Infusing AI in a Cloud Journey
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Infusing AI in your Cloud Journey
The AI “evolution” is moving us from the traditional, programmable IT, to an IT that begins to understand, and learn. Already many applications leverage natural human language inte
Why infuse AI on Journey to Cloud?
But why is this relevant to a Cloud Journey mission? In a MITSloan Global Executive Study, 85% of participants agree that they have an urgent need for an AI strategy, and most prioritize an “Enterprises Journey to AI” as a strategic priority. 72% say that AI will deliver mainly revenue increases. AI infusion can improve decision making, increase automation, enhance the end-user experience, prevent failures, prevent fraud, keep compliance to regulations, etc. Business drivers could be, for example, enriching application solutions by infusing AI to:
1. Improve product quality by integrating better defect detection mechanisms
2. Reduce operation costs by automating handling of simple customer complaints
3. Improve customer satisfaction by deploying chatbots to address user issues
4. Retain customers by integrating churn-prevention analytics.
To support the infusion of AI, as in the above examples, enterprises need to get the right data. We need an Information Architecture (IA) that supports Artificial Intelligence, as the mantra “There is no AI without IA”. Thus, we motivate the need to modernize solutions, as part of the Journey to Cloud, to obtain improved data access, which can then be ingested by AI models, to accelerate the ability of an enterprise to get business value. This approach is not just for infusing AI, but applies to other types of enrichment, for example, integrating Blockchain.
The Data Problems in AI
One of the common pain-points of organizations trying to extract AI insights is the effort involved in preparing and organizing their data so it can be consumed by the AI algorithms. Firms that have leveraged AI the most, particularly in the consumer space, have been very successful at properly organizing their data. But in most large enterprises, data is spread across a variety of environments, including public and private clouds, and traditional on-premises deployments, captive within traditional, siloed systems of record. Many organizations have moved data to a central accessible location, but often these efforts fail to deliver easy and controlled data access required to obtain AI insights. When that is the case, there are fractured views of the data, and it becomes difficult to obtain AI insights.
Data science is a team effort, so appropriate collaboration tools are required to coordinate access and actions. If an organization staff doesn’t know what data they have and how they are using it, the organization can be subject to regulatory non-compliance challenges. If the data used to train AI models has unfair biases, and the resulting recommendations aren’t transparent and trusted, then AI won’t be embraced and won’t be used at scale. Companies embracing AI can have hundreds of experiments built using different tools and running in different environments. They need to detect and proactively mitigate bias, to ensure that the performance of the models is fair given regulatory legal constraints. Data scientists should be able to explain how and why their models make recommendations, and they should be able to trace the lineage of the actual data that was used to build the models.
The AI Ladder
Enterprises need a data management strategy to provide flexible, organized access to all data, of every type, regardless of where it lives, and addresses the above concerns. A modernization effort would define and deploy an information architecture that provides an open, extensible foundation, with choice and flexibility, capable of communicating with other cloud platforms. IBM’s hybrid data management strategy to accelerate the Journey to AI is a prescriptive approach defined by a 4-step AI ladder: Collect, Organize, Analyze and Infuse.
Applying the IBM AI Portfolio
Lets now review the IBM AI Portfolio of tools that support the above ladder to AI. Together, this set of tools helps AI practitioners to Organize, Build, Deploy, Catalog and Manage their AI data and models. Notice that these tools can now be deployed on multiple cloud platforms, including competitors’ public clouds. This is a good example of the value of modernization: by containerizing multiple traditional core offerings we are now able to deploy them in multiple Clouds. The IBM’s AI Watson portfolio, as shown in the figure below, allows to:
Lets describe each:
Watson Knowledge Catalog is the tool that a “Data Engineer” will use to discover, cleanse and prepare the data. It helps understand data quality, data lineage and distribution through data-profile visualizations, built-in charts and statistics. It helps discover and uncover data across multiple on-premises and cloud sources to unlock silo knowledge and catalog new data sources. The tool helps to govern access to the underlying data assets by using an active policy manager.
Watson Studio provides tools for data scientists to collaborate in building and training AI models, and to deploy applications in a hybrid environment to operationalize them. It provides visual and open-source tools to explore data, prepare it and develop models.
Watson Machine Learning is the runtime environment for training and deployment of AI models into production, using Apache Spark. Once a model is built and trained, it can be deployed, auto-retrained, and managed. Users can run experiments, provide model tuning and comparison tools to evaluate models across 100-1000’s of hyperparameter configurations.
Watson OpenScale provides visibility on how AI models are built, run, and managed. It monitors the models to track and measure outcomes and adapts and governs AI to changing business situations - for models built and running anywhere. Track performance of production AI and its impact on business goals, with actionable metrics in a single console. Maintain regulatory compliance by tracing and explaining AI decisions across workflows, and intelligently detect and correct bias to improve outcomes.
In closing, AI is here, we must get ready for it!
Finally, AI infusion should be given due consideration as part of any modernization journey. It should be considered as part of the business case for such a journey and should be implemented in parallel with other modernization activities. Business value comes from enriching the solutions, as they are modernized, with new capabilities, and arguably most valuable will come from infusing AI as part of the journey…