No organization has been immune to the unprecedented challenges brought about by current global political unrest, the demand to optimize business costs constantly, and, of course, the devastating impact of the coronavirus pandemic.
Organizations can no longer rely on traditional, reactive, descriptive, and diagnostic analytics. To say ahead and future-proof business operations, organizations need to develop agile AI. Predictive capabilities that can run autonomously, leveraging real-time data feeds from IoT devices, alerting different users at different times with relevant information and recommendations for their specific roles.
While most organizations understand this imperative for those attempting to develop and operationalize AI, a recent International Data Corporation (IDC) survey highlighted. 75% of these AI projects either failed or have stalled. – So why?
Implementing AI, specifically Machine Learning (ML), is an eight (8) step process of:
- Gathering data from multiple internal and external sources to the business and in numerous formats structured, images or sound bytes.
- Preparing that data which in many cases involves transforming and consolidating the raw data.
- Accessing the data for its suitability for modeling and choosing the proper modeling process.
- Training the Model traditionally involving splitting the data into training and testing subsets and avoiding any data bias in the process.
- Evaluating the trained Model involves ensuring the model has run through the appropriate number of epochs to deliver the optimal results.
- Hyperparameter tuning or fine-tuning the model.
- Publish the final model.
- Operationalizing the model into existing business processes.
The traditional approach adopted by many organizations has been to use different tools and technical resources for each step, which has made the processing time-consuming, prone to error, and extremely costly. A further challenge faced by this traditional approach relates to AI governance; there has been no transparency into the process for key business stakeholders. However, the real challenge has been even if an organization was capable of developing a model and publishing it, the real issue for their eventual failure relates to step 8, the effective operationalizing of the model into existing business processes.
A Cloud-based, end-to-end machine learning development and lifecycle management platform; the Genetica.AI platform has been explicitly architected as a data-intensive real-time cloud computing engine with a tightly coupled big data repository framework and Google’s next-generation TensorFlow framework. It operates as a self-organizing collective of computing nodes node with a high degree of linear scalability; its middle-tier application logic layer can be configured to run on as many computing hosts as needed. Most importantly, the platform provides an environment for non-technical resources to rapidly create, deploy models and actionable AI services & mobile apps with zero programming efforts, reducing the cost and time to deploy AI by a factor of ten.
The Genetica.AI’s platform goes beyond current Enterprise AI capabilities with its embedded machine intelligence, which accurately perceives, utilizes, and manages its environment independently of any manual intervention. That is, once a model has been developed and published, it runs autonomously, capable of leveraging real-time data feeds from IoT devices and alerting different users at different times with relevant information and recommendations for their specific roles.
In short, Cortex not only understands “what” needs to be done but also handles the “how” and “by whom” all within the platform!
Like to know more? –
Email me @ joseph.pizzolato@genetica.ai
