MLOps by Evolutio, Powered by DataRobot extends observability for both predictive AI and generative AI, with always-on monitoring and production diagnostics to track and improve performance of your models. End users can stay informed of key metrics like service health, accuracy, data drift, prompt toxicity, token and inference costs. This module built on Cisco’s Full Stack Observability Platform is designed to make AI model performance, LLM operations and overall data and response quality in AI use cases a priority focus in an organization’s observability posture and ensures delivering your AI solutions with confidence.

Through our MLOps solution, we’ve fine-tuned the process of monitoring machine learning models in real-time. It facilitates a comprehensive view of model performance metrics and how they align with business objectives. If there’s an anomaly in a specific region or sector, it can be promptly addressed.

Modern business revolves around superior digital experience. Any deviation from excellence in this arena is met with swift criticism and dwindling user trust. And, because in the age of data, software applications will continue to incorporate AI capabilities, we need to ensure machine learning models are functioning optimally and meeting the stated objectives. Customers need a 360-degree view of their ML models and the agility to act swiftly based on insights, which is the challenge Evolutio, Cisco, and DataRobot hope to address with this partnership.

Evolutio’s Data Science & AI practice, powered by DataRobot, allows customers to segment their ML model insights by parameters like region, model type, and deployment scale, ensuring anomalies like reduced model accuracies or silent failures are instantly flagged. Evolutio, a services-led consultancy, specializes in “Business-Aware” Observability. Their goal is to bring business context into the realm of Observability to make clients’ view of user experience both highly actionable and proactive. 

Cisco FSO Platform is the only vendor-agnostic, entity-based observability platform in the market. The open, extensible, API-driven architecture of Cisco FSO Platform enables the creation of an observability ecosystem. It also allows development partners to build novel observability solutions and realize new revenue streams from those solutions. With the growing use of generative AI and integrations into the modern application stack, the Cisco FSO Platform helps customers to monitor these applications, their SLOs and bring the monitoring of large language models (LLMs), and MLOps models together with application observability. 

By implementing the DataRobot AI platform, Evolutio helps customers drive AI/ML solutions throughout their organizations by making AI/ML accessible to business stakeholders and subject matter experts. The combined capability of DataRobot and Evolutio accelerates time to value and execution on a monitored, explainable, enterprise-ready platform, and customers can realize immediate ROI.

Evolutio leverages DataRobot MLOps to provide monitoring and observability to increase trust and fairness, reduce bias, and maintain transparency into performance and cost. Most importantly, DataRobot’s mature MLOps capabilities enables models to learn continuously from the real world, with minimal downtime and with appropriate governance in place to protect the business investment. The partnership helps organizations with insights, clarity, and control over their operations when dealing with machine learning deployments to guarantee an impeccable user experience.

Learn more

• See a demo of the MLOps by Evolutio, Powered by DataRobot Module
• See Cisco’s announcement of the MLops by Evolutio, Powered by DataRobot Module
• Read Evolutio’s blog

The post DataRobot and Evolutio: Enabling Better Model Observability and Increased Trust in AI appeared first on DataRobot AI Platform.

As an example, let’s explore how organizations can harness the power of predictive and generative AI to streamline invoice processing offering a faster, more accurate and cost-effective alternative to manual review and validation.

The Business Problem

Right now companies of all sizes grapple with a common challenge:  the relentless influx of invoices.  The substantial amount of financial documentation can be overwhelming, often necessitating an army of employees dedicated to manual review and validation.  However this approach is not only time-consuming and costly, but also prone to human error, making it a fragile link in the financial chain.  

Harnessing the potential of AI is more important than ever before.  Businesses can employ predictive AI models to learn from historical invoice data, recognize patterns, and automatically flag potential anomalies in real-time.  This not only accelerates the validation process but also significantly reduces the margin of error, preventing costly mistakes. Furthermore, the integration of generative AI allows for the concise summarization of detected anomalies, improving communication and making it easier for teams to take swift and informed actions.

SAP and DataRobot Integrated AI Solution

This AI application enhances invoice processing through a combination of a predictive and generative AI to identify irregularities among invoices and to communicate the issues around the invoices.

• Leverage Predictive AI model for anomaly detection.
  • Business perspective: Anomaly detection can help identify irregularities, such as incorrect amounts, missing information or unusual patterns, before processing payments.
  • Implementation: Train the model using historical invoice data to recognize patterns and typical invoice characteristics.  When processing new invoices, the AI model can flag potential anomalies for review, reducing the risk of errors and fraud.
• Generative AI Summarization:
  • Business perspective: After identifying anomalies, it is important to communicate the issues to the relevant team members.  Traditional reporting methods may be wordy and time-consuming.  Generative AI can help interpret and summarize the detected anomalies in a concise and human-readable format.
  • Implementation: Leverage a LLM to generate an explanatory summary of the detected anomalies.  The AI model can extract key information from the anomaly detection results and provide a clear and structured narrative that summarizes the detected anomalies and the reasons to be considered anomalies, making it easier for analysts and managers to understand the issues. 

Architecture and Implementation Overview

To achieve these objectives, our platforms make use of various integration points, as illustrated in the architecture graph below:

1. Data preparation and ingestion 

Invoice data is prepared and parsed in SAP Datasphere / HANA Cloud.  DataRobot accesses and ingest this data from HANA Cloud through a JDBC connector.

