But we don’t shy away from the hard problems with AI. Which is why our new Fall Launch addresses the confidence gap head-on, empowering enterprises to deploy generative AI. The new capabilities allow you to operate AI with correctness and control, govern with full transparency and oversight, and build rapidly with flexibility, with the assurance you need to feel confident putting these solutions into practice. Our robust platform empowers practitioners to rapidly experiment, maintain oversight, and operationalize high-quality generative AI solutions.

Since clear insights and model performance alerts ensure high quality responses, operating with correctness and control becomes possible, allowing you to reliably and assuredly get your generative AI solutions into production. With the new capabilities of the DataRobot AI Platform, you can now continuously monitor performance to ensure real-time observability of deployed models through our unified AI Console. Custom alerts and metrics identify issues proactively, increasing the overall trust in your generative AI solution. Features like Generative AI Guard models score every output for completeness, relevance, and confidence. Coupled with human feedback loops, this ensures that the model’s outputs stay on track over time. When the ongoing monitoring surfaces anomalies, our platform enables immediate intervention to address problems before any downstream impact occurs to maintain operational control.

As generative AI expands, cross-functional coordination becomes imperative but increasingly challenging. DataRobot helps you govern with full transparency and oversight by enabling and facilitating greater collaboration. The unified AI Registry catalogs all models and projects from across your organization in one place, enabling greater coordination, model lineage transparency, and, thus, better overall governance. 

The Workbench centralizes in-flight projects so nothing falls through the cracks. With holistic visibility, DataRobot allows seamless collaboration across data teams, developers, IT, and business users. Granular analytics around generative prediction spend also facilitates financially responsible innovation by providing continuous cost visibility. With robust visibility into model portfolios and spending, DataRobot empowers leaders to govern generative AI in an informed, measured manner.

Experimentation and optionality are crucial to ride the generative AI innovation wave.​​ This can help organizations stay ahead of the curve by being competitive, mitigate vendor risks, and customize solutions for unique use cases.

By allowing organizations to build rapidly with optionality, DataRobot empowers fearless innovation both now and into the future with robust visual experimentation capabilities and support for leading models. 

Our Multi-Provider LLM Playground is the first-of-its-kind visual comparison interface with out-of-the-box access to external LLM services, including Google PaLM, Azure OpenAI, AWS BedRock, as well as the option to bring your own, custom models.

The Playground allows you to easily compare and experiment with different generative AI ‘recipes’ that may include any combination of foundation models, vector databases, and prompting strategies tailored to your needs. And with the freedom to continuously adopt cutting-edge advances as they emerge, you can deliver impactful models at unmatched speeds without being locked into any single technology ecosystem. 

To further boost the velocity of your generative AI experiments, our AI Accelerators with expert-designed templates allow you to kickstart generative AI projects and dramatically shorten time to value. 

We help organizations continuously accelerate generative AI development and augment their ecosystem with turnkey building blocks and seamless integrations. Our library of expert-designed Generative AI Accelerators helps you kickstart development by packaging proven reusable code snippets. 

These Accelerators can help you extend foundational models with proprietary data for security, build a RAG application, add custom metrics, monitor models, or embed your generative AI solution into a communications app. These readymade templates enable rapid time-to-value. 

We also complement your existing tech stack by allowing you to easily leverage existing enterprise messaging tools like Slack and Microsoft Teams to host your generative AI solutions and facilitate user adoption. Integrations with Databricks and BigQuery reduce data wrangling time. With domain expertise encoded into reusable accelerators and ecosystem interoperability, DataRobot is the fastest path to generative AI impact. Our robust library of prebuilt capabilities and complementary ecosystem integrations empower enterprises to jumpstart delivery and maximize results.

The generative AI opportunity is immense, but realizing it requires the right platform. DataRobot helps you operate AI with correctness and control, govern with full transparency and oversight, and build rapidly with flexibility to quickly put any generative AI solution into production. Our robust support for state-of-the-art foundation models empowers you to deliver high-impact solutions today while retaining the flexibility to innovate boldly into the future.

Getting Started

Experience generative AI success for yourself – start a free trial today to build, operationalize, and govern generative models with confidence with Datarobot.

Our experts are also available for 1:1 tailored demonstrations showing how DataRobot can empower your specific AI initiatives. Book a demo for a deep dive into how our new Generative AI offerings can help you propel ahead.

The generative journey is just the beginning. We look forward to partnering with you to maximize results and uncover new opportunities. Let’s realize the full potential of generative AI together.

The post Closing the Generative AI Confidence Gap with DataRobot appeared first on DataRobot AI Platform.

It’s incredibly hard for already tapped out machine learning and data science teams to scale. They are now not only being overloaded with LLM demands, but face being hamstrung with LLM decisions that may risk future headaches and maintenance, all while juggling existing predictive models and production processes. It’s a recipe for production madness. 

This is all exactly why we’re announcing our expanded AI production product, with generative AI, to enable teams to safely and confidently use LLMs, unified with their production processes.  Our promise is to enable your team with the tools to manage, deploy, and monitor all your generative and predictive models, in a single production management solution that always stays aligned with your evolving AI/ML stack. With the 2023 Summer Launch, DataRobot unleashed an “all-in-one” generative AI and predictive AI platform and now you can monitor and govern both enterprise-scale generative AI deployments side-by-side with predictive AI. Let’s dive into the details!

