. 

Machine learning has the capability to take into account various factors that the traditional forecasting model cannot predict.

It not only looks for patterns, but mines deeper into extremely complex data and identify the potential issues that can be the holdup on the process. ML provides better simulation models of future environments by analyzing complex data sets.

2) Another way ML is helping supply chain is by reducing costs and improving response time.

With accurate forecasting capabilities, organizations can easily optimize their processes. They can also pinpoint the challenging areas that display inefficiencies, while also projecting the roadblocks or bottlenecks in the future.

Supply chain companies with emerging technologies, such as artificial intelligence and machine learning, have inculcated the capability to respond quickly to the upcoming threats by detecting them quickly. The faster an organization has the option to respond; the more cost-effective is the solution.

3) Machine learning also has the power to better manage and maintain the assets. 

When ML is integrated into asset management, it can predict the need for repairs with the help of Internet of Things (IoT) sensors. When an equipment breakdown, the IoT sensors sends an immediate notification so that the supply chain process faces very little or no downtime.

Additionally, when these sensors are paired with ML, they can predict when failure is about to occur. These forecasting can lead to prior servicing of the equipment before any issue arises, therefore reducing the cost of damages.

It has been noted that maintained equipment lasts longer with no downtime. 

IoT gives an opportunity that is cost-effective in managing and maintaining the equipment that cannot be achieved with the human inspection. Also, IoT analysis can be done more frequently than human inspections.

4) Real-time monitoring with transparency.

Machine Learning provides real-time monitoring throughout the supply chain process. With the right reporting and tracking, we can monitor each and every aspect in the supply chain with ease.

This helps in identifying core inefficiencies that need to be resolved, as well as the requirement to optimize and streamline the supply chain processes.

ML also promotes transparency that provides a 360 clear view of the process. Making it easier to report any loss in the inventory within the supply chain and also reducing the chances of lost or damaged inventories.

To Conclude

By integrating machine learning along with the emerging technologies in supply chain management, companies can achieve a better understanding of the logistics and operations.

With IoT devices, organizations have collected huge volumes of data that can streamline and optimize the supply chain. Resulting in better maintenance and superior overall outcomes.

Amit Dua is the Founder of Signity Solutions. A tech-evangelist, he has an uncanny ability to synergize and build associations, thriving teams, and reputable clients. His vision is to grow his decade-old company as per global standards, and his deep analytical skills to foresee market trends, as well as global challenges.

The post Identify bottlenecks in your supply chain with machine learning appeared first on Artificial Intelligence.

Australia Post’s adoption of microservices and cloud via a digital transformation allowed it to move faster, but also created a complex environment of interdependencies and a need to establish “observability” across that.

When Post kicked off its digital transformation in 2013, the overarching goal was speed.

“Our time to market was incredibly slow at the time,” recalls head of platform engineering Andrew Nette.

“It was taking up to 50 days for code to reach production, so environments were really slow in spinning up.”

The organisation set up a digital delivery centre internally to help internal application owners transform. 

The centre was a predecessor to platform engineering, where 30 engineers now assist Post’s delivery teams to get products to market quickly, and then support the application once in production.

Nette told last month’s New Relic FutureStack19 conference that Post re-architected applications into arrays of cloud-hosted microservices.

That structure worked insofar as it reduced the time needed to get code into production.

“We were quite successful,” Nette said.

“We were able to get things into production in about 12 minutes, so our time to market has significantly improved.

“[But] we were using a microservices architecture, so our number of things in production scaled out as well, which means our environment got a lot more complex, and we had to think about the way we monitored those applications differently.”

Establishing visibility

The problems appeared “after 2013”, not long into the transformation.

“Microservices were proliferating, and it was really difficult for us to keep up and keep the focus on the number of microservices that we had,” Nette said.

Post’s challenge quickly became establishing “observability” over the transformed environment, and Nette said the organisation had spent “a lot of time” getting to a point of “100 percent visibility.”

“Observability is more than just monitoring – it’s the ability to understand what’s happening inside your application or inside your system through all its dependencies,” Nette said.

“Being able to understand if there’s an issue in the network layer, infrastructure layer, application layer, and even out to third party services that you’re utilising.

“If you can do that, then you have a truly observable system, and if you can do it all in one place then … you have your single pane of glass where you can see all of your issues.”