2. Feature engineering and predictive model training

DataRobot  engineers features and conducts experiments with the invoice data set, allowing you to train anomaly detection models that excel at spotting invoices with irregular or abnormal information.  The approach you choose can be tailored to your specific data scenario—whether you have labeled data or not.  You have options to address this challenge effectively, either with a supervised or an unsupervised approach.

In this case, we utilized historical records that had been categorized as anomalies and non-anomalies.  After data ingestion, DataRobot runs an extensive data exploratory analysis, identifies any data quality issues, and automatically generates new features and relevant feature lists.   With that ready, we were able to conduct a comprehensive analysis through 64 distinct experiments in a short period of time.  As a result, we were able to pinpoint the top-performing model at the forefront of the leaderboard.  This approach allowed us to select the most effective predictive model for the task at hand.  

Within each of these experiments, you have the opportunity to thoroughly assess and gauge their performance.  This analysis provides valuable insights into how each predictive model leverages the features within your invoice to make accurate predictions.  To facilitate this process, you have access to an array of tools, including lift charts, ROC curve, and SHAP prediction explanations, which estimate how much each feature contributes to a given prediction. These insights offer an intuitive means to gain a deeper understanding of the model’s behavior and their influence of the invoice data, ensuring you make well-informed decisions.

3. Model deployment

Once we identify the optimal predictive model, we move forward to transition the solution into production.  This phase seamlessly merges our predictive and generative AI approach by orchestrating the deployment of an unstructured model within DataRobot.  This deployment harmonizes the predictive AI model for anomaly detection with a Large Language Model (LLM), which excels in generating text to communicate the predictive insights.  Alternatively, you have the flexibility to deploy predictive AI models directly within SAP AI Core, offering an additional route for operationalizing your solution.

The LLM summarizes the rationales linked to each prediction, making it readily digestible for your financial analysis needs. This versatile deployment strategy ensures that the insights generated are accessible and actionable in a manner that suits your unique business requirements. 

Two simple python files easily orchestrate this integration through simple functions and hooks that will be executed each time an invoice requires a prediction and its consecutive analysis.  The first file named helper.py, has the credentials to connect with GPT 3.5 through Azure and contains the prompt to summarize the explanations and insights derived from the predictive model.  The second file, named custom.py, easily orchestrates the whole predictive and generative pipeline through a few simple hooks.   You can find an example of how to construct custom python files for unstructured models in our github repository.  

You have the capability to test and validate this unstructured model prior its deployment, assuring that it consistently produces the intended outcomes, free of any operational hitches.  

4. Business Application

Once the deployment is officially in production, an accessible API endpoint becomes your bridge to connect with the deployment, seamlessly generating the precise results you seek in SAP Build. 

Next, we craft a business application for invoice anomaly detection within SAP Build.  This application retrieves the predictive and generative output via API integration and offers a user-friendly interface.  It presents the results in a practical and intuitive manner, ensuring that financial analysts can effortlessly upload invoices in PDF format, simplifying their workflow and enhancing the overall user experience.  

5. Production Monitoring

DataRobot maintains an oversight over the generative AI pipeline through the utilization of custom performance metrics and predictive models.  This rigorous monitoring process ensures the continuous reliability and efficiency of our solution, offering you a seamlessly dependable experience.   

Conclusion

In summary, the partnership between SAP and DataRobot continues to allow organizations to quickly drive value from their AI investments, and now even more by leveraging generative AI.  Predictive anomaly detection and generative AI can transform the challenges and risks associated with invoice processing.  Efficiency and accuracy soar, while communication becomes clearer and more streamlined.  Businesses can now modernize their operations, save time and reduce errors.  It is time to unlock the potential of this transformative technology and take your operations to the next level. 

The post SAP and DataRobot: Elevating Invoice Processing with Anomaly Detection and Generative AI appeared first on DataRobot AI Platform.

This strengthened collaboration allows you to integrate the latest, trustworthy generative and predictive AI technologies. This not only puts you at the forefront of innovation but securely maximizes all of your enterprise data’s value. In addition, DataRobot and Google Cloud make it easier to develop and deploy AI solutions without dealing with complicated cloud-native setups.

“DataRobot and Google Cloud Platform fit seamlessly into the Keller Williams AI ecosystem. We have predictive models in production and are looking to build generative models that add value and benefit our business.” – Fred DeLetter, Senior Director of Business Insights & Analytics, Keller Williams

But what does this mean for our leaders and AI enthusiasts?

For CIOs – We’re not just enhancing your AI projects. We’re supercharging them to align with your broader enterprise strategy. With instant, secure data access via Google’s BigQuery, your path to agile, data-driven decision-making now becomes a highway. The outcome? A robust and quantifiable ROI on all your AI investments.

CDOs – Your commitment to data governance and building ethical AI is about to pay off even more. Our partnership delivers not just advanced governance and explainable and transparent AI results but also enriched datasets and real-time actionable insights. All essential ingredients for your strategic roadmap.

AI Builders – Unlock your creativity in an expansive ecosystem that marries flexibility with functionality. From rapid experimentation to quick deployments, a whole new range of both generative and predictive AI models is accessible for you to start exploring.

IT professionals – Easily and securely integrate our solution, which is built from the ground up to run on Google Kubernetes Engine (GKE). This translates to seamless, secure, and scalable AI model deployments—simplifying what used to be a complex process.