AI Teams Must Address the LLM Confidence Problem

Unless you have been hiding under a very large rock or only consuming 2000s reality TV over the last year, you’ve heard about the rise and dominance of large language models. If you are reading this blog, chances are high that you are using them in your everyday life or your organization has incorporated them into your workflow. But LLMs unfortunately have the tendency to provide confident, plausible-sounding misinformation unless they are closely managed. It’s why deploying LLMs in a managed way is the best strategy  for an organization to get real, tangible value from them. More specifically, making them safe and controlled in order to avoid legal or reputational risks is of paramount importance. That’s why LLMOps is critical for organizations seeking to confidently drive value from their generative AI projects. But in every organization, LLMs don’t exist in a vacuum, they’re just one type of model and part of a much larger AI and ML ecosystem.

It’s Time to Take Control of Monitoring All Your Models

Historically, organizations have struggled to monitor and manage their growing number of predictive ML models and ensure they are delivering the results the business needs. But now with the explosion of generative AI models, it’s set to compound the monitoring problem. As predictive and now generative models proliferate across the business, data science teams have never been less equipped to efficiently and effectively hunt down low-performing models that are delivering subpar business outcomes and poor or negative ROI.

Simply put, monitoring predictive and generative models, at every corner of the organization is critical, to reduce risk and to ensure they are delivering performance—not to mention cut manual effort that often comes with keeping tabs on increasing model sprawl. 

Uniquely LLMs introduce a brand new problem: managing and mitigating hallucination risk. Essentially, the challenge is to manage the LLM confidence problem, at scale. Organizations risk their productionized LLM being rude, providing misinformation, perpetuating bias, or including sensitive information in its response. All of that makes monitoring models’ behavior and performance paramount. 

This is where DataRobot AI Production shines. Its extensive set of LLM monitoring, integration, and governance features allows users to quickly deploy their models with full observability and control. While using our full suite of model management tools, utilizing the model registry for automated model versioning along with our deployment pipelines, you can stop worrying about your LLM (or even your classic logistic regression model) going off the rails.

We’ve expanded monitoring capabilities of DataRobot to provide insights into LLM behavior and help identify any deviations from expected outcomes. It also allows businesses to track model performance, adhere to SLAs, and comply with guidelines, ensuring ethical and guided use for all models, regardless of where they are deployed, or who built them. 

In fact, we offer robust monitoring support for all model types, from predictive to generative, including all LLMs, enabling organizations to track:

• Service Health: Important to track to ensure there aren’t any issues with your pipeline. Users can track total number of requests, completions and prompts, response time, execution time, median and peak load, data and system errors, number of consumers and cache hit rate.

• Data Drift Tracking: Data changes over time and the model you trained a few months ago may already be dropping in performance, which can be costly. Users can track data drift and performance over time and can even track completion, temperature and other LLM specific parameters.
  

• Custom metrics: Using custom metrics framework, users can create their own metrics, tailored specifically to their custom build model or LLM. Metrics such as toxicity monitoring, cost of LLM usage, and topic relevance can not only protect a business’s reputation but also ensure that LLMs is staying “on-topic”. 

By capturing user interactions within GenAI apps and channeling them back into the model building phase, the potential for improved prompt engineering and fine-tuning is vast. This iterative process allows for the refinement of prompts based on real-world user activity, resulting in more effective communication between users and AI systems. Not only does it empower AI to respond better to user needs, but it also helps to make better LLMs. 

Command and Control Over All Your Generative and Production Models

With the rush to embrace LLMs, data science teams face another risk. The LLM you choose now may not be the LLM you use in six months time. In two years time, it may be a whole different model, that you want to run on a different cloud. Because of the sheer pace of LLM innovation that’s underway, the risk of accruing technical debt becomes relevant in the space of months not years And with the rush for teams to deploy generative AI, it’s never been easier for teams to spin up rogue models that expose the company to risk. 

Organizations need a way to safely adopt LLMs, in addition to their existing models, and manage them, track them, and plug and play them. That way, teams are insulated from change.

It’s why we’ve upgraded the Datarobot AI Production Model Registry, that’s a fundamental component of AI and ML production to provide a completely structured and managed approach to organize and track both generative and predictive AI, and your overall evolution of LLM adoption. The Model Registry allows users to connect to any LLM, whether popular versions like GPT-3.5, GPT-4, LaMDA, LLaMa, Orca, or even custom-built models. It provides users with a central repository for all their models, no matter where they were built or deployed, enabling efficient model management, versioning, and deployment.

While all models evolve over time due to changing data and requirements, the versioning built into the Model Registry helps users to ensure traceability and control over these changes. They can confidently upgrade to newer versions and, if necessary, effortlessly revert to a previous deployment. This level of control is essential in ensuring that any models, but especially LLMs, perform optimally in production environments.

With DataRobot Model Registry, users gain full control over their classic predictive models and  LLMs: assembling, testing, registering, and deploying these models become hassle-free, all from a single pane of glass.

Unlocking a Versatility and Flexibility Advantage

Adapting to change is crucial, because different LLMs are emerging all the time that are fit for different purposes, from languages to creative tasks.

You need versatility in your production processes to adapt to it and you need the flexibility to plug and play the right generative or predictive model for your use case rather than trying to force-fit one. So, in DataRobot AI Production, you can deploy your models remotely or in DataRobot, so your users get versatile options for predictive and generative tasks.

We’ve also taken it a step further with DataRobot Prediction APIs that enable users the flexibility to integrate their custom-built models or preferred LLMs into their applications. For example, it now makes it simple to quickly add real-time text generation or content creation to your applications.

You can also leverage our Prediction APIs to allow users to run batch jobs with LLMs. For example, if you need to automatically generate large volumes of content, like articles or product descriptions, you can leverage DataRobot to handle the batch processing with the LLM.

