AIOps stands for Artificial Intelligence for IT Operations. It refers to the application of artificial intelligence (AI) insights to IT and network operations, aiming to simplify and automate these operations to a large extent. AIOps leverages big data and machine learning technologies to collect historical and real-time telemetry data from systems as well as data from IT and network operations tools. This data is then analyzed and correlated using ML models or AI engines to identify operational issues, recommend remedial actions to engineers, and trigger automated responses, thereby enabling more proactive and data-driven decision-making in IT operations​​.

Why Is AIOps Important? 

AIOps allows IT organizations to further optimize their available resources and maintain scalability, which is especially important in an environment where IT teams are being increasingly overwhelmed with the growing number of AI projects that require a lot of customization and maintenance. 

AIOps + DataRobot

The DataRobot AI Platform is an open, complete AI lifecycle platform that provides a unified experience to build, govern, and operate your entire generative and predictive AI landscape. DataRobot is related to AIOps in a few ways.

The DataRobot AI Platform capabilities allow IT organizations to quickly deliver AIOps use cases, using AI Experimentation and AI Production features that streamline the AI lifecycle for both generative and predictive AI use cases. From anomalous network activity detection and application downtime forecasting to network outage predictions and other infrastructure monitoring applications – organizations build AI use cases using advanced DataRobot capabilities in the fraction of the usual time.  

Additionally, DataRobot AI Production capabilities allow organizations to automate and improve processes and practices around management and governance of AI infrastructure that they often own, including model deployment, monitoring, and governance. DataRobot MLOps and LLMOps features deliver full control over all of the generative and predictive AI assets in a single place, with transparent ownership and governance. 

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AI Observability refers to the ability to monitor and understand the functionality of generative and predictive AI machine learning models throughout their lifecycle within an ecosystem. It’s an essential aspect of MLOps (Machine Learning Operations) and, especially, Large Language Model Operations (LLMOps), aligning with DevOps and IT operations to ensure the seamless integration and performance of generative and predictive AI models in real-time workflows. Observability enables the tracking of metrics, performance issues, and outputs generated by AI models, providing a comprehensive view through an organization’s observability platform.

Key components of AI Observability include:

1. Metrics and Performance Monitoring: Monitoring model performance metrics and other key indicators to ensure that AI models are operating as expected. This includes real-time monitoring and root cause analysis to troubleshoot and address performance issues.
2. Model Monitoring and Management: Utilizing observability tools to track the performance and functionality of machine learning models, algorithms, and pipelines, ensuring their optimal operation throughout the lifecycle.
3. Visualization and Dashboards: Providing dashboards for visualization of metrics, datasets, and actionable insights, aiding in the analysis and interpretation of AI model performance.
4. Automation and Lifecycle Management (ALM): Automating various stages in the AI model lifecycle, from data preparation to model deployment, and ensuring smooth workflows.
5. Data Quality and Explainability: Ensuring the quality of datasets used, and providing explainability for AI models’ decisions, enhancing trust and understanding among stakeholders.
6. Generative AI and Advanced Algorithms: Employing advanced algorithms and generative AI techniques to enhance the capabilities and performance of AI models.
7. User and Customer Experience: Ensuring that the AI observability solution enhances the user and customer experience by providing a robust platform for data scientists and other stakeholders to interact with AI and ML systems.
8. AIOps and Integration: Integrating AI observability with AIOps (Artificial Intelligence for IT Operations) and other observability solutions, enabling a unified approach to managing application performance and infrastructure.
9. APIs and Telemetry: Utilizing APIs for seamless integration and collecting telemetry data to provide deeper insights into the operation and performance of AI models.
10. Community and Ecosystem: Building a supportive ecosystem around AI observability that includes data science professionals, end-users, and a variety of observability tools and platforms.

Why Is AI Observability Important? 

AI Observability is crucial for organizations looking to leverage AI and machine learning technologies, ensuring that they can efficiently manage, monitor, and gain insights from their generative and predictive AI models, thereby driving better decision-making and enhanced customer experiences. It’s becoming increasingly important with the introduction of generative AI into the enterprise ecosystem, because of the risks associated with the possibility of returning incorrect responses, “hallucinations”. 

AI Observability + DataRobot

AI observability capabilities within the DataRobot AI platform help ensure that organizations know when something goes wrong and understand why it went wrong and be able to intervene to continuously optimize the performance of AI models. By tracking service, drift, prediction data, training data, and custom metrics, enterprises can keep their models and predictions relevant in a fast-changing world. 