The Highlights: Let’s Unpack This!

1. Open Ecosystem for Generative and Predictive AI 

Everyone is now interested in generative AI and wonders how to adopt it. Our partnership allows you to address any generative or predictive AI use case. 

Start by choosing your preferred LLM, It can be models from Google Cloud’s Model Garden through Vertex AI, including Palm2 LLM. 

Then customize prompts easily within Vertex AI for accuracy and relevancy in your enterprise’s context and effortlessly deploy LLMs to its endpoints for scalable runtime. 

Once your LLM is successfully in production, DataRobot monitoring will keep an eye on your AI applications to make sure they’re reliable and steady. It will also help them adjust to any changes that may come up over time by effectively managing how confident the model is in its predictions.

2. Making All Data Accessible with Native BigQuery Integration  

You probably invest so much time, effort, and money to collect and curate data from your organization, partners, suppliers, customers, and more. 

So why not use all of it? 

Making data easily and securely accessible speeds up the development of more effective and accurate AI solutions. With enhanced BigQuery Connection Management, you can effortlessly connect to Google BigQuery and browse and preview data in real-time to identify the data for your AI business case. 

To avoid “garbage in, garbage out,” DataRobot generates insights on your BigQuery data to identify most relevant features for your AI business case to save you time. 

Furthermore, we push-down data preparation for BigQuery, for not just scalability but also governance. With DataRobot and Google Cloud, you can leverage BigQuery’s scalability, streamline data governance, and build world-class AI solutions.

Enterprise-grade security is vital, and it shouldn’t be a headache. DataRobot’s enhanced integrations with Google Cloud offer peace of mind for IT administrators while empowering AI Builders to create their way. IT administrators can now centrally manage Service Account keys and safely provision access to BigQuery, or AI Builders can easily connect to BigQuery using their Google account with support for OAuth authentication. 

3. Expanded Deployment Options 

DataRobot’s managed SaaS offering in the Google Cloud Marketplace lets you quickly and easily purchase and start using your DataRobot AI Platform solution. Put your Google committed cloud credits to work with DataRobot to tap into the latest advancements in both generative and predictive AI from Google and DataRobot.  

DataRobot now runs natively on Google Kubernetes Engine (GKE), so you can run your AI workflows efficiently and leverage the performance of Google Cloud’s native services.

Ready to Dive In?  

If all of this sounds too good to miss (because it is!), sign up for a 30-day trial and connect DataRobot to your BigQuery data to see things in action. Start a journey to redefine how you work, create, and envision what’s possible with DataRobot and Google Cloud!

The post Introducing the Latest DataRobot Integrations with Google Cloud, Including a New Generative AI Initiative appeared first on DataRobot AI Platform.

DataRobot and SageMaker create a powerful duo to accelerate AI adoption  

With DataRobot AI Production, users can build their own SageMaker containers to train AI models and host them as a SageMaker endpoint, leveraging DataRobot MLOps libraries to automatically collect and monitor inference metrics. Monitoring jobs can be scheduled natively from DataRobot without the hassle of manual pipelines, freeing up data science resources while offering users full observability across a large number of SageMaker models. In addition to traditional MLOps activities, DataRobot AI Production offers out-of-the-box governance best practices such as automated model compliance documentation and model versioning so all DataRobot and SageMaker models can be governed centrally. 

Together, DataRobot and AWS provide a seamless integration that fits our environment and enables better, faster data-driven decisions with confidence. As DataRobot and AWS now become even more aligned, the potential to further leverage the strengths of both platforms with simplified workflows, enhanced scalability and accelerated time-to-market is tremendously exciting.

We’re thrilled to be a recognized Amazon SageMaker Ready Partner, and look forward to helping companies achieve their technology goals by leveraging AWS. To learn more about DataRobot’s integration with Amazon SageMaker, download the whitepaper here.

About the SageMaker Ready Program

Joining the Amazon SageMaker Ready Program differentiates DatRobot as an AWS Partner Network (APN) member with a product that works with Amazon SageMaker and is generally available for and fully supports AWS customers. The Amazon SageMaker Ready program helps customers quickly and easily find AWS Software Path partner products to help accelerate their machine learning adoption by providing out-of-the-box abstractions for most common challenges in machine learning (ML) that build on top of the foundational capabilities Amazon SageMaker provides. 

Amazon SageMaker offers a robust set of capabilities and AWS Partners add value to further expand the capabilities by integrating with their solutions. By providing customers a catalog of Software Path partner solutions that lift the complexities of machine learning, the Amazon SageMaker Ready Program will broaden the user base and increase customer adoption. Amazon SageMaker Ready Program members also offer AWS customers Amazon SageMaker-supported products that offer Amazon SageMaker both in Software Path Partner solutions they already know, or offer products that simplify each step of the ML model building. These applications are validated by AWS Partner Solutions Architects to ensure customers have a consistent experience using the software.

To support the seamless integration and deployment of these solutions, AWS established the AWS Service Ready Program to help customers identify solutions that support AWS services and spend less time evaluating new tools, and more time scaling their use of solutions that work on AWS. Customers can review the Amazon SageMaker Ready Partner product catalog to confirm their preferred vendor solutions are already integrated with Amazon SageMaker. Customers can also discover, browse by category or ML model deployment challenges, and select partner software solutions for their specific ML development needs. 