And because LLMs can even be deployed on edge devices that have limited internet connectivity, you can leverage DataRobot to facilitate generating content directly on those devices too. 

Datarobot AI Production is Designed to Enable You to Scale Generative and Predictive AI Confidently, Efficiently, and Safely

DataRobot AI Production provides a new way for leaders to unify, manage, harmonize, monitor results, and future-proof their generative and predictive AI initiatives so they can be successful for today’s needs and meet tomorrow’s changing landscape. It enables teams to scalably deliver more models, no matter whether generative or predictive, monitoring them all to ensure they’re delivering the best business outcomes, so you can grow your models in a business sustainable way.  Teams can now centralize their production processes across their entire range of AI initiatives, and take control of all their models, to enable both stronger governance, and also to reduce cloud vendor or LLM model lock-in.

More productivity, more flexibility, more competitive advantage, better results, and less risk, it’s about making every AI initiative, value-driven at the core. 

To learn more, you can register for a demo today from one of our applied AI and product experts, so you can get a clear picture of what AI Production can look at your organization. There’s never been a better time to start the conversation and tackle that AI hairball head on.

The post DataRobot AI Production: Unifying MLOps and LLMOps appeared first on DataRobot AI Platform.

• CIOs, imagine having even more robust control over your entire cloud infrastructure.
• CDOs, think about how seamless data manipulation and insight extraction could be with the addition of generative AI. 
• AI Builders, envision a platform filled with tools that amplify your creativity and efficiency.

Here’s what’s new in our summer launch related to our platform and ecosystem integrations:

1. The Next Evolutions of Our Secure, Cloud-Native Platform

Great innovations can come from AI, but without proper scaling and safeguards, there is a risk of security and performance issues. We built a platform that utilizes cloud and container technologies to enable scalability and flexibility without sacrificing security. It can be deployed in the way best suited for your needs, whether via our managed SaaS environments, self-managed in your VPC environments, or your own hardware. Concerned about protecting your data? We have got you covered. Your sensitive information remains confidential, and users can interact with it without compromising their data or privacy.

Here are two major ways we’re helping your AI initiatives be more secure, without slowing down:

1. HIPAA Compliant Single-Tenant SaaS: Our Single-Tenant SaaS offering is now HIPAA compliant on both AWS and Azure. Healthcare providers can now access AI Platform as a managed service in the cloud region of their choice. This gives them the security and isolation typically associated with a self-managed deployment but with the convenience of our managed services.

2. DataRobot latest version is available for Self-Managed VPC Customers on Azure: Our latest version is now certified on Azure Kubernetes Service for Self-Managed customers who wish to run in their own VPC. This deployment dynamically scales modeling workloads based on consumption, lowering the total cost of ownership for the customer. 

2. Open Ecosystem and Interoperability – Your Way!

With broad ecosystem interoperability and open architecture approach, we are taking a step forward and introducing a new area of generative AI and more integrations and support across the entire AI lifecycle.

Generative AI-ready platform: Whether you’re working with predictive or generative AI across various environments, now, with our All-in-One Generative AI Platform, you get consistent experience for developing and managing predictive and generative AI across cloud data warehouses, data lakes, practitioner tools, and business applications.

Integrations in AI Experimentation: Enjoy the freedom to work with all the best tools and techniques, all in one place. 

• Azure OpenAI: Our hosted notebooks integrate with Azure OpenAI, offering unparalleled generative AI assistance, automated code generation, enriched datasets, valuable insights, and optimization, enhancing productivity and performance.
• Streamlit: Quickly prototype your preferred application in Streamlit either in a few clicks from our UI experience or with our templates hosted in our GitHub repo for deploying DataRobot-developed capabilities
• Cloud warehouses: We offer a secure connection to your cloud warehouse, allowing you to retain full control and security over environments such as Snowflake, while still permitting access through the utilization of OAuth, Key-Pair Authentication, and Service Accounts. Additionally, with the feature of push-down processing, you can leverage your data warehouse’s speed and security for in-data preparation and direct materialization. This comprehensive approach ensures a robust and efficient data management system tailored to meet various needs.
• Hugging Face: With generative AI and the rise of extracting valuable insights from unstructured data, now you can get more with our seamless integration with Hugging Face models.

Integrations in AI Production: Easily integrate with your entire technology stack. 

• Airflow: Orchestrate stages of the DataRobot machine learning pipeline using Airflow. From ingesting data for model building, retraining, or monitoring predictions to integrating DataRobot capabilities into larger pipelines and combining them with other services, empowers you. Seamlessly clean your data and store or publish the results to align with your project needs.
• MLflow: Bring metadata from MLflow, including support for Azure Databricks, directly into DataRobot. Enhance your governance strategies and enrich your model compliance documentation with custom content to align with your unique requirements.
• AzureML: Deploy trained models from the DataRobot registry to AzureML Managed Endpoints and then monitor their performance and behavior using DataRobot’s monitoring tools. This integration provides a cohesive and streamlined way to manage the entire lifecycle of your models.

3. Experience the DataRobot AI Platform

Ready to take control over your entire cloud infrastructure without giving up creativity, efficiency, and security?

Experience the power of predictive and generative AI like never before with our exclusive 30-day trial of the DataRobot AI Platform. Here’s what you can expect from your 30-day journey:

• A guided tour that will fast-track you through preparing data, running experiments, and testing your models so you can make data-driven decisions
• Unprecedented access to cutting-edge AI capabilities, from experimentation to production and generative AI.
  • Leverage ecosystem integrations to easily and securely access your data
  • Build custom chatbots in our generative AI workflows
  • Forecast future outcomes with time series modeling 
• Resources for success include hands-on labs, AI accelerators, and a Community of applied AI and product experts to provide guidance tailored to your business goals. 