DataRobot and its MLOps capabilities provide world-class scalability for model deployment. Models across the organization, regardless of where they were built, can be supervised and managed under one single platform. In addition to DataRobot models, open source models deployed outside of DataRobot MLOps can also be managed and monitored by the DataRobot platform.

It is not enough to just monitor performance and log errors. To get a complete understanding of the internal state of your AI/ML system, you also need visibility into prediction requests and the ability to slice and dice prediction data over time. Not knowing the context of a performance issue delays the resolution, as the user will have to diagnose via trial and error, which is problematic for business critical models.

This is a key difference between model monitoring and model observability: model monitoring exposes what the problem is; model observability helps understand why the problem occurred. Both must go hand in hand.

With model observability capabilities, DataRobot MLOps users gain full visibility and the ability to track information regarding service, drift, prediction and training data, as well as custom metrics that are relevant to your business. DataRobot customers now have enhanced visibility into hundreds of models across the organization. 

To quantify how well your models are doing, DataRobot provides you with a comprehensive set of data science metrics — from the standards (Log Loss, RMSE) to the more specific (SMAPE, Tweedie Deviance). But many of the things you need to measure for your business are hyper specific for your unique problems and opportunities — specific business KPIs or data science secrets. With DataRobot Custom Metrics, you can monitor details specific to your business.

After DataRobot has delivered an optimal model, Production Lifecycle Management capabilities of the platform help ensure that the currently deployed model will always be the best one even as the world changes around it. MLOps delivers automated strategies to keep production models at peak performance, regardless of external conditions.

For example, DataRobot Data Drift and Accuracy Monitoring detects when reality differs from the situation when the training dataset was created and the model trained. Meanwhile, DataRobot can continuously train challenger models based on more up-to-date data. Once a challenger is detected to outperform the current champion model, the DataRobot AI platform notifies you about changing to this new candidate model.DataRobot also allows organizations to solve the generative AI confidence problem by pairing each generative model with a predictive AI guard model that evaluates the quality of the output. This framework has broad applicability across use cases where accuracy and truthfulness are paramount.

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Generative AI, short for Generative Artificial Intelligence, is a branch of artificial intelligence focused on creating new content. It leverages machine learning algorithms, particularly deep learning, to generate data for use in a business application. Generative models, including Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs), are central to this type of Artificial Intelligence.

A notable embodiment of generative AI is in the creation of large language models (LLMs) like OpenAI’s GPT-3 (Generative Pre-Trained Transformer 3) and GPT-4. These models are trained on vast amounts of data using transformer architectures, enabling them to generate coherent and contextually relevant text. Furthermore, enterprise-level AI platforms can help facilitate the integration and utilization of generative AI in modern software applications and services.

Why Is Generative AI Important? 

Generative AI can perform many tasks, including creating and summarizing documents, answering questions, brainstorming, editing prose and code, and performing simple planning. From productivity gains to finding new routes to revenue generation, generative AI will radically transform how we work. AI copilots will not just be the norm but will be expected, handling everything from simple tasks, such as customizing travel itineraries to summarizing lengthy documents to complex commands and actions like assisting in coding.

In the enterprise context, two key ways to think about generative AI opportunities are optimizing operating costs and aligning it to key growth levers. 

40% of all working hours can be impacted by LLMs like GPT-4. – Accenture

Generative AI will help enterprises optimize operating costs by increasing the productivity of existing teams and also driving down cost levers, including:

Customer Self-Service

• Support 
• Administrative request handling
• Inquiry routing and triage
• New product recommendations and education 

Internal Business Communications

• Content generation and ghost writing, such as reports, presentations, briefings, and updates

Knowledge Retrieval and Summarization

• Strategy, R&D, and mergers and acquisitions research
• Internal knowledge bases and expert agents
• Financial and analyst report generation or summarization

Operational Efficiencies

• Agent assistance, request processing
• Report summarization and retrieval
• Data entry assist
• Coder productivity, including business analyst and back office workflows

Growth levers can open new revenue streams or drive more topline outcomes. Some of these include:

New Products and Experiences

• Chat-based agents 
• Shopping assistants with dynamic recommendations 
• New product education

Enhancements to Existing Products and Experiences

• Natural language product manual and support interfaces

Marketing and Sales

• Content generation
• Ghostwriting
• Personalized outreach
• Knowledge retrieval and summarization
• Sales call preparation and intelligence gathering

Organizations should also factor in emerging use cases for specific industries that will help them transform the organization with generative AI. The ability to successfully apply these use cases will be highly dependent on the complexity of the use case and the general AI maturity of the organizations. You don’t want to over index too far on one end or the other. 

