The game of baseball has no shortage of statistics — from batting average to exit velocity, strikeouts to wins above replacement. Among all sports, Major League Baseball (MLB) arguably contains the most analytical and data-driven participants and fan base. Subconsciously or viscerally, players and managers on the field and those following from anywhere are constantly assessing and making decisions based off of game play trends and expectations — whether a batter will come through with a hit in an important situation, when a pitcher should be pulled. Less analyzed, however, is what leads fans to become engaged with certain players or teams, and what factors drive their love of the game. This is the motivation behind the problem being posed by Major League Baseball in their Kaggle competition for Player Digital Engagement Forecasting. Can you use machine learning to deconstruct baseball fandom?

This competition asks you to predict measures of digital engagement for each active player on a daily basis during the MLB season. So, how large was the surge in fan interest after Joe Musgrove threw the first no-hitter in Padres history? Is Shohei Ohtani’s engagement higher when he pitches well, when he hits a monster home run…or when he does both? You’re provided a wealth of game, team and player information – detailed stats, awards, rosters, and transaction information – as well as social and digital engagement data as your inputs. Data scientists will recognize this as an exciting forecasting problem with both traditional regression and time series components, where having this input data just prior to the prediction date is critical to determining which players will receive the most engagement.

With so many variables in the game, there are an endless number of vectors which could possibly influence fan engagement. Eleven-time All-Star Miguel Cabrera delighted fans by hitting the first home run of the season – in the snow! Occasionally a lesser-known player like Musgrove or Carlos Rodón “wins the day” with an unlikely no-hitter. And sometimes just getting traded to an iconic franchise like the Yankees generates a ton of fan interest, like it did for Rougned Odor in early April.

As these examples show, a player’s digital engagement can be pretty dynamic during the season, with many different potential contributors to who is “trending” on a given day. How can you use data to uncover which factors are the most influential of engagement with each player’s digital content?

Ready to play ball? Check out the competition on Kaggle for all the details. $50,000 in prizes is up for grabs in two prize categories. The code competition puts your machine learning skills to the test, to see who can build the most accurate forecasting models to predict daily digital engagement for every active player. You’ll have until July 31st to build your models and then be evaluated on a future time frame, which will determine the winners. For data visualization and exploration experts out there, the explainability prizes give you an opportunity to analyze more broadly which factors, even those outside of what we’re providing directly, most influence digital engagement. You’ll be evaluated on how well you can use what the data is telling you to support your findings.

And if you’re looking to get started, we’ve provided an introductory video and some notebook tutorials, including a starting point for harnessing the power of Vertex AI through tools including Cloud Notebooks, Explainable AI, and Vizier.

With the second half of the season upon us, it’s an exciting time to be an MLB fan. With this Kaggle competition, it’s also a perfect opportunity to use data science to help understand baseball fandom and potentially earn some of your own accolades in the process. Step up to the plate!

Major League Baseball trademarks and copyrights are used with permission of Major League Baseball. Visit MLB.com.

Providing best-in-class customer service is crucial for the success of your business. Contact centers are a critical touch point, as they have to balance between representing your brand and prioritizing customer care. When your customers seek help and support, they expect efficient service that is accessible through modern voice and digital channels. In short, customer expectations are increasing—and that’s a problem if your contact center infrastructure and solutions are becoming outdated.

All of these factors are why today, we’re announcing Google Cloud Contact Center AI Platform, an expansion to Contact Center AI that offers an out-of-box, end-to-end solution for the contact center. It brings together the advantages of AI, cloud scalability, multi-experience capabilities, and tight integration with customer relationship management (CRM) platforms to unify sales, marketing, and support teams around data across the customer journey.

Improving customer experiences from all angles
Google Cloud’s Contact Center AI helps you leverage AI to scale your contact center interactions while maintaining a high level of customer satisfaction. Over the last two years, we have built a large group of partners, including the largest contact center and customer experience ISVs and our system integrator ecosystem, to bring Contact Center AI to customers. Today, we are helping enterprises across industries and geographies to cost-effectively reimagine contact center experiences. For example, Marks & Spencer reduced in-store call volume by 50%, and similarly, The Home Depot improved call containment by 185%, all while significantly increasing customer self-service engagement.

Adding to our Contact Center AI capabilities, Contact Center AI Platform is purpose-built for customer relationship management, extending your ability to offer personalized customer experiences that are consistent across your brand, whether delivered through a virtual agent, a human agent, or a combination of both. It eliminates many long-running pain points, from managing data fragmentation to replacing rigid customer experience flows with more engaging, personalized, and flexible support. With this addition, Contact Center AI now lets you:

Orchestrate the customer journey by creating modern experiences that can be embedded in their chosen channels with mobile/web software developer kits (SDKs), compatible with iOS and Android;

Leverage CRM as a single source of insight into the customer experience, to unify content, increase personalization, and automate processing with CRM data unification;

Manage multiple channels without pivoting across voice, SMS, and chat support;

Predict customer needs and route calls appropriately with AI-driven routing, based on both historical CRM data and real-time interactions;

Automate scheduling, schedule adherence monitoring, and manage employee scheduling preferences with Workforce Optimization (WFO) integration;

Provide customers with self-service via web or mobile interfaces using Visual Interactive Voice Response (IVR).