You can learn about all of this and more from our summer launch recording. Don’t miss out on this opportunity to explore the future. Sign up now for your 30-day trial and embark on a journey that will redefine the way you work, create, and envision what’s possible!

The post Introducing the Latest Advancements in Our Platform and Ecosystem with Generative AI at DataRobot Summer Launch appeared first on DataRobot AI Platform.

Whether you’re a CIO steering the company’s tech strategy, a CDO managing large datasets, or an AI builder creating the next big thing, we’ve got something for you. Our advancements provide unparalleled opportunities to transform your AI initiatives and take your data teams to new heights. You can now extract real value from your AI investments like never before.

Let’s explore the key highlights from our Summer Launch in AI experimentation:

1. Transforming AI Experimentation

Time is our most valuable asset. And unfortunately, we can’t always focus on more innovative tasks. Now you can tap into automation for the routine or simple aspects of your work and free up time for more challenging and creative problem solving.

Prepare Modeling Data: Our platform simplifies the data preparation process like magic. With automation and advanced operations, you can quickly discover, test, and create quality data. Say goodbye to tedious data clean-up tasks, and watch how our platform optimizes your data and outcome for better performance.

Augment Modeling Data with Deep Learning: We’ve taken feature engineering to a new level. Auto-create thousands of features effortlessly with our model blueprints, all customized to incorporate your unique tasks. With the advent of generative AI, this now includes popular models for text and visual embeddings like RoBERTa, TinyBert, and MiniLM.

2. Versatile and Customizable Solutions for Generative and Predictive AI

Ever felt boxed in by limited tools that don’t accommodate your diverse development needs? ‘Build Your Way’ is not just a motto. It’s our promise. And it’s not limited to just predictive or generative AI use cases.

Build Your Way: From pre-generated and customized models to building by yourself with our advanced hosted notebooks, we’ve got you covered. Our platform fosters collaboration between code-first data scientists and low-code users, catering to diverse business needs.

Tackle Any AI Challenge: Our AI Platform supports various data types and challenges, from question answering to time series forecasting. Now you have the freedom to tackle any generative or predictive AI challenge.

3. Empowering AI Builders with Code-First Templates, Generative AI, and Deep Learning

Are you tired of jumping between tools and platforms to develop your AI projects? Do complex integrations and a lack of customizable solutions slow you down? We understand the challenges you face.

Deliver on Any Use Case: Our Notebooks empower you to create end-to-end code-based solutions, encompassing everything from generative AI and knowledge bases to predictive models. Seamlessly take your AI workflows from experimentation all the way to production.

Explore a Comprehensive Developer Playground: Looking to jumpstart your AI projects? We offer code-first templates and integrations you can borrow, customize, and make your own. These include AI Accelerators, templates for deploying DataRobot-developed capabilities to customized Streamlit apps, DataRobotX extensions, and Azure OpenAI integrations. Benefit from automated code generation, enriched data, insights, and optimization to quickly inspire your next project.

Harness Deep Learning Power: Conquer unstructured data challenges with our advanced deep learning capabilities. Foundation Models are now at your fingertips for preprocessing text-based tasks. Additionally, our platform seamlessly handles PDF ingestion and offers integration with Hugging Face models. Leverage OCR capabilities to model on scanned documents with just a click of a button.

Ready to ramp up your AI experimentation? Visit our Summer Launch page to watch the recordings and get started with DataRobot. We can’t wait to see what you’ll create!

The post Introducing the Latest Advancements in AI Experimentation with Generative AI at DataRobot Summer Launch appeared first on DataRobot AI Platform.

If you’re a CIO or CDO, you’re more than aware of the pivotal role you play in leading your organization. You’ve been instrumental in mapping the journey toward a data-driven, AI-powered future. Your passion for innovation, eye for efficiency, and leadership in decision-making are exactly what’s driving success in various aspects of the business. But it isn’t always clear how to demonstrate the ROI of your AI investments. We’ve built brand new capabilities with you in mind to help you make faster and better decisions, streamline internal processes, and build a data-driven culture.

And AI builders, we see you, too! Your craft, commitment, and collaboration are the beating heart of the organization’s success. The work you do now – building transparent, powerful AI solutions – has never been more critical. You’re not just developing technology. You’re defining the future, and working hand in hand with business stakeholders. This is your moment to shine.

Let’s explore the Summer Launch highlights crafted for the innovators, the thinkers, and the AI enthusiasts like you:

1. Create Interactive Generative and Predictive Apps

We’ve developed AI Apps for business users to extract value from AI. We have successfully achieved this with predictive models, but now we’re taking an even bigger leap. 

Your Ideas, Our Platform: With DataRobot AI Apps, you’ll find tools to bring your innovative ideas to life. Predictive models were just the beginning – now, we’re transforming generative AI into fully interactive experiences.

Collaboration Made Easy: Within a few clicks from our GUI you can launch a Streamlit sandbox that lets you and your teammates freely play, prototype, and perfect generative AI applications. 

2. Translate AI into Business Language 

We know that translating the output of a predictive model can feel like decoding a foreign language. Especially if you’re on the business side of things. It’s crucial that these insights are accessible to everyone, not just the tech-savvy. That’s why we are adding a new explainability layer to our AI Apps. 

Intuitive Insights: Our new Model App lets you to turn complex predictions into interactive, visually intuitive insights. Whether you’re an AI expert or just getting started, this tool helps you connect with the technology in new ways.