Generative AI + DataRobot

DataRobot is the only end-to-end generative AI platform that allows organizations to build, operate and govern enterprise-grade generative AI solutions, safely and reliably.

DataRobot allows organizations to work with all the best tools and techniques, across cloud environments, in one unified experience, due to its open, flexible, and multi-cloud architecture. With DataRobot Generative AI, organizations can safeguard data privacy and control all financial aspects of their generative projects. Enterprises can confidently deploy and maintain safe, high-quality generative AI applications and solutions in production with advanced LLMOps capabilities built into the platform. 

DataRobot Generative AI gives enterprises the confidence to achieve real-world value with generative AI, enabling them to rapidly build with optionality, govern with full transparency and oversight, and operate with correctness and control. Designed to integrate with your existing tools, processes, and infrastructure, it fuels the delivery of real-world value from your AI initiatives, with the adaptability to evolve as demands shift and the enterprise control to scale with confidence.

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LLMOps (Large Language Model Operations) is a subset of Machine Learning Operations (MLOps) tools, practices, and processes, tailored to large language models’ (LLMs’) unique challenges and requirements. LLMOps specifically focuses on managing and deploying large language models (like GPT-3.5) in production systems. LLMOps includes fine-tuning language models for specific applications, handling large language model inference at scale, monitoring large language model performance, and addressing ethical and regulatory concerns related to large language models.

Why Is LLMOps Important? 

This operational domain is critical for organizations looking to safely and confidently derive value from their generative AI projects, especially in a landscape where AI production processes haven’t kept pace with the rapid development and adoption of generative AI and LLMs.

LLMOps aims to tackle a number of organizational, technical, and procedural problems that arise with the growing popularity of generative AI: 

• The hodge-podge of AI/ML tooling across multiple platforms, technologies, languages and frameworks used to build models, which can crush production processes, hurt productivity, increase risks around governance and compliance, and elevate cost.
• Internal software development and non-data science teams are aggressively building out generative AI solutions using open source tooling, creating a “Shadow IT” problem for data leaders and IT leaders, with lack of visibility and governance. These “non-traditional” model builders may not be aware of AI lifecycle management best practices and have trouble getting their models into production.
• Improved prediction accuracy requires use-case specific vector databases, which increases the proliferation of “narrow” databases throughout your infrastructure. The best practice for increasing prediction accuracy is to create a specialized vector database for each use case, increasing database sprawl and increasing governance challenges.
• As the number of generative and predictive model assets increase exponentially within your technological infrastructure, so does the complexity in managing, monitoring, and governing these models to ensure top performance.

LLMOps tools and practices address these challenges through a variety of model observability and governance solutions. 

These, when applied holistically, allows organizations to:

• Deploy and run generative AI models in production
• Monitor generative models and take actions to improve model metrics
• Learn from users and continuously optimize/improve generative outputs
• Test generative model performance and generate documentation
• Register generative models and manage them across deployment platforms
• Create an audit trail for each generative model and approve changes

LLMOps + DataRobot

DataRobot offers a complete generative AI lifecycle solution that allows you to build, monitor, manage, and govern all of your generative AI projects and assets.

It enables “confidence” in the correctness of responses by combining generative and predictive AI, where predictive models are used to verify and evaluate your generative responses, with confidence scoring, evaluation metrics, and guard models.

With DataRobot, your users can evaluate and rate generated responses for accuracy, so your generative AI applications can learn from their feedback and improve confidence scores.

DataRobot LLMOps provides multiple LLM-specific metrics, out of the box, like anti-hallucination metrics and content safety metrics, like prompt toxicity.

DataRobot AI Registry gives you a 360-degree view of all AI assets no matter where the model was built or hosted, reducing “lock-in risk”:

• Consolidate, organize, and version multiple generative and predictive AI artifacts from any source, regardless of platform or cloud, into a single source of truth and system of record.
• Manage vector databases, LLMs, and prompt engineering strategies neatly together.
• Utilize a unified governance and policies and roles-based access control tied to your AI assets, not your data warehouse, lake or hosting platform.

