AI Forecasting Can Overcome Real-World Complexity and Integrate Existing Processes  

While AI-powered forecasting can help retailers implement sales and demand forecasting—this process is very complex, and even highly data-driven companies face key challenges:

• Scale: Thousands of item combinations make it difficult to manually build predictive models 
• Real-World Complexity: The constant changing conditions of demand swings, uncontrolled factors, such as the COVID pandemic, and internal activities are hard to forecast against and can render models useless overnight
• Integration and Disparate Tools: Within the same organization you might have different teams working with different technologies, tools, and frameworks, so there is a challenge in consistently of forecasting answers, making alignment more difficult and slowing down time to value 

Why is it so difficult to do it manually? For example, just to forecast sales on a shirt with five different sizes in five different colors gives you 25 combinations. Now, add over 5,500 store locations with a 7-day rolling forecast—which actually takes 42 days to forecast—and you’ll get more than 5 million predictions. 

This is where the DataRobot AI platform can help automate and accelerate your process from data to value, even in a scalable environment. Let’s run through the process and see exactly how you can go from data to predictions. 

The use case will be forecasting sales for stores, which is a multi-time series problem—both supervised learning and time series regression. In this use case, the forecasting will be on a day resolution, but for other Time Series Forecasting, the resolution can be different, such as a month, a year, etc.

The process I will present will be using the DataRobot GUI. For code-first users, we offer a code experience too, using the AP—both in Python and R—for your convenience.

Setting up a Time Series Project

The machine learning life cycle always starts with the dataset. Import the data from various options: from a local file or URL or create a data connection with diverse data sources, such as Snowflake or Amazon Redshift, and upload it to the AI Catalog, which helps manage datasets, versioning, and shared capabilities with other users. 

If your dataset is not in time order (time consistency is required for accurate Time Series projects), DataRobot can fix those gaps using the DataRobot Data Prep tool, a no-code tool that will get your data ready for Time Series forecasting. 

Once the data is ready, DataRobot will do some initial exploratory data analysis –  in addition to a data quality assessment of the data – to get a deeper understanding of the dataset prior to model training. As you dive in, you can look at the distribution of each feature, identify outliers, target leakage, or missing data, create a var transformation, better understand what those features may be, and more.

Once the data is ready to start the training process, you need to choose your target variable. When we choose ‘sales’ it’s immediately recognized as a regression problem. Note: the DataRobot platform supports both supervised and unsupervised learning. 

Next, you need to set up the time-aware modeling settings, including the Feature Derivation Window (FDW), or how long of a period you may need to generate features that would be relevant for your problem. Then generate a Forecast Window—which shows the futures period you want to forecast—and the operationalize gap (the period of time for which forecasted predictions can’t be made actionable).

Calendars can also help you understand seasonality and incorporate it into the forecast model. For example, how holidays and events affect forecasting. If you don’t have your own calendar, DataRobot will generate one based on your location. 

Advanced settings allow you to configure additional parameters to the forecasting project, like “known in advance” (KA) features—that don’t change after the forecast point—such as marketing promotions, tourist events, and more.

I could also configure the project based on segment, which will result in multiple projects “under the hood.” Once the segments are identified and built, they are merged to make a single-object—the Combined Model. This leads to improved model performance and decreased time to deployment.

The DataRobot Training Process

Now that all our settings are in place, we are ready to go. To begin training your model, just hit the Start button and let the DataRobot platform train ML models for you. Based on the FDW, new features will be generated. You can dive into each one of them and explore the feature lineage, allowing you to see the transformation from the original feature to the one that was created.

You can also see the correlation between each feature and the target variable. In the background, models are being trained in parallel for efficiency and speed—from Tree-based models to Deep Learning models (which will be chosen based on your historical data and target variable) and more.

To accelerate the process, you can also increase the number of modeling workers (number of jobs running at the same time).

After your project has been finalized, you can review all the models that were trained. The order of the models will be based on the project’s metric—and can be changed based on your configuration. In the training process, different models with different feature lists and training periods were tested, and only the best performing models continued to the next round, resulting in the first model listed in the leaderboard, which is the recommended model by DataRobot for deployment. 

The model training process is not a black box—it includes trust and explainability. You can see the entire process from data to predictions with all of the different steps—as well as the supportive documentation on every stage and an automated compliance report, which is very important for highly regulated industries. 