Building Trust Together: Transparency fosters trust. And our new features are all about bridging the gap between business users and AI builders. Now you have a single place for your team to collaborate, deliver results, and build a culture with a clear sense of shared purpose and vision.

An overwhelming amount of data leaders and business execs struggle to show value from their AI initiatives. You don’t have to be one of them. We invite you to explore even more by accessing the recorded session from the Summer Launch. Your journey into AI consumption and generative AI starts here, and we’re thrilled to be part of it with you.

The post Introducing the Latest Advancements in AI Consumption with Generative AI at DataRobot Summer Launch appeared first on DataRobot AI Platform.

It all boils down to a simple question: how can generative AI deliver tangible business value? A question that’s no different for teams implementing predictive AI. 

We, at DataRobot, have the privilege of witnessing firsthand the challenges faced by companies in realizing value with AI. The three biggest hurdles that we see are:

• Silos across generative and predictive AI lifecycles.
• The need to explore and evaluate new and rapidly evolving technologies. 
• Insufficient integration between the existing enterprise ecosystems and existing data and AI investments that undermines value generation.

At the same time, the pressure on AI teams to deliver on sky-high expectations of what’s possible with AI is building up. 

With the DataRobot Summer ‘23 Launch, we’re announcing innovations aimed at addressing these challenges today by delivering a consistent experience for development and management of generative and predictive AI projects across your cloud data warehouses, data lakes, practitioner tools, and business applications.

Generative and Predictive AI: the Best of Both Worlds

At DataRobot, we see the fusion of generative and predictive AI as one of the primary sources of future value, and we’ve combined the best of both to help you drive differentiated value at scale. 

To highlight this potential, as part of the Summer ‘23 Launch, we showcase an example of how we’re using predictive and generative AI to create GenAI solutions people will trust. 

With DataRobot, generative and predictive AI becomes a streamlined and effective process, designed to overcome the biggest challenges and roadblocks on the path to value. 

And that path all begins with your users and stakeholders… 

Build Interactive Generative and Predictive AI Apps Your Users Will Love 

In our new release, we’ve closed the gap between consumption, production, and experimentation. After all, what good is an AI solution if your users can’t use it, or don’t understand how to use it. 

With our newly introduced Streamlit app hosting you can build bespoke generative AI applications and then deploy and share them in an instant, with just a few lines of code. By tightly integrating consumption, experimentation, and production together you and your teams can volley back and forth between building your GenAI solution and prototyping the GenAI app experience quickly and easily. This makes it easy for you to curate rich interactive applications people want to use. But you don’t just have to build apps to get GenAI to your users. You can easily meet your users wherever they are by easily integrating generative AI into your organization’s operations and business systems—Slack, Salesforce, BI tools, and more— with just a few lines of code.

We’ve also made it effortless to create fully interactive, meeting-ready predictive AI insight apps without any code required. These insight apps include business and model templates, facilitating explanations of results in business-friendly language. With features like what-if analysis, optimization, and simulation of future scenarios, data science teams can effectively translate predictions into interactive, visually intuitive insights. 

Quickly Create AI with a Modern, Live Code-First Experience

We recognize the need to streamline AI experimentation and prototyping and so are investing even more into the DataRobot Notebooks capabilities—to help you focus on creating predictive AI and generative AI use cases. In the background, we handle the infrastructure and project organization, so you don’t have to. 

Our API-first integrations let you stay in the driver’s seat for your generative AI initiatives – you can manage LLM selection, safeguard data privacy, and control financial aspects of your generative projects. The flexibility and openness of our platform lets you use any LLM, use embedding methods and vector databases of your choice, and rapidly experiment and optimize your prompts to deliver an accurate, user-friendly experience, suitable for your specific generative AI use case. As a bonus, with our built-in notebooks solution, you no longer need to design, configure, manage, and scale infrastructure. 

Additionally, new deep learning and NLP features for text-based AI, like the seamless integration with Hugging Face models, end-to-end support for PDFs, and embedded foundational models for preprocessing tasks, further simplify the handling of unstructured data necessary for generative AI use cases.

To further improve the process, DataRobot Notebooks allows you to jumpstart any AI project with a variety of ready-to-use code snippets and libraries (Generative AI and Predictive AI Accelerators),  new task specific API commands, and the Azure OpenAI-powered code assistant (now available for public preview).

Unify AI Management and Governance Across your Ecosystem 

DataRobot has simplified the management of hybrid generative and predictive AI environments. It offers governance and monitoring capabilities that allow organizations to track model performance and ensure responsiveness. DataRobot AI Production offers a 360-degree view of all generative and predictive AI assets, irrespective of their deployment and origin. 

You can manage and “deploy” vector databases and API-based LLMs, create monitoring SLAs for generative AI projects, and organize both generative and predictive AI assets in a single registry, while the platform ensures unified and standardized compliance, security, and governance policies across all your AI assets and cloud-environments. And, to ensure generative AI costs don’t spiral out of control, DataRobot will help you monitor LLM usage.

Trust in Generative AI and Guarantee Performance

Building trust in AI is crucial for widespread adoption. One of the common challenges with generative AI is the potential hesitation to fully rely on automated content. Especially in light of news about erroneous generative AI outputs and AI hallucinations. 

DataRobot addresses these concerns with custom performance metrics and by combining predictive AI with generative AI. With DataRobot, you can go beyond basic monitoring – we let you define custom performance metrics like toxicity monitoring, or whether your LLM is staying “on-topic” to protect your business reputation. 

An Established Path for Generative AI Success

Many clients and prospects that we talk to are facing pressures to deliver their strategic plan for generative AI right now. 