You can monitor all your generative AI assets in one dashboard, automatically test new challenger models and “hot swap” out old models for the new champion model without disrupting your business processes. Metrics include:

• Data Drift: drift metrics to alert you to the potential causes of model degradation 
• Accuracy: variety of prediction performance metrics for any model
• Custom performance metrics: user-defined metrics for all your business needs (including cost)
• Fairness monitoring: bias and fairness metrics
• Data quality checks: user-defined, rules-based data quality ranges with custom alerting

You can use DataRobot as a central location for automatic monitoring and alerting for all of your generative AI assets, regardless of where they are built/deployed, with metrics including:

• Service Health: Operational metrics for deployment (eg volume, response times, mean and peak loads, and error rates)
• SLA monitoring: SLA metrics regardless of model origin.
• Prediction archiving: Archive past predictions for analysis, audit, and retraining
• Custom operational metrics: User-defined metrics including LLM cost
• Maintain cost control: Avoid budget overruns from increased compute with clear monitoring and management of LLM usage.
• Real-time alerts: Ensure models continue to create value with automatic model performance alerting

DataRobot helps you prepare for pending regulations with our generative AI trust framework. Utilize DataRobot Bias and Fairness capabilities to prevent generative models from inadvertently learning biases and creating unfair outputs. 

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Natural language processing (NLP) is a set of artificial intelligence techniques that enable computers to recognize and understand human language. It helps make computers more easily accessible for humans. Natural language processing uses computer science and computational linguistics to bridge the gap between human communication and computer comprehension. It does this by analyzing large amounts of textual data rapidly and understanding the meaning behind the command. Natural language processing enables computers to comprehend nuanced human concepts such as intent, sentiment, and emotion. It is similar to cognitive computing in that it aims to create more natural interactions between computers and humans. 

Why is Natural Language Processing Important?

Humans comprehend words, phrases, and sentences largely through context and familiarity. For example, when we hear a certain phrase in one context, it may inspire a certain understanding. However, when we hear it around other words, it may take on a completely different meaning. This is how we can have sophisticated conversations with relative ease. This is the same concept behind natural language processing. Computers analyze text, words, or other data to parse the intent of the speech. The advantage is that computers can analyze much larger sets of data much faster.

Natural language processing applications include the following:

• Smart assistants like Alexa or Siri rely on natural language processing algorithms to function. These tools use voice recognition to understand what is said and match it to a useful response. Initially, this was used to identify someone alerting the assistant with “Hey Siri” or “Hey Alexa.” More recently, these tools have been able to understand context and create shortcuts or make other improvements. This new processing is also how these tools can identify jokes and respond with humorous answers. 
• Transcription and translation on our phones are becoming increasingly common. A good example of this is when you get a call during a meeting. Your phone’s ability to transcribe a voicemail helps you identify important messages. Similarly, translation tools help us navigate new countries or new situations. Previously, people had to carry around translation dictionaries. Nowadays, we can simply speak into our phones and be understood in a matter of seconds. 
• Spam and email filters rely heavily on natural language processing techniques to scan and classify emails. They analyze content for language common to spam or phishing emails. Examples of this include the use of financial terms, typographical errors, poor syntax or grammar, and threatening language. Another example is your inbox recognizing an email as primary, social, or promotional in nature. This filtering helps keep inboxes manageable and pushes more relevant emails to the forefront. 
• Predictive text, along with autocorrect and auto-complete, helps us save time and increases accuracy in texts and emails. These tools use NLP to function and improve over time. For example, the more you use your phone, the better it will get at predicting the words you are starting to type. Predictive text can  not only help semi-automate several repetitive steps, but can also ensure accuracy. Similarly, NLP can help improve search engine results and web page suggestions. 
• Data and text analysis are crucial tools for business intelligence and other companies. Amassing data is meaningless unless it can be analyzed in actionable and meaningful ways. Natural language processing can help businesses enhance processes by using natural language to segment data. NLP can also be used to analyze social media comments and get more accurate understandings of customer interactions, for example. 

These natural language processing examples demonstrate the value of this software discipline. This is due largely to the fact that natural language processing techniques, combined with machine learning, improve over time. In fact, deep learning in natural language processing can take these applications in bold new directions. 

Natural Language Processing + DataRobot

DataRobot AI platform features a variety of NLP capabilities. If text features are detected in your dataset, DataRobot identifies the language and performs necessary preprocessing steps. For feature engineering with text data, DataRobot automatically finds, tunes, and interprets the best text mining algorithms for a dataset, saving both time and resources.