Model Performance, Insights, and Explainability

Do you want to see how your model is performing? Looking at Accuracy Over Time allows you to see the actuals versus the predictions of the model—and shows how seasonality and calendar events are incorporated. Advanced Tuning, meanwhile, will enable you to further tweak the model. 

Are your business decisions aligned with the model results? On a macro level, see which features drive the model’s outcome. On a micro level, discover how a change in a specific feature affects the target variable. For example, choosing the ‘tourist event’ feature shows us that holding such events results in higher sales.

All of the from the platform can also be exported outside of DataRobot. 

The Deployment Process

Now it’s time to put our model into production and get some predictions—and unlock real value and ROI. There are multiple ways to do so. Perform ad hoc analysis on your dataset and preview the predictions for the upcoming seven days for a specific series. You can also deploy the model using the DataRobot API—ensuring a smooth and fast connection between data scientists and the IT team.

In general, using DataRobot MLOps, you can also see models that you currently have in production—from different training and deployment environments. Check for model accuracy and data drift and inspect each model from governance and service health perspectives, respectively. If your model is decaying, you can replace it with a more accurate challenger model—which can be monitored with automatic rules and notifications. 

Close the loop by connecting your predictions into any database—including batch or real-time predictions using the DataRobot API. And to connect to the business, you can connect predictions to your business application. For example, I used Tableau in this use case. On the top, you can see the overall forecasted sales for the next seven days in all the stores combined, and on the bottom, you have each series (each store) displayed individually. 

Accelerate the Machine Learning Life Cycle with AI-Powered Forecasting

Time Series Forecasting might be the most powerful aspect of machine learning available to organizations today. The ability to strategically plan for what’s to come can set you apart from your competition. 

With accessibility from the UI, but also from code—and with Trusted AI and explainability to help increase the value and unlock ROI—the DataRobot platform can help your organization quickly make accurate predictions and get actionable insights.

To see a demo on how you can leverage AI to make forecasting better, and accelerate the machine learning life cycle, please watch the full video, AI-Powered Forecasting: From Data to Consumption.

The post Better Forecasting with AI-Powered Time Series Modeling appeared first on DataRobot AI Platform.

Initiate Robust Customer Engagement by Offering the Right Incentives

In a more traditional marketing approach, you’d take your customer list and segment it into distinct groups based on shared characteristics like region. You might then select a specific coupon for everyone in that segment to increase quarterly sales. 

The trouble with this approach is that it often overlooks the unique desires and characteristics of individual customers. What might be the right incentive to buy for one customer may not entice a nearly identical customer. You need to find a more exact way to put the right material in front of each prospect to maximize engagement.

How Can AI Target the Right Prospects with Sharper Personalization? 

Artificial intelligence (AI) can help improve the response rate on your coupon offers by letting you consider the unique characteristics and wide array of data collected online and offline of each customer and presenting them with the most attractive offers. 

You may learn that customers who were grouped together using a traditional approach to market segmenting look very different after a machine learning assisted analysis. 

To solve this problem, you can leverage datasets with demographic and transactional information along with product and marketing campaign details. Ingest your data and DataRobot will use all these data points to train a model—and once it is deployed, your marketing team will be able to get a prediction to know if a customer is likely to redeem a coupon or not and why. 

All of this can be integrated with your marketing automation application of choice. For example, you could set up a data pipeline that delivers DataRobot predictions to HubSpot to automatically initiate offers within the business rules you set. You could also use the predictions to visualize a BI dashboard or report for your marketing managers to access. 

From there, your marketing team can prioritize and target the clients that will receive coupons. DataRobot also gives you the details about how it came to that conclusion. This explainability of the predictions can help you see how and why the AI came to these predictions.

Get Started with DataRobot and Choose Your Target Variable

To get started with DataRobot, connect or import the datasets you already have from your existing mar-tech, CRM, and offline sales and marketing channels. You can upload all these datasets in our AI Catalog and start a project from there.  

In this case, the datasets include demographic information from customers, plus a dataset with further information on the marketing campaigns, and two others that will provide information on past transactions and product information at the SKU level. All of these files have a combination of numeric, categorical, and date features, but remember that DataRobot can also handle images, text and location features.

I started my project with a simple data set with historical information of coupons sent to clients and a target variable that captured information about whether the coupon was redeemed or not in the past. As you upload your data, DataRobot will do some initial exploratory data analysis to get a deeper understanding of the dataset prior to model training. Next, choose your target variable—in this instance it is automatically detected as a classification problem and an optimization metric is recommended. 