And while our platform is built to help you do it yourself, from robust generative AI workflows to advanced monitoring and retraining capabilities, we also recognize the importance of applied AI expertise, which is hard to come by in this complex and relatively new space. 

DataRobot is one of the very few organizations that can deliver this expertise, with our decade of experience, thousands of customers, and tens of thousands of use cases. 

That’s why we’re introducing several enablement products that can help you deliver value with generative AI.

Generative AI Strategic Advisory and Technical Enablement Services

• Systematically identify and prioritize high-value opportunities with our generative AI roadmapping sessions.
• Quickly get all your leaders up to speed with our “Generative AI for Executives” program.

Generative AI Service Packages

• Learn how to build, optimize, and monitor generative AI applications at scale with live workshops and hands-on labs.
• Get dedicated support and fastrack your initiatives with our GenAI Execution Support.

Build a Unified Intelligence Layer Across your Cloud and Hybrid Environments 

We know that your AI projects don’t exist in a vacuum, which is why we continue to extend and enhance our integrations with your existing data and analytics infrastructure. 

We’ve enhanced our native integrations with Google and Snowflake. Now you can fastrack data preparation like deduplication, table joins, and aggregations all in Snowflake and Google BigQuery, without ever moving your data. We ensure that you retain full control and security over your Google and Snowflake environments, while still allowing access, by utilizing OAuth, Key-Pair Authentication, and Service Accounts.

We’ve also made it easy for your streamline AI Production pipelines – ingest Airflow data, run predictions on Databricks Spark clusters with simple python code, or deploy models to AzureML in a single click.

Other updates include our Single Tenant SaaS offering that is now available both on Azure and AWS to help you maintain complete control over your infrastructure, including a HIPAA-compliant option for AWS, as well as native support for Azure Kubernetes Service for Azure users.

These are just some of the highlights of our latest release. I recommend checking out the on-demand Summer ‘23 Launch event to learn more about all of our new features and hear from two of our customers who have already embarked on their GenAI journey with DataRobot. 

This is just the beginning of what’s to come at DataRobot. We have so much more that we’re working on for fall, so stay tuned.

The post Summer ‘23 Launch Recap: Powering Generative and Predictive AI from Vision to Value appeared first on DataRobot AI Platform.

Many that I speak to are trying to transition their teams and focus to value-driven AI, and view our world-class data scientists, AI strategists, and AI engineers as their copilots in this journey. Earlier this year, we launched two new ways to tap into this expertise:

• AI Accelerators: Code-first, modular building blocks to jumpstart your AI projects.
• Service Packages: Curated offerings to rapidly get you to the next stage of ML maturity.

Jumpstart Your AI Projects with AI Accelerators

For over a decade, we’ve worked with data teams on thousands of ML projects across the world’s most recognized organizations. We’ve heard our customers’ feedback about what works, and we know what’s needed to move the needle with ML. That’s why we’ve created AI Accelerators.

AI Accelerators are designed to help speed up model experimentation, development and production using the DataRobot API. They codify and package our expertise in building and delivering successful ML projects into repeatable, code-first workflows and modular building blocks. AI Accelerators are ready right out-of-the-box, work with the notebook of your choice, and can be combined to suit your needs.

We’ve designed them to help you: 

1. Get the best data science techniques right at your fingertips.

Think of AI Accelerators like modular lego building blocks built by the best data scientists in the world, curated specifically for you. They provide a template that is simple to understand and use. Giving you the best ways to set up a problem, you can do things like customize a view to your liking, or rank models by ROI. Getting started is as straightforward as finding an AI Accelerator that interests you, and copying and pasting the GitHub URL link into your notebook. You then have a ready to go.

2. Jumpstart a new AI or ML project.

Some AI Accelerators are designed to be functional and quickly get you the insights you need to experiment. Others help you stretch your skills and move beyond model-centric ML approaches to value-centric ones. Still more showcase unique DataRobot capabilities that positively change the way ML practitioners can solve problems. Whatever use case you have in mind, whether that’s a recommendation engine, or cold-start forecasting the chances are that we’ve done it or something close to it and have an AI Accelerator you can use, so you never have to start from a blank notebook again. It’s also not a static resource. We add new AI Accelerators every week to keep our library dynamic and up-to-date, and we invite comments on what you’d like to next see.

3. Fine-tune your projects and maximize your existing ecosystem.

Our AI Accelerators are modular. They can be fine-tuned to specific problems and combined to get the precise insights you need for your projects. And because the DataRobot AI Platform is designed to give you choice and unify your teams and ecosystems, AI Accelerators help you get the most out of existing data and infrastructure investments.

They are built for every major cloud data warehouse, as well as popular, open-source toolkits and different data types. For example, the Automated Future Discovery Accelerator provides a step-by-step pipeline from data to deployment. This is ideal if you have Snowflake and are struggling with an ML project where data exists in many tables. And because AI Accelerators can be used in the notebook of your choice, you can use them in Jupyter, Databricks, or native DataRobot Notebooks.

With AI Accelerators, never having to start a project from scratch anymore. Instead, you will have all the applied expertise of our technical teams at your disposal.

To see AI Accelerators in action, visit DataRobot.com/AIAccelerators.

Unlock the Next Stage of Your ML Journey with Specialized Partners and Our New Service Packages 

No matter where you are on your AI journey, from early days to quite advanced, there are challenges throughout the journey and they change as you move up the maturity curve. We’ve seen them firsthand working with our customers over the years. 

With this knowledge we’ve curated a set of strategic channel partners and a new set of service offerings designed to meet you where you are, help you de-risk your ML investment, accelerate your journey up the AI maturity curve, and give you a strong foundation for operationalizing at scale with the DataRobot AI Platform.