Helping you do more with contact centers
The addition of Contact Center AI Platform provides your partners the ability to integrate with Contact Center AI, so you can enjoy a more seamless experience operating your customer service center, with a complete view of the customer in a single workspace that includes real-time AI intelligence, native agent call controls, and real-time call transcription. For example, we are expanding our partnership with Salesforce to integrate Contact Center AI with Service Cloud Voice to deliver a unified Service Cloud agent console and Customer 360.

“Customers are continually raising their service expectations, and our research tells us 79% of consumers believe the experience a company provides is as important as its products and services,” said Ryan Nichols, SVP & GM, Contact Center, for Salesforce Service Cloud. “Through intelligence, workflows, and a deeper understanding of the customer, Salesforce’s Service Cloud Voice paired with Google’s Contact Center AI will empower agents with a seamless experience to help them wow customers.”

We are also excited to partner with UJET, an innovative and experienced Contact Center as a Service (CCaaS) provider. UJET offers secure user-centric design, scalability, and mobile-focused solution, with turnkey implementation, strong omnichannel capabilities, and best-in-class user experience, making their product a natural fit into Google’s contact center vision. To learn more about the partnership, see here.

Delivering impact for customers
Contact Center AI is already making a difference for our customers such as OneUnited Bank, the largest Black-owned bank in the U.S. “OneUnited Bank has been in partnership with Google Cloud and UJET, as well as a long-standing customer of Salesforce. The expansion and enhancements of Google Cloud’s Contact Center AI, along with its deeper integration with Salesforce, means better return on investment as we drive towards evolving our contact center to deliver exceptional client experiences,” said Teri Williams, President and Chief Operating Officer at OneUnited Bank.

Fitbit, which boasts more than 29 million active users, is also reaping the benefits. “Fitbit relies on Google Cloud and UJET to provide support to our customers with a mobile-first approach. This collaboration, in combination with a strong Salesforce integration, has helped us modernize our entire customer support experience,” stated Cassandra Johnson, VP, Devices & Services Customer Care & Vendor Management Office, at Google.

According to industry analyst Sheila McGee-Smith of McGee-Smith Analytics, “Google Cloud’s Contact Center AI is already a force in the contact center industry thanks to its early focus on AI for customer experience.” She continued, “Through their partnerships with UJET and Salesforce, as well as these expanded capabilities, Google Cloud’s Contact Center AI Platform will help define the future of customer service by powering more secure, engaging, and personalized customer experiences.”

Contact Center AI Platform is supported by a host of integration partners, including Accenture, CDW, Cognizant, Deloitte, HCL, IBM, Infosys, Quantiphi, Tata Consultancy Services, and Wipro. We will also continue to partner closely with the contact center and customer experience (CX) ISVs that our customers already rely on. If you already have a contact center solution provider, you can still integrate Google Cloud’s Contact Center AI into your existing environment.

To learn more about how you can leverage the power of AI to reimagine your contact center experience, visit our Contact Center AI page.

Establishing a mature MLOps practice to build and operationalize ML systems can take years to get right.  We recently published our MLOps framework to help organizations come up to speed faster in this important domain.  

As you start your MLOps journey, you might not need to implement all of these processes and capabilities. Some will have a higher priority than others, depending on the type of workload and business value that they create for you, balanced against the cost of building or buying processes or capabilities. 

To help ML practitioners translate the framework into actionable steps, this blog post highlights some of the factors that influence where to begin, based on our experience in working with customers. 

The following table shows the recommended capabilities (indicated by check marks) based on the characteristics of your use case, but remember that each use case is unique and might have exceptions. (For definitions of the capabilities, see the MLOps framework.)

Your use case might have multiple characteristics. For example, consider a recommender system that’s retrained frequently and that serves batch predictions. In that case, you need the data processing, model training, model evaluation, ML pipelines, model registry, and metadata and artifact tracking capabilities for frequent retraining. You also need a model serving capability for batch serving.

In the following sections, we provide details about each of the characteristics and the capabilities that we recommend for them.

Pilot

Example: A research project for experimenting with a new natural language model for sentiment analysis.

For testing a proof of concept, your focus is typically on data preparation, feature engineering, model prototyping, and validation. You perform these tasks using the experimentation and data processing capabilities. Data scientists want to set up experiments quickly and easily and track and compare them. Therefore, you need the ML metadata and artifact tracking capability in order to debug, to provide traceability and lineage, to share and track experimentation configurations, and to manage ML artifacts. For large-scale pilots, you might also require dedicated model training and evaluation capabilities.

Mission-critical

Example: An equities trading model where model performance degradation in production can put millions of dollars at stake.