Automate Feature Engineering 

DataRobot will accelerate machine learning by automating feature engineering, often considered one of the most laborious and time-consuming steps along the path to value. Traditional approaches are manual and require domain expertise. This means building hundreds of features for hundreds of machine learning algorithms—this approach to feature engineering is neither scalable nor cost-effective. 

In contrast, DataRobot simplifies the feature engineering process by automating the discovery and extraction of relevant explanatory variables from multiple related data sources. This allows you to build better machine learning models in less time and increase the pace of innovation with AI.

I started with a single dataset containing basic information on coupons redeemed or not by customers and enhanced it by joining additional secondary datasets from all the other relevant data sources. You can create a relationship configuration by using simple key joins or more complex multi-key joins between your datasets. 

Training and Testing Different AI Models 

As DataRobot starts building predictive models, a large repository of open source and proprietary packages will experiment with various modeling techniques. The model selection process will test several models to see which one is likely to yield the best results. Increase your workers count to build models in parallel with a large repository of open source and proprietary packages. 

DataRobot will try out various modeling techniques and the models that will survive the first round will be fed more data and move on to the next round. Ultimately, only the best algorithms that solve specific problems will survive. 

Looking at the model leaderboard, you can see that DataRobot built over 100 models and chose a winner. You can survey the model blueprint and see all of the pre-processing steps that were taken to get it ready.

If you want more information, click on the links and DataRobot will generate clear documentation that explains the details of what DataRobot did within each particular step. Now, if you want to move forward with the model, the next step is to evaluate the fit.

Evaluate Model Fit and Understand How Features Are Impacting Predictions

The evaluation tab gives us some handy evaluation tools. The lift chart shows the fit of the model across the prediction distribution, while an ROC curve explores classification, performance, and statistics related to a selected model at any point on the probability scale. 

Once you’ve evaluated the fit of your model, the next step is to understand how the features are impacting predictions. Feature Discovery allows you to significantly improve the model’s overall performance by intelligently generating the right features for your models. 

For this marketing offer model, the most important features are the average discount offer that a customer received in the last 30 days, the day of the month that a transaction takes place, the duration of a campaign, and other features with average sums and minimum values. 

If you open these features, you can access feature lineage, which visualizes how a feature was created. 

If the model looks good, it’s time to deploy it. DataRobot lets you deploy the model to an endpoint with an API that can serve up predictions in real time. If you click ‘Deployments’ you can see the DataRobot MLOps dashboard.

In this example, 17 active deployments are being monitored. By clicking on the Marketing Deployment, which has been serving predictions for a few days now, you can see an overview screen, which gives you:

• A top-line view on service health
• A look at data drift
• A clear picture of the model’s accuracy

You also have governance information, such as when and who created the deployment and who was involved in the review and approval workflow, which is important for audits and risk and compliance purposes.

Integrate Model Predictions with Your Existing Technology

After the model is in place and returning results, you can use a DataRobot API to integrate the model predictions with your existing mar-tech and CRM systems, like Tableau or HubSpot. This allows you to automate the process and offer targeted promotions to the specific customers who are most likely to use them. 

To see how you can leverage AI to target your prospects and customers better with the promotions they’re most likely to accept, please watch the full demo video: DataRobot Platform Overview: Solving Business Problems at Scale.

¹ https://www.statista.com/outlook/dmo/digital-advertising/worldwide#ad-spending

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Is There Such a Thing as Unbiased AI?

A common concern with AI when discussing governance is bias. Is it possible to have an unbiased AI model? The hard truth is no. You should be wary of anyone who tells you otherwise. While there are mathematical reasons a model can’t be unbiased, it’s just as important to recognize that factors like competing business needs can also contribute to the problem. This is why good AI governance is so important.

So, rather than looking to create a model that’s unbiased, instead look to create one that is fair and behaves as intended when deployed. A fair model is one where results are measured alongside sensitive aspects of the data (e.g., gender, race, age, disability, and religion.)

Validating Fairness Throughout the AI Lifecycle

One risk mitigation method is a three-pronged approach to mitigating risk among multiple dimensions of the AI lifecycle. The Swiss cheese framework recognizes that no single set of defenses will ensure fairness by removing all hazards. But with multiple lines of defense, the overlapping are a powerful form of risk management. It’s a proven model that’s worked in aviation and healthcare for decades, but it’s still valid for use on enterprise AI platforms.