The post DataRobot Joins the Amazon SageMaker Ready Program appeared first on DataRobot AI Platform.

In our recent announcement, From Vision to Value. Creating Impact with AI, we showcased all that we have been doing to help you implement, scale and govern your AI initiatives with the DataRobot AI Platform. Part of the work we’ve done is to curate a strong AI partner ecosystem made up of technology partners as well as service providers. Together these partners are uniquely positioned to help you de-risk and accelerate your journey up the AI maturity curve, and give you a strong foundation for operationalizing at scale with the DataRobot AI Platform. 

Building the DataRobot Cloud Partner Ecosystem

A modern data and analytics platform is essential to your digital transformation strategy. Our expanded strategic technology partnerships across our cloud and technology make it easy for you to modernize quickly and with confidence. With a commitment to product co-innovation, we are creating new ways for you to maximize your existing technology investments. 

When it comes to deployment, the DataRobot AI Platform gives you the ultimate flexibility and choice when it comes to how you want to modernize – whether that’s on AWS, Google Cloud or Azure, single-tenant SaaS, on premises or hybrid deployment models – the choice is yours. With the ability to procure DataRobot through cloud marketplaces and using your existing cloud commitment, getting up and running and delivering valuable AI use-cases has never been easier. 

Beyond deployment, successful AI initiatives require flexibility and extensibility when it comes to your ecosystem. In our 9.0 announcement, we introduced even deeper integrations with your existing investments in data platforms, AI frameworks, DevOps tools, application stacks, and business processes. Our co-innovation and platform integrations with cloud services, as well the technology partners like Snowflake, SAP, and Azure Open AI, make it easy for you to leverage all of the rich data in your existing cloud data warehouses and transform it into value-driving ML use-cases at production-level scale. Learn more about these new innovations, and How DataRobot Integrates into a Broad Enterprise Technology Ecosystem (On-Demand).

DataRobot AI Services Partners Help You Get to Value Fast 

There is a lot that has to happen in bringing your entire modern ecosystem together, and embarking on or accelerating your AI journey. Thankfully our customers do not have to navigate this complexity alone. Our consulting partners have over a decade of experience delivering thousands of AI projects to customers of all sizes and all over the world. 

With proven success delivering on complex, specific and targeted requirements, they know how to ensure your AI initiatives deliver value within your broader digital transformation and modernization strategies. Our strategic AI partners have extensive technology, industry and use case expertise to help you make the most out of the DataRobot AI Platform. With hundreds of expert data scientists, engineers and consultants, they have developed best practices to help make your AI projects successful and result in fast time to value at scale. 

No matter where you are in the AI maturity curve, our AI services partners will meet you where you are. Whether you’re just getting started and need help identifying which use cases will drive the most value, or helping you build a scalable, well governed full-lifecycle machine learning process, our partners know what you need to get to the next stage in your ML journey. You can find a partner the best for your AI needs, location, or industry requirements in our Partner Finder. If you have any questions, reach out to your DataRobot sales or customer success team and they will help you get connected.

This is just the beginning of what we’re working on at DataRobot. Catch up our new offering, From Vision to Value. Creating Impact with AI, and stay tuned for what we’re coming out with next.

The post Accelerate Your AI Journey with the DataRobot Partner Ecosystem appeared first on DataRobot AI Platform.

Today we are unveiling a new cutting-edge integration with Microsoft Azure OpenAI Service. This integration, which leverages the ChatGPT model in Azure OpenAI, provides a conversational AI experience that will allow you to interact with and interpret model results and predictions directly. This important milestone is the first step in drastically modernizing not only the development, but most importantly, the interpretation, understanding, and adoption of AI use cases.

The integration of DataRobot and Azure OpenAI Service breaks down a barrier that has long existed between data teams and business stakeholders. This integration takes the power of one of the most advanced large language model technologies that exists today in Azure OpenAI Service, and through DataRobot, drives value-centric outcomes with machine learning.

Traditionally, developing appropriate data science code and interpreting the results to solve a use-case is manually done by data scientists. It is a time-intensive process that can slow the adoption of AI across an organization. However, we’re now taking the information managed by DataRobot (such as the data, features, models, predictions) and leveraging the capabilities of the Azure OpenAI Service to make it more accessible and understandable. The integration allows you to generate intelligent data science code that reflects your use case. For example, generating code to prepare data as well as train and deploy a model. And, it allows you to translate modeling results into key business takeaways. An example of this is proposing why a feature has a high impact on predictions. Data scientists still need to review and evaluate these results. However, data science teams can spend less time generating ML prediction interpretations and business users can derive greater understanding from their ML applications. Ultimately, users benefit from a transparent, and clear explanation of what ML predictions means to them.

While I’m extremely excited about what this will mean for increasing the applications and impacts of AI, it is just the beginning. Microsoft and DataRobot will work closely to expand on the performance and reliability of these solutions together, giving customers even greater confidence to depend on the insights.

This new innovation is a testament to DataRobot’s relentless focus on developing pioneering solutions to jumpstart a customer’s AI projects for game-changing results. This is another example of how DataRobot AI Platform makes it easy to seamlessly integrate with new technologies, like Azure OpenAI Service, so you can create innovative business solutions using ML.

