Google Cloud Next OnAir Week 8: Cloud AI

Week 8 of Next OnAir ‘20 focused on how to generate value with AI on Google Cloud’s AI Platform. SADA’s cloud experts found the following sessions to be particularly compelling and provided some informative post-session insights and commentary that you won’t want to miss:

Week 8 #SADASays Post-Session Recaps

Opening Keynote

Rajen Sheth, VP, Product Management, Google Cloud, kicked off the week with a keynote on how industries are using AI to navigate uncertain times, and how to start using innovations from Google Cloud AI in your business today.

“The organizations that embrace AI will have a competitive advantage, and the window for being ahead of the pack, rather than getting left behind, is closing,” said Sheth. In support of this, Sheth shared a recent McKinsey study that revealed that companies that fully absorb AI in their value-producing workflows by 2025 will dominate the 2030 world economy with +120% cash flow growth.

“To truly unlock the value of AI, you first need to really focus on the business problem and not the technology. And it really helps to work with companies that have already engaged with customers to deploy AI at scale so they can help you overcome the roadblocks you’re going to face in the process,” said Sheth. Google Cloud AI helps businesses with these roadblocks and stands out in the industry for three reasons:

1. Leadership in AI research through Google Research and in practical applications of AI through Google products. For example, Google just delivered record-setting performance in six out of eight benchmarks in the latest round of the MLPerf benchmark released in July.
2. Google Cloud brings its heritage and experience of deploying AI in production across Google in products like Google photos, Gmail, and many more.
3. Trust is a top priority for Google Cloud. The company is driving leadership in responsible AI through AI principles and recommended governance practices.

AI for Common Business Challenges

Sheth delved into how AI is being used to solve common business problems. He covered the following solutions:

Horizontal Solutions

• Contact Center AI: Enables businesses to deliver exceptional customer service and increase operational efficiency using AI. Virtual agents converse naturally with customers and expertly assist human agents on complex cases. Newly announced features include:
  • Agent Assist for Chat (beta): Provides agents with continuous support over “chat” in addition to voice calls, by identifying intent and providing real-time, step-by-step assistance.
  • Custom Voice (private beta): Allows you to create a unique voice to represent your brand across all your customer touchpoints, instead of using a common voice shared with other organizations.
  • Dialogflow CX: Provides a new way of designing agents, taking a state machine approach to agent design.
• Document AI: Enables businesses to unlock insights from documents with machine learning. It gives you the ability to tap into the opportunity offered by unstructured data to increase operational efficiency, improve customer experience, and inform decision-making.

Industry-Specific Solutions

Retail

• Recommendations AI (beta): Enables retailers to deliver highly personalized product recommendations at scale.

Healthcare

• Google Cloud formed a strategic partnership with Mayo Clinic combining its cloud and AI capabilities with Mayo’s clinical expertise to jointly develop solutions to transform healthcare.

Financial Services

• Lending DocAI (alpha): A new, specialized solution powered by Document AI for the mortgage industry. It processes borrowers’ income and asset documents to speed-up loan applications—a notoriously slow and complex process.
• Procure2Pay DocAI (beta): Helps companies automate one of their highest volume, and highest value business processes -- the procurement cycle.

AI for Unique Business Challenges

Ting Liu, Principal Software Engineer, Google Cloud, went on to discuss how Cloud AI is solving unique business challenges. “Customers tell us that their teams working on AI are composed of various skill sets, from ML engineers to data scientists to developers. So we have new tools for everyone on your team,” said Liu. Liu announced a set of services that will simplify Machine Learning Operations (MLOps) for data scientists and ML engineers, so that businesses can realize the value of AI. Here are some of these solutions and services:

• Fully Managed Service for ML Platform Pipelines (coming soon): Google Cloud announced a hosted offering for building and managing ML pipelines earlier this year. Now, a fully managed service for ML pipelines will be available by October this year. With the new managed service, users can build ML pipelines using TensorFlow Extended (TFX’s) pre-built components and templates that significantly reduce the effort required to deploy models.
• Continuous Evaluation Service: Regularly samples prediction input and output from trained machine learning models that you have deployed to AI Platform Prediction. AI Platform Data Labeling Service then assigns human reviewers to provide ground truth labels for your prediction input; alternatively, you can provide your own ground truth labels.
• Continuous Monitoring Service (coming soon): A service that will monitor model performance in production to let you know if it is going stale, or if there are any outliers, skews, or concept drifts, so teams can quickly intervene, debug, or retrain a new model.
• ML Metadata Management Service (coming soon): Enables AI teams to track all the important artifacts and experiments they run, providing a curated ledger of actions and detailed model lineage. This will allow businesses to determine model provenance for any model trained on AI Platform for debugging, audit, or collaboration.
• AI Platform Will Include AutoML (coming soon): This combines the best of non-code and code-based options to build custom ML models faster with high quality.
• AI Platform Notebooks (generally available): A managed service that offers an integrated and secure JupyterLab environment for data scientists and machine learning developers to experiment, develop, and deploy models into production.

On Tuesday, September 8th, Next OnAir will be focused on Google Cloud’s Business Application Platform. Tune in to learn more about designing, securing, analyzing, and scaling APIs anywhere with visibility and control. Also, be sure to check out SADA’s Next OnAir post-session recaps on Thursdays. Here’s what’s coming up on September 10th:

Upcoming #SADASays Post-Session Recaps: Week 9