The first slice is about getting the right people involved. You need to have people who can identify the need, construct the model, and monitor its performance. A diversity of voices helps the model align to an organization’s values.

The second slice is having MLOps processes in place that allow for repeatable deployments. Standardized processes make tracking model updates, maintaining model accuracy through continual learning, and enforcing approval workflows possible. Workflow approval, monitoring, continuous learning, and version control are all part of a good system.

The third slice is the MLDev technology that allows for common practices, auditable workflows, version control, and consistent model KPIs. You need tools to evaluate the model’s behavior and confirm its integrity. They should come from a limited and interoperable set of technologies to identify risks, such as technical debt. The more custom components in your MLDev environment you have, the more likely you are to introduce unnecessary complexity and unintended consequences and bias.

The Challenge of Complying with New Regulations

And all these layers need to be considered against the landscape of regulation. In the U.S., for example, regulation can come from local, state, and federal jurisdictions. The EU and Singapore are taking similar steps to codify regulations concerning AI governance. 

There is an explosion of new models and techniques yet flexibility is needed to adapt as new laws are implemented. Complying with these proposed regulations is becoming increasingly more of a challenge. 

In these proposals, AI regulation isn’t limited to fields like insurance and finance. We’re seeing regulatory guidance reach into fields such as education, safety, healthcare, and employment. If you’re not prepared for AI regulation in your industry now, it’s time to start thinking about it—because it’s coming. 

Document Design and Deployment For Regulations and Clarity

Model risk management will become commonplace as regulations increase and are enforced. The ability to document your design and deployment choices will help you move quickly—and make sure you’re not left behind. If you have the layers mentioned above in place, then explainability should be easy.

• People, process, and technology are your internal lines of defense when it comes to AI governance. 
• Be sure you understand who all of your stakeholders are, including the ones that might get overlooked. 
• Look for ways to have workflow approvals, version control, and significant monitoring. 
• Make sure you think about explainable AI and workflow standardization. 
• Look for ways to codify your processes. Create a process, document the process, and stick to the process.

In the recorded session Enterprise-Ready AI: Managing Governance and Risk, you can learn strategies for building good governance processes and tips for monitoring your AI system. Get started by creating a plan for governance and identifying your existing resources, as well as learning where to ask for help.

The post Taking a Multi-Tiered Approach to Model Risk Management and Risk appeared first on DataRobot AI Platform.

AI Innovators were selected for their exceptional pioneering efforts applying AI in the transformation of their business and industry. In a market as dynamic and rapidly evolving as AI, these organizations are inspiring examples of how AI can bring more intelligent decisions to vital business processes and business needs.

At DataRobot, we measure ourselves by the clear results our customers achieve. And these AI Innovators are great representatives of how companies of all sizes and across a range of industries are able to harness the power of human and machine intelligence for maximum business impact: 

• Decode Health
• Decathlon Technology
• DHL
• Freddie Mac
• Macquarie Group

Today, DataRobot is one of the most widely deployed, most trusted AI platforms in the world, supporting global organizations that include nearly 40% of the Fortune 50, spanning all major industries, including:

• 80% of top U.S. banks and 40% of the top global banks
• 50% of top global manufacturers
• 70% of the top pharmaceutical companies
• 70% of the top telecommunications companies
• 30% of top global retailers
• 30% of top global healthcare companies 

We’re incredibly proud of the trust we’ve earned from some of the most successful, most innovative organizations in the world—from the largest banks in the world protecting their customers from fraud to retailers more accurately predicting market demands to manufacturers accelerating innovation for their markets, while reaching new levels of sustainability. Together, we’re pushing the limits of both human and machine.

More Information

For more information about these AI Innovators and a complete view into the incredible organizations that trust DataRobot with the future of their businesses, visit datarobot.com/customers. I hope that these organizations inspire you to achieve even more ambitious goals together with DataRobot AI Cloud.  

The post Celebrating Our Customers appeared first on DataRobot AI Platform.

Showcasing the industry’s most innovative use of AI, this global event offers you the opportunity to learn from DataRobot data scientists—as well as AI pioneers from retailers like Shiseido Japan Co., Ltd., financial services and healthcare leaders, and the McLaren Formula 1 Team. Through a series of virtual keynotes, technical sessions, and educational resources, learn about innovations for the next decade of AI, helping you deliver projects that generate the most powerful business results while ensuring your AI solutions are enterprise ready—secure, governed, scalable, and trusted. 