Our curated set of partners have specialized industry expertise with AI and ML as well as deep backgrounds in helping you modernize your AI and ML lifecycle. If you’re needing help migrating your models from on-prem to the cloud, integrating our Production into your ecosystem, or establishing compliance workflows with your model risk management with DataRobot, they have done it. You have a set of trusted partners that you can turn to to help you throughout your AI and ML journey. 

In addition, we are offering a set of service packages to help you get started with DataRobot and get started fast. Our foundational elements quickly get you where you want to be in your AI journey – helping your teams develop and deploy models and generate value within just 90 days:

We also offer specialty packages to focus on what’s most important to you and help you achieve great outcomes.

The combination of AI Accelerators, Service Packages, and our partner ecosystem lets you leverage our expertise to tap into our proven success delivering complex, specific, and targeted requirements in a way that works for you. The result? Your organization will be able to better balance people, process, innovation, and tech at scale and rapidly accelerate its path to value with AI.

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Slowing down machine learning (ML) projects to collect documentation manually can cause friction, block other projects, and delay ROI. So although model documentation is a best practice all ML teams should embrace, it can be painful to put into practice.

It’s also hard to handle knowledge loss from turnover or changes in tech stacks as data science teams evolve. With little notice, you might lose the context for how a critical model operates, leaving you ill-prepared for the future.

Every business needs the ability to audit and explain the models they have deployed, plus regulatory guidance across many industries requires strong documentation. Yet keeping up with regulations means making sure data scientists and model risk teams are in lockstep, or you’ll delay or need to retrace steps in your deployment process.

DataRobot 9.0 can help you meet these challenges. We’re expanding our governance and compliance offerings to be inclusive of all models across your organization, and ensure every business critical ML asset is governed properly. The three key capabilities are:

• Extending our existing automated documentation offering, which is unique to us, to include almost all custom models, even models built or deployed outside of DataRobot.
• New ML Flow Import and extensible features for model documentation, to enhance your model governance and flexibility.
• Bias Mitigation, so you can quickly increase your ability to deliver safe and sound models.

Save Time by Automatically Creating Compliance Documentation 

If you have undocumented models running throughout your organization, on different clouds or platforms, you’re exposing your team to additional risk, especially if there’s knowledge loss across your team. Until now, we offered full model compliance documentation capabilities and insights for models running in DataRobot. These save data scientists hours of work: with the touch of a button, they automatically document any model’s behavior for compliance.

Now, you can just as easily automatically create compliance documentation for externally hosted models. DataRobot 9.0 helps you:

• Quickly connect to a range of systems and document your portfolio of models, on almost any infrastructure – and all without any code or infrastructure changes.
• Bring together all the information known about a model, from multiple systems and frameworks.
• Customize compliance documentation to adhere to enterprise or industry-specific requirements.

Whether you’re working in a highly regulated industry, or simply practicing good model governance, this feature helps ensure all models tied to business critical applications are managed and reported on.

Enhance Governance and Compliance with MLflow Integration

In many enterprises, there’s not just one way of experimenting and building models. While such freedom can be an enabler, it’s also a problem when critical information is stored across multiple tools.

DataRobot 9.0 adds MLflow import and extensible features for model documentation. This enhances your model governance and flexibility, and helps your AI builders save time and increase quality.

MLflow integration lets data scientists sync metadata they have created and bring key pieces of data, benchmarks and statistics from MLflow to DataRobot. This can then instantly be used to assist with model governance, documentation, and reviews. If there is another source besides MLFlow, the new import API can be used to bring in metadata from there as well.

Model documentation can be easily extended with other datasets, charts or tables relevant to your models, such as scenarios generated from a Notebook. This means you retain flexibility and can continue to build in the way you think is best, while keeping DataRobot AI Platform as the central location to help you manage, monitor, document, and govern your suite of models.

Ensure Fairness and Correct Model Discrimination with Bias Mitigation

Bias is a risk factor in any model you will deploy for customer decisions. And with governments exploring bias regulations, organizations are increasingly interested in understanding model discrimination based on features such as race, gender, or income. It’s therefore critical to detect when bias exists in your AI models, measure it, and have real strategies to fix it.

DataRobot 9.0 introduces Bias Mitigation, so you can quickly increase your ability to deliver safe models. It includes the ability to choose from multiple strategies for squashing bias in a model to ensure fair treatment for the classes you care about. So whether you’re driven by regulatory pressures, customer and supplier expectations, or are simply designing ethical and fair AI systems, you can now prevent bias from being an unknown risk as you deploy new models.

Equip Your Data Scientists with the Tools They Need

It’s vital to help your organization document models, uphold high ethical standards, and stay ahead of bias regulations. With DataRobot 9.0, you can equip your data scientists with these tools, and accelerate model documentation, manage risk, and fix bias – all while retaining existing freedoms and flexibilities, and saving time.

To learn more and see how these new DataRobot 9.0 features can benefit your organization, watch our Compliance and Governance in Production session.

The post Meet the Challenges of Assured Compliance and Governance with DataRobot 9.0 appeared first on DataRobot AI Platform.

Today, we want to share what we learned and established as the key requirements for an AI Platform to consistently deliver value from investments in AI. We are also thrilled to share the innovations and capabilities that we have developed at DataRobot to meet and exceed those requirements. 

Why model-driven AI falls short of delivering value

Teams that just focus model performance using model-centric and data-centric ML risk missing the big picture business context. That focus often leads to over-rotatation on building a better algorithm or neural-network or finding more data to improve model performance as opposed to the improvement of business performance. This narrow focus can lead to accurate and true insights that are not really useful, leaving business stakeholders feeling frustrated. What AI teams really need to do is to think about the business problem first and use the tools to meaningfully collaborate with business stakeholders to ensure the project doesn’t fall short of meeting expectations.