Accelerating Value-Driven AI with DataRobot and Azure OpenAI  

So how is this happening? In this new approach, we are creating an entirely new data science development and collaboration experience. DataRobot and Microsoft infused new capabilities from large language models to anticipate the code that AI builders need to write to solve a particular use-case, and to translate the resulting statistical results into the business language necessary to communicate and collaborate with key business stakeholders. 

For example, a data scientist can generate data prep code that is appropriate for the use-case, such as merging the relevant data and deriving targets, automatically, by describing the problem at hand in natural language. This saves us the time it would otherwise take to memorize metadata and APIs.

Next, when a business user starts to ask questions and analyze the insights, the DataRobot AI Platform dynamically surfaces the use case information, data, and models along with analysis generated using an Azure OpenAI model in order to generate text descriptions of the most key observations, and the interpretations of what they mean. Not only are models being explained in business language, the conversational capabilities of Azure OpenAI Service allows business stakeholders to ask follow-up questions and to drill in to what is most impactful findings.

This is a revolutionary conversation experience that lets everyday people interact with a ML model and its insights. New for data scientists, it helps translate the math of the model into impact on the business, and equally helps business stakeholders get the answers they need to effect change.  

Giving Data Scientists New Power Tools to go Faster

As any data scientist knows, developing models and explaining results is a time-consuming process. Coding involves memorizing APIs, debugging, and fixing errors. Explaining results means translating what the raw data features represent and contextualizing the insight trends. While a data scientist may know the data by heart, the AI-generated explanations help others to also understand what the different findings mean. 

The unique user experience, combining DataRobot and Azure OpenAI Service, modernizes and accelerates many of the repetitive tasks required to develop and implement models, such as developing in a notebook and summarizing key results for stakeholders. Data scientists can quickly innovate to tackle new ML problems and see their work impact organizations. The integration also helps data scientists create new ways to clearly articulate and explain ML models. DataRobot and Azure OpenAI Service together help generate more actionable insights. 

The Potential of DataRobot and Microsoft Azure OpenAI Service

We are only getting started. It’s been a natural fit for Microsoft and DataRobot to work together. We’ll be working together to embed complex generative AI strategies from Azure into DataRobot modeling strategies next – unlocking completely new use cases for the enterprise.

A History Rooted in Innovation

DataRobot has been at the forefront of innovation in the areas of AutoML, MLOps, Automated Time Series, and feature engineering. I am personally excited by what the integration with Azure OpenAI Service will mean for data science and our customers next. 

We’ve been innovating for the last decade, and we’re not done yet. Stay tuned and keep an eye out for what’s coming. The DataRobot team is working hard to push the boundaries through all of the new innovations coming out in AI to help organizations apply them to their organizations for value-driven AI. 

See the DataRobot and Azure OpenAI capabilities in action and learn more about the DataRobot and Microsoft partnership in the virtual event, From Vision to Value: Creating Impact with AI, live or on-demand.

The post Microsoft Azure OpenAI Service and DataRobot Modernize Data Science Work with Cutting-Edge Technology Innovations appeared first on DataRobot AI Platform.

Resolving this tension and helping you make the most of your current ecosystem investments is core to the DataRobot AI Platform. The DataRobot team has been working hard on new integrations that make data scientists more agile and meet the needs of enterprise IT, starting with Snowflake. In our 9.0 release, we’ve made it easy for you to rapidly prepare data, engineer new features and subsequently automate model deployment and monitoring into your Snowflake data landscape, all with limited data movement. We’ve tightened the loop between ML data prep, experimentation and testing all the way through to putting models into production. Now data scientists can be agile across the machine learning life cycle with the benefit of Snowflake’s scale, security, and governance. 

Why are we focusing on this? Because the current ML lifecycle process is broken. On average, 54% of AI projects make it from pilot to production. Hence, nearly half of AI projects fail. There are a couple of reasons for this. 

First, being able to experiment long enough to identify meaningful patterns and drivers of change is difficult. The prototyping loop, particularly the ML data prep for each new experiment, is tedious at best. It’s difficult for data scientists to securely connect to, browse and preview, and prepare data for ML models particularly when data is spread across multiple tables. From there, every time you run a new experiment, you’re back to prepping the data again. And when you do find a signal and have built a great model, it’s difficult to put those ML models into production. 

Models that do make it into production require time-consuming management through monitoring and replacement to maintain prediction quality. A lack of integrated tooling along the entire process not only slows down data scientist productivity, but it increases the total cost of ownership as teams have to stitch together tooling to get through this process. The DataRobot AI Platform has been focused on making the entire ML lifecycle seamless, and today we’re doing even more with our new Snowflake integration. 

Secure, Seamless, and Scalable ML Data Preparation and Experimentation

Now DataRobot and Snowflake customers can maximize their return on investment in AI and their cloud data platform. You can seamlessly and securely connect to Snowflake with support for External OAuth authentication in addition to basic authentication. DataRobot secure OAuth configuration sharing allows IT administrators to configure and manage access to Snowflake.

DataRobot will automatically inherit access controls, so you can focus on creating value-driven AI, and IT can streamline their backlog. 

With our new integration, you can quickly browse and preview data across the Snowflake landscape to identify the data you need for your machine learning use case. Automated data preparation and well-defined APIs allow you to quickly frame business problems as training datasets. The push-down integration minimizes data movement and allows you to leverage Snowflake for secure and scalable data preparation, and as a feature engineering engine so you don’t have to worry about compute resources, or wait on processes to complete. Now you can take full advantage of the scale and elasticity of your Snowflake instance.  