DataRobot’s capabilities include—but are not limited to—tokenization, data cleaning (stemming, stop word removal, etc.), and application of various vectorization methods. DataRobot AI platform supports n-gram matrix (bag-of-words, bag-of-characters) analytical approaches, as well as word embedding techniques, such as Word2Vec and fastText with both CBOW and Skip-Gram learning methods. Additionally, the platform can perform Naive Bayes SVM and cosine similarity analysis.

DataRobot is continuously expanding its NLP capabilities, including the latest language representation models like BERT (Google’s transformer-based de-facto standard for NLP transfer learning). Tiny BERT (or any distilled, smaller, version of BERT) is now available with certain blueprints in the DataRobot Repository. These blueprints provide pretrained feature extraction in the NLP field.  

For visualization, there are word clouds for improved text analysis that allow users to see which words are impacting predictions made by a model (for binary classification, multiclass classification, and regression), view class-specific word clouds (for multiclass classification projects), filter out common stop words (for, was, or, etc.), and much more. 

To learn more about DataRobot AI platform and its NLP capabilities, schedule a demo today.

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Model monitoring is the close tracking of the performance of ML models in production so that production and AI teams can identify potential issues before they impact the business. A robust MLOps infrastructure should be able to proactively monitor for service health (that is, its  availability),assess data relevance (the variance between the data models were trained on and the live data it is scoring against across all features), model performance (model accuracy), trust elements such as fairness and bias, and of course, business impact.  

Why Is Model Monitoring Important?

The predictive performance of a model typically starts to diminish as soon as it’s deployed. For example, someone might be making live predictions on a dataset with customer data, but the customer’s behavioral patterns might have changed due to an economic crisis, market volatility, natural disaster, or even the weather. Models trained on older data that no longer represents the current reality might not just be inaccurate, but irrelevant, leaving the prediction results meaningless or even harmful. Without dedicated production model monitoring, the user or business owner cannot know or be able to detect when this happens. If model accuracy starts to decline without detection, the results can impact a business, expose it to risk, and destroy user trust.

Model Monitoring and DataRobot

DataRobot machine learning operations (MLOps) is a key pillar of the DataRobot enterprise AI platform. It provides a scalable and governed means to rapidly deploy and manage machine learning applications in production environments. The four pillars of DataRobot MLOps are model monitoring, simplified model deployment, production lifecycle management, and production model governance. With DataRobot MLOps, models built on any machine learning platform or in practically any language can be deployed, tested, and seamlessly updated in production on any runtime environment and managed from a single location. There are controls in place to natively integrate with most access, log, and resource management services to seamlessly integrate with any production environment and for legal and regulatory compliance.

Remote Model Monitoring Architecture

The architecture includes three layers:

MLOps Library

The MLOps Library can be invoked by a model’s scoring code to track service, drift, and accuracy metrics. The library is responsible for capturing metrics and posting them to the configured channel. The library supports common languages, such as Java, Python, and R.

The MLOps Library can be used to report to a highly scalable channel, such as Google Cloud Pub/Sub or Amazon SQS, for scalable, near real-time monitoring. Or it can be used to report metrics outside of the prediction path.

Channel

There are multiple channels available to pass metrics captured by the MLOps Library as the model makes predictions. Passing metrics over a channel instead of directly gives you more configuration options and provides support for disconnected models that do not have network connectivity to the MLOps application.  

The following channels are available:

● File system

● RabbitMQ

● Amazon SQS

● Google Pub/Sub

MLOps Agent

DataRobot is the first in the industry to use agents to collect and analyze monitoring statistics from models running practically anywhere. These statistics are available in the customer’s MLOps center of excellence (CoE). The MLOps Agent is a lightweight software program designed to run on remote hosts that monitors an application’s behavior, including its events, performance, and availability. The DataRobot MLOps Agent supports any model, written in any language, deployed in any environment, including:

● Models developed with open-source languages and libraries and deployed outside of DataRobot (Amazon Web Services, Microsoft Azure, Google Cloud Platform, on-premise).

● Models running on distributed compute platforms, such as Spark.

● DataRobot models downloaded and deployed on organization infrastructure.

● Stored procedures running in modern databases.