Join this worldwide event in your time zone, delivered in focused, two-hour time segments over two days, as DataRobot customers, partners, and AI experts share best practices for success in AI today while setting the stage for the next decade of AI innovation.

Your access to DataRobot AIX is free with just a quick registration. Join the virtual event sessions in your local time across Asia-Pacific, EMEA, and the Americas. 

We know that delivering AI is a team effort, so we invite you to bring your whole team. DataRobot AIX has purpose-built content for business leads, data scientists, and IT leaders. Whether you’re just learning about the power of AI or already in production and planning your long-term AI strategy, DataRobot AIX has something for everyone. Learn how to empower every individual to ask better questions about data, combining human intelligence and artificial intelligence for transformative business outcomes. 

Explore nine great reasons to join DataRobot AIX 2022. 

1. AI Success Stories from Global Organizations

Across three exciting keynote sessions and 12 technical breakout sessions, discover how today’s innovative organizations are using DataRobot solutions. 

• Learn why global skincare, makeup, and fragrance leader Shiseido Japan Co., Ltd. views AI as a strategic business asset. 
• Be moved with the realization that trusted AI is improving children’s healthcare outcomes at Phoenix Children’s. 
• Understand how the State of West Virginia is saving taxpayers millions of dollars by leveraging human and machine intelligence for more responsible state government spending. 

Even more DataRobot customer stories will be unveiled at the virtual event. Join us to learn how AI is already redefining entire industries.  

2. Behind-the-Scenes Details from the McLaren Formula 1 Team

Formula 1 is the fastest growing sport in America and has been popular around the world for decades. As a partner of the McLaren Formula 1 Team, DataRobot is excited to share an exclusive view of how McLaren uses machine learning and AI. Learn how the McLaren Formula 1 Team is delivering AI-powered predictions and insights to maximize performance and optimize simulations. 

3. New DataRobot AI Platform Product Announcements

Continuously advancing the power of AI, new DataRobot product details will inspire both expert data scientists and forward-looking business leaders. Learn how new DataRobot capabilities and enhancements can help you integrate AI with your business critical services, apps, and workflows, bringing greater intelligence into all of your core business decisions. Join the DataRobot product leadership team for roadmap insights, new product demonstrations, and technology announcements. 

4. Ecosystem Partner News and Technical Sessions

Supporting the technology ecosystems that top organizations use today, DataRobot aligns with the platforms, applications, and strategic services most in demand. DataRobot technology, cloud, and services partners like Accenture, BCG, Hexaware, Ernst & Young, and more will share the latest updates to help advance your AI strategy. 

5. Data Scientist-Driven Breakout Sessions

With hundreds of data scientists supporting nearly one million projects across a diverse range of customers, DataRobot teams have created all new technical sessions presented by data scientists for data scientists. Dive into AI-powered forecasting, code first AI, aligning to a model risk management framework, and leveraging differentiated geospatial data for location AI. Join data science breakout session tracks to spark ideas for your next AI project. 

6. Breakouts for Business Leads

It’s more common than ever to see business leaders creating AI projects or working with data scientists for business outcomes. Each AI owner has common interests, like bias mitigation or creating business-ready apps to drive insights. Explore the business leaders breakout session track for insights that you can apply right away. 

7. Deep Dives into the DataRobot AI Platform 

See the DataRobot AI platform up close. Join the platform breakout session track to see an end-to-end product demo, dive deep into Continuous AI, learn how to create scalable AI projects, and understand how to manage governance and risk. 

8. Direct Access to AI Product Experts

Ask DataRobot product teams how to address your latest projects. In a robust virtual expo, visit with experts in data engineering, machine learning, ML Ops, and AI-powered apps. See the latest courses from DataRobot University and learn how to join the DataRobot Community. 

9. Innovation Inspiration for the Next Decade of AI

DataRobot is celebrating 10 years of AI advancements. Celebrate this milestone with a look into the future of AI innovation. An inspiring Day 2 keynote highlights what’s included in the next decade of AI solutions—along with a vision for the future of applied AI. 

The post 9 Great Reasons to Join the DataRobot AI Experience Virtual Event Jun 7-8 appeared first on DataRobot AI Platform.

Three sessions from the event sum it up best:

Julian Forero and Katy Haynie from Snowflake delivered a powerhouse case for the new opportunities that the DataRobot and Snowflake integration has unlocked. Their session, Expand Your Reach with Data Cloud, explains how this integration drives AI adoption. In short, Snowflake makes it much easier to access data for a faster time-to-production model. What’s more, Snowflake’s data governance allows DataRobot to build trusted models with robust data lineage that enables traceability for every data element back to its original source. This is crucial because as AI goes mainstream, trust in where data originates is paramount.