With our DataRobot hosted notebooks, you can leverage Snowpark for Python alongside the DataRobot Python Client to quickly connect to Snowflake, explore, prepare, and create machine learning experiments with your Snowflake data. You can leverage the two platforms in the way that make the most sense for you – leveraging Snowpark and the DataRobot developer framework that has native support for Python, Java, and Scala. Because this integration is native to the DataRobot AI Platform, you get your time back with one frictionless experience. 

One-Click Model Deployment and Monitoring in Snowflake

Once trained models are ready to be deployed, you can operationalize them in Snowflake with a single click. Supported models can be deployed directly into Snowflake as a Java UDF by DataRobot. This functionality includes being able to deploy models, built outside of DataRobot, in Snowflake. This means you can bring a model directly into the governed runtime of Snowflake, allowing businesses to make accurate predictions in-database on sensitive data at scale, and without the fuss of configuration. One-click model deployment also gives ML practitioners the flexibility to use normal queries or more advanced features like Stored Procedures from within Snowflake to read scoring data, score data, and write predictions.

Along with one-click model deployment come more robust monitoring capabilities, allowing for ongoing monitoring of not just deployment service health, but also drift and accuracy. Model replacement is made easy with retraining and deployment workflows to ensure enterprise-grade reliability of production machine learning on Snowflake. 

Snowflake and DataRobot: Combining Data and AI for Business Results

The new Snowflake and DataRobot integration provides organizations a unique and scalable enterprise platform for data and AI driven business results. We shrunk the ML cycle time, and made it easy for you to experiment more, prepare datasets and build ML models fast, and then get those models out into production to drive value even faster. 

Try out the new integration and let us know what you like. Learn more from Torsten Grabs, Director of Product Management at Snowflake, who will share more about these new innovative capabilities at the DataRobot virtual on-demand event: From Vision to Value: Creating Impact with AI. Join us on March 16 and see more of the DataRobot and Snowflake integration first hand! 

¹ Gartner®, Gartner Survey Analysis: The Most Successful AI Implementations Require Discipline, not Ph.D.s, Erick Brethenoux, Anthony Mullen, Published 26 August 2022

The post New DataRobot and Snowflake Integrations: Seamless Data Prep, Model Deployment, and Monitoring appeared first on DataRobot AI Platform.

As a result, enterprises can now get powerful insights and predictive analytics from their business data by integrating DataRobot-trained machine learning models into their SAP-specific business processes and applications, while bringing data science and analytics teams and business users closer together for better outcomes.

More details about the joint capabilities were shared at the March 8^th SAP Data Unleashed virtual event and furthermore will be shared at From Vision to Value, Creating Impact with AI, the March 16^th DataRobot virtual event. Tune in to learn more. Registration is free for both events.

SAP AI Solutions: Making Business Applications More Intelligent

AI is at the heart of the SAP strategy to help customers become intelligent, sustainable enterprises. With SAP AI solutions such as SAP AI Core, AI Business Services and SAP HANA PAL, customers can bring predictive intelligence into their business processes through ready-to-use, pre-trained AI capabilities in SAP applications. 

Enterprises can extend AI capabilities to meet their specific needs, using SAP Business Technology Platform. They can also ensure trust and reliability by using AI capabilities that are built on a stringent ethics policy and data privacy standards that enable responsible use of AI with full transparency and compliance.

This partnership between the two brings together DataRobot’s multimodal machine learning capabilities with SAP’s extensive business data and processes to create business-centric ML solutions.

DataRobot AI: Complete AI Lifecycle Management

The DataRobot AI Platform is an open, complete AI lifecycle platform, leveraging machine learning that has broad interoperability, end-to-end capabilities for Experimentation and Production, and can be deployed on-premises or in any cloud infrastructure. 

DataRobot improves collaboration among AI teams so that they can discover and prove the value of models in business use cases through experimentation and then get models into production faster to improve how they run, grow, and optimize their business. Additionally, DataRobot data scientists and support teams have a proven record of success working with thousands of customers on tens of thousands of AI use cases across a wide range of industries.

DataRobot integration with SAP Datasphere enables customers to have quick access to their most updated, trusted, and governed data assets—with business context and logic intact.

Build Custom ML Models Combining Multimodal SAP and non-SAP Business Data

Every business has a unique data landscape and business processes that it wants to extract maximum value out of without causing too much disruption to its existing tech stack. So in order to get maximum value from AI, it needs to build machine learning models that are unique to each of its business usecase.

Here, it becomes crucial for the company to leverage an AI solution that can integrate with their existing workflows to ensure frictionless adoption by all stakeholders involved. 

With DataRobot’s built-in capability to connect to SAP HANA, users can quickly build custom AI models, using either the DataRobot user interface or DataRobot Notebooks for code-first data scientists. SAP customers can also ingest multimodal external data from other non-SAP sources, export DataRobot models into SAP AI Core through model-deployment pipelines, and use their predictions in SAP business applications, as well as continuously monitor and retrain models.

This joint solution thus helps enterprises get even more value out of their existing SAP and non-SAP business data by applying machine learning with broad interoperability across existing machine learning libraries and frameworks, and end-to-end capabilities for Experimentation and Production.