The MLOps Agent watches the channel for updated metrics, collects them, and passes them to the MLOps application. This capability provides instant visibility into the performance of models that are running anywhere in your environment. The MLOps Agent can be deployed anywhere it has access to the channel—in the prediction environment or on the edge. Advanced capabilities may include features that have been built into the MLOps solution to help customers increase their trust in their models in production even more. 

For example, in AI the principle of humility dictates that models should be able to inform not only when predictions are possibly losing accuracy or relevance, but also when they’re not confident in the quality of the fairness of their predictions. When this information is captured and sent to a centralized server, it’s much easier to detect and diagnose issues occurring in any remote application. DataRobot MLOps and its model monitoring capabilities make it possible for organizations to run their models in their preferred infrastructure and quickly get metrics on any deployed model in a single, centralized environment.

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With organizations relying on data from multiple sources, it becomes increasingly important to have a consistent set of standards for managing data from collection to usage. Data management refers to the practices that companies follow when collecting, storing, using, and deleting data. An effective data management process includes the establishment of a unified technological infrastructure and a governing framework that outlines an organization’s preferred data-handling protocols. Having the right data management solutions in place can make it simpler and faster to process and validate large quantities of data that they receive and make it easier for companies to extract real-world value from their data-based insights.

Why Is Data Management Important?

Managing data can be a complicated ordeal, especially as companies seek to gain value using data from multiple sources. The goal of most companies’ data management strategy is to establish a consistent set of standards for handling data across an organization that applies to all applications and business operations.

As organizations seek to consume data at a considerably faster pace, having data management solutions empowers organizations to gain data-based insights faster and get deeper insights into a wide range of use cases. These insights can help organizations respond more effectively to a wide range of scenarios, such as identifying customers at the highest risk of churn or determining the best products to offer to a customer based on their specific needs. Having strong data management practices and tools in place helps organizations to quickly convert data into actionable plans.

Data management tools can be used to help organizations understand which users and administrators have been handling data. This, in turn, can help organizations remain compliant with existing laws, such as the EU’s Global Data Protection Regulation, (GDPR) by ensuring that any data used by the organization is kept secure and private, with the right access management procedures in place. Having a strong data management strategy in place can also help companies understand the best practices to employ when storing data using on-premise resources or across multiple clouds.

Data Management + DataRobot

DataRobot added AI Catalog to its platform last year following its acquisition of data collaboration platform Cursor. With AI Catalog, companies gain stronger data management capabilities, such as access to data assets from a governed AI environment and strictly monitored sharing permissions to ensure machine learning applications are kept trustworthy. Users can also search for and connect to data assets from any location, including data lakes, databases, in-cloud, or on-premise resources. AI Catalog provides access to a collaborative environment for enterprise AI that enables companies to search for trusted data assets as they pursue their AI ambitions.

The availability of an AI catalog provides organizations with a critical advantage as they work to become AI-driven. By implementing strong data management solutions, organizations can automate their AI pipelines to more seamlessly collect, organize, and validate their data. Most importantly, this level of automation can also result in organizations gaining actionable insights from their collected data and pinpoint opportunities to improve efficiencies, reduce costs, deliver more personalized services, increase revenues, and find opportunities to expand into valuable new markets.

Sources: 1, 2, 3, 4, 5

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In AI and machine learning, predictive maintenance refers to the ability to use volumes of data to anticipate and address potential issues before they lead to breakdowns in operations, processes, services, or systems. Having strong predictive maintenance tools in place enables businesses to anticipate when and where potential breakdowns in service can occur and move to respond to them in order to prevent potential interruptions in services.

Predictive Maintenance vs. Preventive Maintenance

Predictive maintenance is similar to preventative maintenance. Both are types of scheduled maintenance. However, preventative maintenance involves general best practices for care of equipment, without knowing any specifics of how the item was used. Predictive maintenance utilizes actual measured usage, operating conditions, and equipment feedback to generate individualized predictions of impending issues.

Why Is Predictive Maintenance Important?

Implementing predictive maintenance services enables organizations to maintain critical assets for as long as possible in order to ensure that systems remain operational. This allows organizations to use their existing data to stay a step ahead of potential breakdowns or disruptions and address them proactively, instead of reacting to issues as they arise. This includes:

• Lowering costs by reducing unplanned downtime, fewer redundant inspections and ineffective preventative maintenance measures. Savings are incurred from increased productivity and decreased labor and materials costs.
• Reduced equipment lifecycle costs through improved performance and extended equipment life.
• Indirect benefits, including improved quality, reduced rework, reduced defects, improved safety and increased energy efficiency.