In another session, Ilan Gleiser, founder and CEO of Synarchy AI, explained the fundamentals of Delivering AI ROI at Scale. He makes the case that any business can find AI success and maximize ROI. His session was full of simple and clear advice for getting started and best practices creating value from an investment in AI.

Finally, the session Streamline Your Model Monitoring  demonstrates how DataRobot MLOps scales to all users—from data scientists to business analysts to engineers in DevOps and IT. This is so powerful because it means many more members of your organization can participate in post-production activities helping to manage and monitor your machine learning models. And that means you can better focus on the big problems that generate value for your organization.

These are just a few instances of the opportunity I see for AI. But there are so many more. I urge you to watch the entire event and to especially check out the DataRobot and Snowflake integration. We have a joint DEMO coming up that can’t be missed.

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I believe we’re at a crossroads, and as AI enters the mainstream, it is imperative that it upholds the public’s trust. We need to create responsible AI systems that are beneficial for everyone.

In my session, Bringing Trust into AI, we investigated the root cause of bias in machine learning and how we can make suggestions to address it. At every stage of development, built-in guardrails ensure your AI system is ethical and aligns with organizational values. We also look at how  tools such as the ones provided by DataRobot help users identify bias and take action. 

Many other sessions—especially those focused on core data science activities—touched on the need to trust your AI and gave suggestions for making your systems more reliable.

We also discussed how we are moving from AutoML to Composable ML, and my colleague Sylvain Ferrandiz examines how machine learning systems can fail and what you can do to prevent such breakdowns. In this session we focus on how to compose a system that’s fully under your control but also adaptable. 

Demand Forecasting with DataRobot demonstrates how extraordinary events (like a global pandemic) can skew data sets and cause traditional approaches to demand forecasting to falter. My colleague Jarred Bultema outlines ways DataRobot’s automated suite of tools can improve predictions and account for disruptive events that can impact performance and introduce unintended bias into your models.

Lastly, in the session Improving Your Model after Deployment with AutoML, my colleague Tristan Spaulding explains how unexpected conditions can degrade performance unless your  model is constantly refreshed with live data. He then teaches you how build and evaluate challenger models in DataRobot to improve model accuracy overall accuracy and keep ML at peak performance

For businesses to benefit from machine learning and see transformational growth, it is essential they ensure their AI systems reflect their principals and values. At DataRobot, trusted AI underlines everything we do. If you are curious, sign up for a personalized demo to learn more.

The post AI Experience Worldwide: Why Trust? Why Now? appeared first on DataRobot AI Platform.

One key to success is our vision for augmented intelligence, which builds and is dependent on humans creating structure and rules that assert their values, then puts machines to work to operate at a scale and timeframe that would not be possible otherwise. It combines the best that humans and machines offer. 

While there’s a lot to consider if you’re struggling to bring trustworthy AI to your organization, AIX included many sessions that illustrated how machines and humans can work together to chart a clear path forward.

We also announced a number of major product announcements. We are incredibly excited to bring Composable ML, Continuous AI, No-Code AI Apps, and many more major new capabilities to the platform this year. We are in no doubt that 2021 will be a game changing year for DataRobot.

If you missed it, another key session to watch is Model Monitoring at Scale with Seph Mard, director of technical product, MLOps, at DataRobot. Seph explains the many ways machine learning models can fail and then demonstrates how to use DataRobot to create an automated system for continuous model monitoring.

From there, there are another three sessions you should watch, especially if you’re more business-oriented:

1. Automation and Guardrails teaches the basics of automated model deployment, management, and monitoring. It also shows you how DataRobot’s built-in guardrails prevent many machine learning errors. 

2. Accelerating Value from AI with DataRobot’s DataRobot’s App Builder introduces our new platform that gives non-technical users the ability to create AI Apps. It walks you through the process and shows you how to build from scratch an app that uses AI to make predictions about late shipments in a supply chain.

3. Success with AI: A Chance Encounter at the Food Court is a great way to showcase how collaboration is key to solving big problems with AI. DataRobot’s Gonzo Gonzalez and Karin Jenson team up to explain why breaking down silos and partnering with colleagues from other teams is crucial to successful AI projects.