Enterprises can accelerate collaborative experimentation with the flexibility to customize models across open-source frameworks and production environments through a superior user experience, a decade of DataRobot’s data science expertise, and support for diverse organizational use cases. Using DataRobot, companies can monitor their models in production for accuracy and data drift, in addition to retraining them proactively. 

With DataRobot and SAP,  joint customers can:

leverage powerful machine learning on top of SAP Datasphere and bring it directly into their business data fabric – on whichever cloud platform it resides

facilitate collaborations between business teams and data science teams to create accurate and robust ML models and, more importantly, integrate predictions directly into SAP business applications through SAP AI Core

monitor model performance, regardless of where it is deployed, using DataRobot MLOps

detect and mitigate bias for more responsible use of ML models

SAP and DataRobot enable customers to connect core SAP software with DataRobot advanced machine learning capabilities. The result is more intelligent business predictions with advanced analytics.

Get Started with Business-Centric Machine Learning 

We are keen to collaborate with our customers and get feedback on our joint product roadmap as it evolves. 

You can learn more about today’s DataRobot and SAP announcements here:

• Visit datarobot.com/partners/technology-partners/sap

The post DataRobot and SAP Partner to Deliver Custom AI Solutions for the Enterprise appeared first on DataRobot AI Platform.

DataRobot is available on Azure as an AI Platform Single-Tenant SaaS, eliminating the time and cost of an on-premises implementation. AI Platform Single-Tenant SaaS are fully managed by DataRobot and replace disparate machine learning tools, simplifying management. Unique to DataRobot, this service helps customers with specific data management or data sovereignty needs, as well as organizations interested in outsourcing the IT management and set up of new software purchases. Reducing time to value in deploying, upgrading, and managing the AI infrastructure, AI Platform Single-Tenant SaaS allows customers to focus on generating more value, faster, with AI.

Enterprises that use DataRobot on Azure get the knowledge, experience, and best practices of data scientists from DataRobot. They also gain access to more than 200 products and cloud services on the Azure cloud platform designed to help clients create new solutions to solve today’s business challenges. Customers can build, run, and manage applications across multiple clouds, on-premises, and at the edge, with the tools of their choice. The DataRobot AI Platform seamlessly integrates with Azure cloud services, including Azure Machine Learning, Azure Data Lake Storage Gen 2 (ADLS), Azure Synapse Analytics, and Azure SQL database. 

Flexibility and Scalability: Benefits of the DataRobot and Azure Integration

The DataRobot integration with Microsoft provides our customers with flexible options for procurement via the Azure Marketplace, easy model deployment in the Azure ecosystem, and built-in data connectors for Azure Synapse Analytics, ADLS Gen2, and Azure SQL Database. Models trained in DataRobot can also be easily deployed to Azure Machine Learning, allowing users to host models easier in a secure way. AI Platform Single-Tenant SaaS can be ubiquitously used across various Microsoft products enabling more AI builders across enterprises to scale business impact. 

Today’s organizations are realizing success with enterprise-grade AI technologies for fast and secure business growth. The scalability of the Microsoft Azure cloud platform combined with the powerful machine learning capabilities of DataRobot’s AI platform Single-Tenant SaaS empowers customers to grow their business through reliable AI-driven decisions.

Together, the DataRobot AI Platform and Azure provide: 

• Security, governance, and compliance of AI projects. With built-in guardrails and automated model documentation for compliance,  have the confidence you need to make business decisions quickly. You will have the ability to deploy a model generated in  DataRobot for inferencing on Azure Machine Learning for geographic isolation, security, and control over production models. The centralized, governed DataRobot MLOps environment provides maximum flexibility for users to decide which of the DataRobot recommended models they want to leverage and the scale to support robust inferencing through Azure Machine Learning. 

• The ability to scale the productivity of AI teams by simplifying complex AI lifecycles.  DataRobot MLOps gives you everything you need to scale AI in production, with one place to manage all models whether deployed inside the DataRobot AI Platform or deployed on top of Azure Machine Learning. Your AI teams will be equipped with self-serve tools, explainable automation, and manual overrides to run hundreds of diverse models in minutes, allowing you to solve business problems faster with less risk to the business. The intuitive DataRobot user interface and APIs make it easy for AI builders with different skill sets to collaborate, improve productivity, and integrate DataRobot with their existing ecosystem. The combination of rapid experimentation and production simplifies the machine learning lifecycle, enabling AI teams to continuously monitor valuable metrics including the health and accuracy of production models, and accelerating overall time to value.

• The capability to rapidly build an AI-powered organization with industry-specific solutions and expertise. Finally, users get access to cutting-edge algorithms and model blueprints, including the latest in deep learning, that incorporate advanced data science best practices, developed by Kaggle-ranked data scientists in DataRobot. The platform empowers teams with a library of hundreds of industry-specific best practices, use cases, and resources like notebooks and solution accelerators that expedite time to insight. 

Get Started with DataRobot on Azure

DataRobot AI Platform on Azure provides enterprises the security and governance required to scale applied AI. Guardrails and best-practices are embedded throughout the AI lifecycle—from development to deployment—enabling specialized teams, such as data science, IT, and business experts, to do more with less and collaborate together. This drastically improves productivity of teams and allows them to scale business results. 