What Do AI Teams Need to Realize Value from AI?

• Better ways to experiment and collaborate with the business: AI Teams need the right tools and processes to be able to iterate quickly on many ML problem statements, compare different approaches, cohorts, and collaborate with the SME’s in their business to learn from and iterate on building the model, simply and without huge manual effort.
• Reliable and repeatable ways to scale to production within real-world constraints: To get to sustained value, teams need to be able to get the models and insights into production, in front of the decision making users. This means they need the tools that can help with testing and documenting the model, automation across the entire pipeline and they need to be able to seamlessly integrate the model into business critical applications or workflows.
• Best-Practice Compliance and Governance: Businesses need to know that their Data Scientists are delivering models that they can trust and defend over time. This means implementing safety best practices proactively, and applying the highest governance standards without slowing down the process.
• An AI platform that works well with a broad enterprise ecosystem: A platform that seamlessly integrates with the substantial investments businesses have already made in infrastructure, practitioner tools, data platforms and business applications.
• Expert advice to navigate the challenges and complexities of AI: AI Teams should not have to go it alone when it comes to driving value. They need the right expertise at the right stage as they work up the AI maturity curve. 

DataRobot AI Platform Delivers on Value-Driven AI

In our new 9.0 DataRobot AI Platform release we’ve broken down the barriers that exist across the ML lifecycle. We’ve abstracted away the complexity and streamlined the end to end ML lifecycle so teams can collaborate easily, rapidly experiment, and most importantly get any model into production fast. 

• Collaborative Experimentation Experience – the new experience, called the Workbench, comes packed with new capabilities such as new integrated data prep for modeling and notebooks providing a full code-first experience. This helps teams collaborate over all the ML assets in one location so they can experiment faster.
• Value at Production Scale – DataRobot’s Production is more than just basic MLOps tooling and now new features are making it even easier and faster to scale and maintain model performance. New GitHub Marketplace Action for CI/CD integrates DataRobot into your existing DevOps practices, custom inference metrics for tracking business performance, and an expanded suite of drift management capabilities ensure models perform as expected. 
• Assured Compliance and Governance – DataRobot has always been strong on ensuring governance. We’ve extended our governance and compliance capabilities to support models built outside of Datarobot with new compliance documentation for External models, MLflow experiment metadata integration, and bias mitigation capability to give teams oversight and control over all of their AI artifacts.  
• Broad Enterprise Ecosystem – The DataRobot AI Platform is an open system supporting key integrations to help businesses maximize value from their existing investments. New Snowflake integrations and the SAP joint solution have tightened the data to experimentation to deployment loop. While new Kubernetes support standardizes and simplifies installation. When it comes to deploying the platform, customers get the broadest range of infrastructure choices, whether it’s deploying the platform self-managed on-premises, or in a public cloud VPC or fully managed multi-tenant SaaS, and single-tenant SaaS – we have an option that will meet all needs.
• Applied AI Expertise – In addition to all of the new platform innovations, we’re also taking 1000s of person-years of AI implementation experience and packaging it up in two new ways – our new DataRobot services packages that will help our customers realize value within 90 days, and our new AI Accelerators, which are code-first, modular building blocks and solution templates for specific use cases that are designed to help you jumpstart your AI projects and results. 

Explore the New DataRobot AI Platform

Dig deeper and explore our new product details on the website, and stay tuned as we continue the 9.0 blog series and deep dive into the new 9.0 features over the next few weeks. Or, reach out to our team to schedule a demo to see the and many more of our new features in-depth. 

We’re only just getting started.

The post How the DataRobot AI Platform Is Delivering Value-Driven AI appeared first on DataRobot AI Platform.

Today, DataRobot unveiled a new AI platform designed to help businesses derive measurable value from AI – something that too many organizations today have been unable to achieve. 

The reality is that the current ML lifecycle process is broken. On average, 54% of AI projects make it from pilot to production,¹ meaning that nearly half of AI projects fail. Massive investments in AI struggle to deliver tangible value as they are too-often met with brittle, hand-stitched tooling, data and organization silos, and governance and compliance blind spots – making scalable, continuous and explainable success nearly impossible.

DataRobot is on a mission to change that.

We’re doubling down on value and embedding it in everything we do at DataRobot with Value-Driven AI, a unique and collaborative approach that helps businesses deliver measurable value from their AI investments by enabling various functional teams to work together on DataRobot’s enterprise-ready AI platform.

We are stepping boldly into this new era of Value-Driven AI and unveiling new technology breakthroughs, deeper ecosystem partnerships and redesigned service offerings aimed at closing the last mile gap from vision to value. We are offering customers rapid experimentation and value identification, with both code-first and no-code approaches. We are also reducing enterprise risk with automated model compliance and offering new out-of-the-box value with AI Accelerators and tailored services packages that meet customers where they are on their AI journey, enabling them to jumpstart projects and attain results. 

DataRobot is the leader when it comes to helping businesses derive value from AI. Today, we celebrate the data leaders from customers including BMW Group, Polaris and Inchcape who share how they put Value-Driven AI to work during our virtual event, From Vision to Value: Creating Impact with AI. We’re also thrilled to showcase our unique innovations with leading industry voices such as Snowflake, SAP, Microsoft, and more.

I believe that focusing relentlessly on value is what will enable us to bring the full potential of AI to life. Keep an eye out for the exciting things we’ll build as we realize this vision.

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

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