Finally, I recommend watching Under the Hood: Transforming Products with AI. We assembled an all-star panel of data scientists from Yelp, Pendo, and Overstock.com to share their advice on successfully using AI to enhance a product. It’s a must-see session for anyone looking to integrate AI into a product’s lifecycle.

DataRobot is democratizing AI. By empowering businesses large and small to use machine learning to build better products and improve customer relationships, our enterprise AI platform helps  generate incredible value.

There’s so much more to learn. See the full list of the event’s sessions and then tour the DataRobot AI Platform.

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There’s a lot to unpack, but first, I want to thank everyone—from our guest speakers to event planners to the technical team—who pulled together to make this event an incredible success. 

I’m also excited to welcome Zepl, who joined us via acquisition, to the DataRobot family. We’ll have a lot more to share on this in the weeks and months ahead. 

Now, on to a few of my takeaways from the event:

Introducing Augmented Intelligence

I was happy to share our vision of Augmented Intelligence. DataRobot’s core mission has always been to unleash the full potential of human and machine intelligence, and we strongly believe that our Augmented Intelligence platform converges the best that humans and machines have to offer. Humans uniquely and creatively use their intuition and experience with a high degree of context and domain expertise to build customized solutions. Machines bring unparalleled power, speed, and efficiency to processing large data sets and routine tasks based on a set of rules. DataRobot is now in a unique position to deliver solutions that harness the combined strength and intelligence of both humans and machines. 

Our customers will benefit from this regardless of their industry, size, or mission. It’s obvious that every organization will become AI-driven. It is our job to help make this future a reality as fast as possible.

Product Announcements

In addition to the launch of Augmented Intelligence, we were also thrilled to announce a number of groundbreaking new product enhancements that further strengthen our platform and enhance value for our customers.

In 2021, we are embracing the code-first data scientist. In addition to the tightly integrated and extremely powerful cloud-based notebook environment that the Zepl acquisition brings, we also announced Composable ML. This unlocks our world-class blueprints and allows advanced data scientists to customize and extend our automated capabilities with their own code to create new models that are explainable, trusted, and have a clear pathway to production through our MLOps product.

We also announced Continuous AI. This is another first for Data Science and Machine Learning technology. Continuous AI enables our MLOps customers to set up multiple retraining policies on their production models. You can set a model for automatic retraining on a scheduled basis or when an event like data drift occurs. Continuous AI also leverages our AutoML capabilities to automatically create new challenger models ensuring production models are constantly stress tested and kept at peak performance regardless of how crazy external conditions get.

Finally, we announced our new No-Code AI Apps that will enable customers to build beautiful AI applications and get the predictive power of their models in the hands of front line decision makers without any code. 

By the way, we haven’t even talked about day two yet!

AI and Trust

So let’s talk about the second day. First off you should definitely watch the opening keynote “Trust in AI is Not a Feature. It’s a Requirement.” Presented by our own Ted Kwartler, DataRobot’s Vice President of Trusted AI, this session helps us all understand what trust in AI really means and why it’s so important. Ted’s analysis is spot-on. As businesses increase their reliance on AI, trust will become the defining characteristic of successful AI-driven enterprises. It’s essential that people believe and trust the predictions their models generate, and we believe we can help foster a culture where AI has a positive and lasting impact on the world. Ted announced a couple of new product capabilities, including an early preview of a new trust app that we are calling the Model Grader. We’d love you to register if you’d like to get your AI models graded. We will use our best-in-class scoring criteria for data quality, robustness, model accuracy, and fairness.

Ted also announced an exciting new partnership with the World Economic Forum. We’ll work with the Forum on initiatives designed to build a more ethical, explainable, and equitable AI ecosystem. 

Becoming AI-Driven

I’d also recommend three sessions that are especially tailored for business leaders who want to hear insights on building an AI-driven enterprise. 

• The first is our CEO Panel on AI & Transformational Growth. What I liked most about this session was how candid the conversation was. It’s refreshing to hear people talk so honestly about their journey to bring AI to their business.

• The second is Rachik Laouar’s fascinating session on the ways Adecco UK use of AI is Turning an Industry Upside Down. Rachik is head of Data Science for the Adecco Group. He partnered with DataRobot just six months ago and is now disrupting the global recruitment market. After some experimenting, Adecco fully embraced Enterprise AI to help eliminate bias in the recruitment process and ensure equal opportunity to all qualified candidates.  