According to data from McKinsey, predictive maintenance tools can reduce manufacturing machine downtime by 30 to 50 percent and increase machine life by 20 to 40 percent. Manufacturers can also improve their operations and keep their supply chains intact.

Predictive Maintenance Use Cases

Predictive maintenance holds significant potential to enhance the efficiency and productivity of several verticals that rely on assets requiring frequent repair.

Manufacturers can use predictive maintenance techniques to implement safeguards that notify the right people when a piece of equipment needs to be inspected. Using their existing historical data, such as electrical current, vibration, and sound generated by equipment, manufacturers can build models to anticipate the likelihood of a potential breakdown before it occurs. These models can identify which equipment is at greatest risk of failing, allowing maintenance teams to respond accordingly. The insights from the models fit to historic data can also help point to the root cause of the problem and inform operators of underlying issues.

Supply chain operators can also use predictive maintenance analytics to plan around equipment downtime and potential disruptions. Model insights can inform the supply chain team how long an asset, system, or component could be offline, allowing them to plan accordingly.

Original equipment manufacturers (OEMs) can provide predictive maintenance as a service. By collecting data from multiple customers’ equipment, OEMs can build models with data collected from the wider customer base to provide individual customers with insights and equipment-specific maintenance schedules.

Government agencies can also benefit from implementing proper predictive maintenance techniques. Automated machine learning for predictive maintenance can help officials understand when new parts, components, and overhauls will be required for military equipment like helicopters, aircraft, and weapons systems. Using predictive maintenance models that rely on AI and machine learning can help public sector agencies operate more efficiently, keep expensive assets in usage longer, and enhance supply chain operations.

DataRobot can help government and other public sector officials address time-consuming Failure Mode, Effects, and Criticality Analysis (FMECAs) by running models that can predict patterns based on different assets’ environments. These predictive maintenance models can lead to more accurate asset and component lifespans and can be deployed for other use cases, including accident analysis and labor optimization.

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Explainable artificial intelligence or explainable AI (sometimes known as the shorthand “XAI”) refers to the ability of algorithm or model owners to understand how AI reached its findings by making AI technology as transparent as possible. With explainable AI – as well as interpretable machine learning – organizations gain access to AI technology’s underlying decision-making and are empowered to make adjustments as needed.

Why Is Explainable AI Important?

A common concern among potential artificial intelligence adopters is that it is often unclear how the technology reaches its conclusions. When AI algorithms are locked in a “black box” preventing humans from analyzing how the findings were reached, it can be difficult to trust the technology because human experts are unable to explain the AI’s findings.

Being able to explain AI can help organizations establish greater trust with clients, customers, and invested stakeholders. One of the key benefits of being able to have artificial intelligence explained simply is that it can help technology owners determine if human bias influenced the model. This can be especially important when the AI is being called upon to make life-or-death decisions, such as in a hospital environment where medical professionals may have to explain to patients and their families why certain decisions were made.

Take the example of a healthcare system that was designed to determine whether a patient will need additional medical resources by assigning a “commercial risk score” to determine the level of care management a patient should receive. When medical professionals gained access to proprietary data, they discovered that the algorithm was more likely to measure healthcare costs rather than illness. As a result, researchers learned that zip codes were a leading predictor of patients’ hospital stays. The zip codes that correlated to longer hospital stays tended to be in poor and predominantly African-American neighborhoods. When mapped by commercial risk scores versus the number of active chronic conditions split by race, researchers discovered that African-American patients with the same number of chronic health problems received lower commercial risk scores and, as a result, less care.

In other words, explainable AI helped healthcare providers pinpoint how human bias built directly into their AI was impacting patient care. In addition to the healthcare market, this level of transparency can allow individuals impacted by the European Union’s Global Data Protection Regulation (GDPR) or the U.K.’s Data Protection Bill to exercise the “right to explanation” as to how data was used by an algorithm. These are just a few examples of how explainable AI can make regulated markets – banking, healthcare, and insurance, to name a few – more transparent and trustworthy.

Explainable AI + DataRobot

DataRobot offers a model-agnostic framework that enables owners to interpret results, make informed adjustments, and access easy-to-use and state-of-the-art interpretation techniques for all their models. This promotes consistent techniques across all models instead of different approaches for different models that can result in biased decision-making. Having this level of transparency empowers firms to meet the “right to explanations” for end-users, provide stakeholders with explanations into a model’s logic, and improve compliance with existing regulations.