Nette continued: “If I think about the way Post’s observability platform or our monitoring ecosystem developed … we’ve spent a lot of time trying to develop our ecosystem so that we have 100 percent visibility and no gaps.”

Tool-wise, Australia Post uses a mix of “New Relic APM [application performance monitoring], synthetics, Sumo Logic event [management], even Bash scripts if that was what was required to get the visibility.”

Nette described finding the right mix of tools as Post’s “Goldilocks zone” – “not too many, not too few, just the right number.” 

“The tools [also] need to provide value and not add more toil,” he said.

The ‘Vanilla Ice rule’

Post worked closely with delivery teams to instrument all parts of the environment.

“Collaboration was really important,” Nette said.

“We follow the Vanilla Ice rule: ‘stop, collaborate and listen’.

“It was a two-way conversation with delivery teams. They needed to understand why we were trying to do the things we were doing, and we needed to appreciate that they had other work, they were delivering features.”

One of the main things to monitor were Post’s customer-facing APIs, which large enterprise customers use to directly integrate with Post’s parcel delivery systems.

Post built all its APIs using a standard pattern that “had API health checks built in”, Nette said.

“It was really important that we worked with the delivery teams and the developers to make sure that those health checks were instrumented correctly, that they were calling their dependencies and that the dependencies were showing the correct states,” Nette said.

“Once we had a screen where all of our APIs were calling all of their dependencies, we could very quickly identify when there was an issue, and once we had that, we significantly reduced our mean time to identify issues and also our mean time to resolve.

“It was a big win, and it showed effective collaboration was really helpful.”

Australia Post used an unspecified set of open source tools to create that “API health check dashboard”, and then other “observability” dashboards useful to platform engineering.

It then started creating dashboards for individual delivery teams that showed them only the alerts they needed to see for the code and services they maintained.

In part, these dashboards helped the delivery teams to “evolve their DevOps capabilities”, Nette said, instead of fully relying on operations for alerting and support.

Platform engineering at Australia Post now offers “hybrid” support options to delivery teams.

“There’s full end-to-end support that we provide with delivery teams providing a third level [of] escalation, all the way up to the delivery teams providing full DevOps and doing all of the support [themselves], and [us just] providing advice where required,” Nette said.

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Enterprise AI service provider DataRobot has unveiled MLOps, a machine learning operations (MLOps) solution for deploying, monitoring, and managing machine learning models across the enterprise.

MLOps combines DataRobot’s existing model management and monitoring solution with capabilities from MLOps category leader ParallelM, which DataRobot acquired in June.

DataRobot’s new MLOps offering provides a centralised hub for deployment, monitoring, and governance of models created from a variety of tools.

As a result, organisations will be able to cut the time it takes them to deploy and scale machine learning-based services in production.

Despite the investments in data science teams and infrastructure, many companies have not been able to derive measurable value from AI projects.

According to industry analysts, only a fraction of machine learning models make it into production.

The few models that do make it into production do not have the necessary monitoring and governance that’s required to ensure they are accurate and consistent throughout changing market or environmental conditions.

Effective and responsible use of AI requires a modern and centralised system to automate the deployment, monitoring, management, and governance of both models and projects through every step of the AI production lifecycle.

DataRobot’s previous model management and monitoring solutions, embedded within its DataRobot automated machine learning product allowed customers to operationalise models and monitor their performance.

ParallelM’s technology deploys and manages machine learning models built on a variety of machine learning platforms onto customer-managed environments, including Kubernetes and Spark.

By combining the two, DataRobot’s new MLOps product offers real-time monitoring and centralised management and governance for models created using machine learning platforms, frameworks, and languages, making it an open platform.

“We created ParallelM and MLOps to help the industry accelerate the path to value that should be derived from machine learning by scaling the operationalisation of models across any execution environment,” says DataRobot MLOps managing director Sivan Metzger and former ParallelM CEO.

DataRobot customers now have a single platform to deploy models and see the status of all production models independent of where they were created or where they are deployed.

Real-time dashboards allow users to identify models that should be re-trained or replaced to prevent production issues or poor business performance.

In addition, centralised and embedded governance allows organisations to maintain control over AI projects, comply with government regulations, and reduce risk from access or changes to production models.

“With monitoring dashboards that contain real-time information about all AI and machine learning models across the enterprise, this product supports the AI deployment plans of our customers,” says DataRobot product and customer experience senior vice president Phil Gurbacki.

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