In a mission-critical use case, failure with the training process or production model has a significant negative impact on the business (a legal, ethical, reputational, or financial risk). The model evaluation capability is important to identify bias and fairness, as well as to provide explainability of the model. Additionally, monitoring is essential to assess the quality of the model during training and to assess how it performs in production. Online experimentation lets you test newly trained models against the one in production using a controlled environment before you replace the deployed model. Such use cases also need a robust model governance process to store, evaluate, check, release, and report on models and to protect against risks. You can enable model governance by using the model registry and metadata and artifact tracking capabilities. Additionally, datasets and feature repositories provide you with high-quality data assets that are consistent and versioned.

Reusable and collaborative

Example: Customer Analytic Record (CAR) features that are used across various propensity modeling use cases.

Reusable and collaborative assets allow your organization to share, discover, and reuse AI data, source code, and artifacts. A feature store helps you standardize the processes of registering, storing, and accessing features for training and serving ML models. Once features are curated and stored, they can be discovered and reused by multiple data science teams. Having a feature store helps you avoid reengineering features that already exist, and saves time on experimentation. You can also use tools to unify data annotation and categorization. Finally, by using ML metadata and artifacts tracking, you help provide consistency, testability, security and repeatability of the ML workflows. 

Ad hoc retraining

Example: An object detection model to detect various car parts, which needs to be retrained only when new parts are introduced. 

In ad hoc retraining, models are fairly static and you do not retrain them except when the model performance degrades. In these cases, you need data processing, model training, and model evaluation capabilities to train the models. Additionally, because your models are not updated for long periods, you need model monitoring. Model monitoring detects data skews, including schema anomalies, as well as data and concept drifts and shifts. Monitoring also lets you continuously evaluate your model performance, and it alerts you when performance decreases or when data issues are detected. 

Frequent retraining

Example: A fraud detection model that’s trained daily in order to capture recent fraud patterns.

Use cases for frequent retraining are ones where model performance relies on changes in the training data. The retraining might be based on time intervals (for example, daily or weekly), or it could be triggered based on events like when new training data becomes available. For this scenario, you need ML pipelines to connect multiple steps like data extraction, preprocessing, and model training. You also need the model evaluation capability to ensure that the accuracy of the newly trained model meets your business requirements. As the number of models you train grows, both a model registry and metadata and artifact tracking help you keep track of the training jobs and model versions.

Frequent implementation updates

Example: A promotion model with frequent changes to the architecture to maximize conversion rate.

Frequent implementation updates involve changes to the training process itself. That might mean switching to a different ML framework, such as changing the model architecture (for example, LSTM to Attention) or adding a data transformation step in your training pipeline. Such changes in the foundation of your ML workflow require controls to ensure that the new code is functional and that the new model matches or outperforms the previous one. Additionally, the CI/CD process accelerates the time from ML experimentation to production, as well as reducing the possibility for human error. Because the changes are significant, online experimentation is necessary to ensure that the new release is performing as expected. You also need other capabilities such as experimentation, model evaluation, model registry, and metadata and artifact tracking to help you operationalize and track your implementation updates. 

Batch serving

Example: A model that serves weekly recommendations to a user who has just signed up for a video-streaming service.

For batch predictions, there is no need to score in real time. You precompute the scores and you store them for later consumption, so latency is less of a concern than in online serving. However, because you process a large amount of data at a time, throughput is important. Often batch serving is a step in a larger ETL workflow that extracts, pre-processes, scores, and stores data. Therefore, you need the data processing capability and ML pipelines for orchestration. In addition, a model registry can provide your batch serving process with the latest validated model to use for scoring.

Online serving

Example: A RESTful microservice that uses a model to translate text between multiple languages. 

Online inference requires tooling and systems in order to meet latency requirements. The system often needs to retrieve features, to perform inference, and then to return the results according to your serving configurations. A feature repository lets you retrieve features in near real time, and model serving allows you to easily deploy models as an endpoint. Additionally, online experiments help you test new models with a small sample of the serving traffic before you roll the model out to production (for example, by performing A/B testing).

Get started with MLOps using Vertex AI

We recently announced Vertex AI, our unified machine learning platform that helps you implement MLOps to efficiently build and manage ML projects throughout the development lifecycle. You can get started using the following resources: 

• MLOps: Continuous delivery and automation pipelines in machine learning
• Getting started with Vertex AI
• Best practices for implementing machine learning on Google Cloud

^{Acknowledgements: I’d like to thank all the subject matter experts who contributed,  including Alessio Bagnaresi, Alexander Del Toro, Alexander Shires, Erin Kiernan, Erwin Huizenga, Hamsa Buvaraghan, Jo Maitland, Ivan Nardini, Michael Menzel, Nate Keating, Nathan Faggian, Nitin Aggarwal, Olivia Burgess, Satish Iyer, Tuba Islam, and Turan Bulmus. A special thanks to the team that helped create this, Donna Schut, Khalid Salama, and Lara Suzuki, and Mike Pope for his ongoing support.}