With built-in compliance documentation and automated governance, the DataRobot AI Platform lets regulated industries scale AI with unprecedented speed and confidence. Financial services organizations can use DataRobot AI Platform on Azure to solve business challenges, such as credit risk management, while remaining compliant with industry regulations.

The DataRobot AI Platform is the next generation of AI. DataRobot’s vision is to bring together all data types, all users, and all environments to deliver critical business insights for every organization. DataRobot is trusted by global customers across industries and verticals, including a third of the Fortune 50. For more information, visit https://www.datarobot.com/.

DataRobot AI Platform is available via Azure Marketplace.

The post Make Better Data-Driven Decisions with DataRobot AI Platform Single-Tenant SaaS on Microsoft Azure appeared first on DataRobot AI Platform.

Some businesses have spent significant money on tools to remain innovative and competitive. While this can be an excellent strategy for a future-oriented company, it can prove futile if you don’t maximize the value of your investment. According to Flexera¹, 92% of enterprises have a multi-cloud strategy, while 80% have a hybrid cloud strategy.

Integrating different systems, data sources, and technologies within an ecosystem can be difficult and time-consuming, leading to inefficiencies, data silos, broken machine learning models, and locked ROI. 

The DataRobot AI Platform and the Snowflake Data Cloud provide an interoperable, scalable AI/ML solution and unique services that integrate with diverse ecosystems so that data-driven enterprises can focus on delivering trusted and impactful results.

Extending Snowflake Integration: New Capabilities and Improvements

To help customers maximize their Snowflake investment, DataRobot is extending its Snowflake integration to help customers quickly iterate, improve models, and complete the ML lifecycle without repeated configuration. 

This includes: 

1. Supporting Snowflake External OAuth configuration
2. Leveraging Snowpark for exploratory data analysis with DataRobot-hosted Notebooks and model scoring
3. A seamless user experience when deploying and monitoring DataRobot models to Snowflake
4. Monitoring service health, drift, and accuracy of DataRobot models in Snowflake

“Organizations are looking for mature data science platforms that can scale to the size of their entire business. With the latest capabilities launched by DataRobot, customers can now guarantee the security and governance of their data used for ML, while simultaneously increasing the accessibility, performance, and efficiency of data preparation, model training, and model observability by their users,” said Miles Adkins, Data Cloud Principal, AI/ML at Snowflake. “By bringing the unmatched AutoML capabilities of DataRobot to the data in Snowflake’s Data Cloud, customers get a seamless and comprehensive enterprise-grade data science platform.”

Complete the Machine Learning Lifecycle, Without Repeated Configuration

Connecting to Snowflake

Connect to Snowflake through external identity providers using Snowflake External OAuth without providing user and password credentials to DataRobot. Reduce your security perimeter by reusing your existing Snowflake security policies with DataRobot.

Learn more about Snowflake External OAuth.

Exploratory Data Analysis 

After we connect to Snowflake, we can start our ML experiment.

We recently announced DataRobot’s new Hosted Notebooks capability. 

For our joint solution with Snowflake, this means that code-first users can use DataRobot’s hosted Notebooks as the interface and Snowpark processes the data directly in the data warehouse. This allows users to work with familiar Python syntax that gets pushed down to Snowflake to run seamlessly in a highly secure and elastic processing engine. They can enjoy a hosted experience with code snippets, versioning, and simple environment management for rapid AI experimentation. 

Learn more about DataRobot hosted notebooks.

Model Training

Once the data is prepared, users choose their preferred approach for model development using DataRobot AutoML through the GUI, hosted Notebooks, or both.

When the training process is complete, DataRobot will recommend the best-performing model for production based on the selected metric and provide an explanation.

Model Deployment

Customers need the flexibility to deploy models into different environments. Deploying to Snowflake reduces infrastructure operations complexity, data transfer latency and associated costs, while improving efficiency and providing near limitless scale.

A new Snowflake prediction environment configured by DataRobot will automatically manage and control the environment, including model deployment and replacement.

When deploying a DataRobot model to Snowflake, this new seamless integration significantly improves the user experience, reduces time and effort, and eliminates user errors. 

The automated deployment pushes trained models as Java UDFs, running scalable inference inside Snowflake, and leveraging Snowpark to score the data for speed and elasticity, while keeping data in place.

Model Monitoring

Internal and external factors affect models’ performance.

The new monitoring job capability, which is run seamlessly from the DataRobot GUI helps customers make business decisions based on predictions and actual data changes and govern their models at scale.

Over time models degrade and require replacement or retraining. The DataRobot MLOps dashboards present the model’s health, data drift, and accuracy over time and can help determine model accountability.

Learn more about the new monitoring job and automated deployment.

There’s more coming

We have more exciting capabilities to share, many related to the Snowflake integration, which will be announced at the DataRobot 9.0 launch event on March 16th. Register here to be part of this virtual event. 

If you are already a customer of Snowflake and DataRobot, reach out to your account team to get up to speed on these new features.

Getting Started with DataRobot AI and Snowflake, the Data Cloud

DataRobot and Snowflake together offer an end-to-end enterprise-grade AI experience and expertise to enterprises by reducing complexity and productionizing ML models at scale,  unlocking business value. Learn more at DataRobot.com/Snowflake. 

¹ Source: Flexera 2021 State of the Cloud Report

The post Bringing More AI to Snowflake, the Data Cloud appeared first on DataRobot AI Platform.