DataRobot’s team of consumer facing data scientists can not only help your organization become AI-driven, but driven by explainable AI. Meanwhile, our R&D team is constantly growing and testing its library of AI and machine learning models and provides documents outlining each step a model uses to reach its conclusions to help you trust your AI and explain it to your stakeholders. Here are some key features that DataRobot can offer:

• Feature Impact: Shows how much a model relies on each individual feature to reach its decisions.
• Feature Effects: Enables users to delve deeper into models and investigate how feature values influence the model’s decision on a global level.

• Prediction Explanation: Highlights the features variables that impact each model’s decision outcome for each record and the magnitude of different features for each.

DataRobot automates several standard data processing steps within each model blueprint and makes all these transformations transparent. This ensures that AI models are not locked in a black box, a common problem that can arise when organizations turn to third-party technology suppliers to address their AI solutions. Our products are designed to help your organization build trustworthy AI models for a wide array of use cases and to promote the democratization of data science and machine learning tools.

Sources

Machine Learning Explainability vs Interpretability: Two concepts that could help restore trust in AI

A right to explanation

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Data governance is defined as the organizational framework that applies to how data is obtained, managed, used, and secured by your organization. Having a strong data governance strategy in place empowers your organization to trust the integrity of their AI and machine learning models by ensuring that their data originates from reliable sources. These structures also ensure that your machine learning models are instructed to follow your organization’s key principles and values. With data originating from multiple sources that rarely, if ever, follow the same data science practices, a strong data governance framework is the key for your organization to follow best data science practices.

Why Is Data Governance Important?

First, data governance is one of the four key principles of AI ethics – which also include ethical purpose, fairness, and disclosure. Governance (the fourth principal) is necessary to study and understand the outcomes of AI failures and push your organization to apply high standards of risk management to your models. This is especially important in situations where life and death are stake, such as hospital treatments and healthcare.

Second, as businesses, organizations, and agencies often rely on data from numerous sources with different workflows and data-handling practices, governance is essential to reaching best data science practices. Establishing a reliable governance framework can provide your company with assurances that the data used by your models is reliable and consistent – and that the model reflects and follows your organization’s internal values. Understanding your own data governance definition enables your organization to more easily trust your machine learning models. It will also enable you to more easily explain relevant findings to others and how the model arrived at its conclusions.

Finally, following existing regulatory compliance obligations is another important motivation for firms to craft and adhere to a data governance model. Recent data breaches have prompted many organizations to make security an integral part of their own data governance frameworks. Having a clear data governance framework in place can help your organization remain compliant with existing laws like the European Union’s Global Data Protection Regulation (GDPR).

Traditional approaches to data governance are often based on corporate policies that are implemented across organizations in a top-down fashion. However, the reality is that these policies do not accurately reflect the way business teams or individuals interact with data. As data and analytics becomes increasingly democratized, it is important for data governance policies to reflect the day to day activities and interactions with data. This notion of bottoms-up data governance is often referred to as emergent data governance.

Data Governance + DataRobot

Poor data governance can lead to unreliable conclusions being generated by your machine learning models. It’s important for you to understand where your data originated, how it was handled, and the goals that your AI platform and machine learning models set out to achieve.

The first step in establishing a data governance framework is understanding your organization’s priorities and values, as well as what you expect your internal policies to ultimately achieve. DataRobot offers a free online white paper to help your organization understand a wide range of AI ethics topics and abide by best data science practices. Our enterprise AI platform can also offer your company an alternative to black box-sourced AIs that might not share your organization’s values and that may prevent you from explaining how your AI reached its conclusions. DataRobot MLOps, a product that is also available through the AI platform, offers users production model governance capabilities that help users oversee the lifecycle of their machine learning models from deployment to production. MLOps also allows you to test and update models currently in production, as well as access full controls and log information to ensure that you are meeting legal and regulatory compliance.

The AI platform also provides extensive capabilities to manage the data you use in your AI initiatives. The platform includes access to AI Catalog, a centrally managed location where datasets are collected and where access to data, models, and deployments can be regulated and tracked.

Data Prep, DataRobot’s data preparation solution, is also available through the AI platform. Using Data Prep, data preparation projects can automatically record every action performed on a dataset to provide a full lineage of actions, along with explanations. Data Prep directly integrates with the AI Catalog to publish curated data back into the catalog where it is consumed and managed for machine learning model creation and deployment.

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