• Lastly, I suggest you watch Mark Coyne’s session on AI Operations and the Importance of Monitoring Models. As Vice President of Operational AI at Cotiviti, Mark has deep experience in building and maintaining complex machine learning systems. With DataRobot’s help, he’s simplified how models are monitored and managed. It’s a great case study on how DataRobot helps data science teams better collaborate with IT.

This is just the beginning. Whether you’re a data scientist, IT lead, business analyst, or CEO, there’s a session for you.

Take the Next Step

We’re here to help you on your journey to building an AI-driven enterprise and find success. 

Take a look at everything this year’s AI Experience Worldwide has to offer on-demand and consider signing up for your own personalized demo. I look forward to partnering with you to transform your business and lead your industry into a bright future with the power of Augmented Intelligence.

The post AI Experience Worldwide: Highlights, Reflections, and a Call to Action appeared first on DataRobot AI Platform.

During the conference, participants will have the opportunity to learn how the DataRobot AI Platform drives digital transformation, discover practical insights unearthed by industry pioneers, find solutions to roadblocks that prevent their company from realizing AI’s potential, and find ways to adopt AI strategies that lead to measurable business results.

We are especially looking forward to hearing from our roster of first-rate speaker talent. This list includes:

Rachik Laouar is Head of Data Science for the Adecco Group. Rachik is working to transform that company’s products through data analytics and AI and will be speaking on the topic, Executive Track: Turning an Industry Upside Down. 

Rob O’Neill is Head of Analytics for the University Hospitals of Morecambe Bay, NHS Foundation Trust, where he leads teams focused on business intelligence, data science, and information management. Rob will be speaking on the topic, Technology Track: Predictive Healthcare Analytics Democratization.

Eric Weber is Head of Experimentation And Metrics for Yelp. Weber holds a PhD in mathematics and also teaches at Product Faculty and Propulsion Academy. Eric will be speaking on the topic, Executive Track: Under the Hood – Transforming Products with AI.

Kyle Treece is Senior Director And Group Product Manager for Overstock. He is putting his expertise in machine learning and web analytics to use for the thriving online retailer. Kyle will be speaking on the topic, Executive Track: Under the Hood – Transforming Products with AI.

Austin Chou is a Postdoctoral Fellow at the University of California, San Francisco. Austin is exploring the ability of big data analytics to deal with neurological trauma cases and will be speaking on AI for Good with DataRobot. 

Brian Banks is an Analytics And Data Advisor for the United States Agency for International Development. Brian is a specialist in using data-driven insights to improve water stewardship and will be speaking on AI for Good with DataRobot. 

Rayid Ghani is a Professor at Carnegie Mellon University where he is focused on using machine learning to drive improved public policy decisions. Rayid will be speaking on AI for Good with DataRobot.

Mark Coyne is Vice President for Operational AI at Cotiviti, a healthcare analytics company, where he has built out a robust, scalable AI platform that supports a set of reusable AI capability solutions. Mark will be speaking on the topic, Executive Track: AI Operations and the Importance of Monitoring Models.

Julian Forero is Senior Product Marketing Manager for Snowflake, the data cloud company, where he works with data science teams to drive product growth through data-driven insights. Julian will be speaking on the topic, Technology Track: Expanding the reach of Data Science with the Data Cloud.

Linda Klug is Founder and CEO of Airin, an artificial intelligence deep technology company that clones the cognitive reasoning of experts. Linda will be speaking on the CEO Panel: AI’s Role in Transformational Growth.

Conference participants will also hear from a stellar group of DataRobot leaders:

• Nenshad Bardoliwalla, Senior Vice President for Products with DataRobot
• Ted Kwartler, DataRobot’s Vice President for AI Trust
• Haniyeh Mahmoudian, who was recently promoted to Global AI Ethicist at DataRobot
• Ari Kaplan, Director of AI evangelism and frequent host of our popular podcast

And of course, we’re looking forward to seeing our keynote speakers: Alexis Ohanian, founder of the venture-capital firm Seven Seven Six; and Bill Nye, popularly known as the Science Guy, Author and CEO – The Planetary Society.

These are exciting times for AI as we begin to see genuine, widespread impact from this powerful technology. Businesses around the world are deriving actionable insights from AI and machine learning as they capitalize on the power of data to deliver new products, connect with customers, and grow revenue.

Join us May 11th and 12th to learn more about how your business can experience transformational growth through AI.

The post Strong Speakers List Highlights DataRobot’s 2021 AI Experience Worldwide Conference appeared first on DataRobot AI Platform.