Paving the way to AI and machine learning success

Simith Nambiar, Practice Leader, Emerging Tech, APJ, Rackspace Technology, tells us how companies can overcome the challenges they face with their AI/machine learning efforts.

As enterprises continue to take advantage of cloud-based computing technologies, attention is shifting to the explosion of new data, AI and machine learning (AI/ML). With the powerful combination of new data and AI/ML technologies, organizations can deliver superior customer-centric experiences, allowing them to understand their business environment like never before, driving them to new levels. of efficiency.

In Singapore, the government continued to invest in ambitious projects in key sectors to accelerate the adoption of AI/ML. For example, through the National AI in Finance Program, financial institutions will soon leverage an AI platform to assess environmental impact, identify emerging risks, and enable financial institutions to make green investments. In the public sector, frontline government agencies will leverage AI capabilities such as natural language processing (NLP) to understand and process feedback and better serve citizens.

However, despite growing spending on artificial technology and machine learning initiatives, achieving AI/ML-based successes is challenging. Information from a recent study sponsored by Rackspace Technology reveals that only 18% of respondents report mature AI/ML capabilities. Additionally, a majority of local respondents (75%) are still exploring or struggling to operationalize AI/ML models.

More than a third (32%) of respondents report AI R&D initiatives that have been tested and abandoned or failed. The main causes of these failures were a poorly designed strategy (43%), lack of data quality (36%), lack of production-ready data (36%), and lack of expertise within the organization (34%).

The failures underscore the complexity of building and managing a productive AI and machine learning program. Upon closer examination, companies are struggling with their AI/ML efforts for several reasons, including:

● Failure to get the right data to the right application or real-time analysis point – The quality of a company’s machine learning training depends on the data that is fed into AI/ML frameworks and applications smart. If the data is bad, old, or incomplete, the training will be poor and the answers and results generated will match the quality of the data.

● Lack of organizational collaboration – Designing the right machine learning training and AI algorithms requires a holistic understanding of data and automated processes across organizational boundaries. Lack of collaboration often results in poor implementation, lower quality data, and rejection of automation applications/projects by key parts of the organization.

● IT and business process immaturity – IT and business processes must be well trained to ensure data quality and seamless AI/ML execution. Additionally, AI/ML is best served with a

data and algorithm iterations and refinements – something that happens most effectively in a DevOps culture.

● Lack of expertise in math, algorithm design, or data science and engineering – Since AI and machine learning relies on timely, high-quality data and well-trained algorithms – representing the best real-world processes and models – skills are critical. Finding talent is difficult in today’s market.

To overcome these challenges, organizations can take the following steps:

Step 1: Build the Foundation

Start by preparing data and applications to migrate to the appropriate multicloud and data architecture environments. This includes knowing and understanding the current business environment and requirements and defining a roadmap.

Companies also need to ensure that the data architecture supports new application deployments appropriately and that IO costs can be minimized while maximizing performance and availability. This is also the stage where database transformations and data warehouse migrations are implemented.

Step 2: Modernize the data architecture

Defining the modern data architecture, strategy, and roadmap guides the transition to this phase. Focusing on data architecture modernization will help define, design and build the data structure. This phase includes pipelines and integration, data lakes and warehouses, and the analytics platform.

Step 3: Set the Stage for More Innovation

AI/ML prepares the organization for high-quality automation and predictive intelligence, taking innovation to the next level. At this stage, designing, training, and deploying the models, as well as operationalizing machine learning (MLOps) will enable the business to deliver greater value to modern cloud and data architecture. constructed in steps 1 and 2.

Step 4: Create smart apps

Finally, to start delivering strategic value and capabilities and take full advantage of this new cloud-based data structure, intelligent applications that integrate chatbot services, natural language processing, machine vision, recommendation, predictive maintenance and even actions can be deployed. With insights from Internet of Things (IoT) data, anything is now possible and provides a new foundation for the business.

Many organizations are still determining whether they will build AI/ML support in-house or outsource it to a trusted partner. Given the high risk of implementation failure, the study also indicated that organizations (66%) prefer to work with an experienced vendor to navigate the complexities of AI and machine learning development .

Additionally, according to Gartner, a shortage of IT skills among staff, salary inflation and a war for talent will likely drive chief information officers (CIOs) to rely more on consultants and managed service providers (MSPs). to pursue digital strategies.

As companies attempt to bridge the gap between their digital ambitions and their internal resources and capabilities, many companies will increasingly rely on external consultants to achieve their business goals.

With the help of data, organizations can get more out of their resources, delivering intelligent applications, services and outcomes, enabling the business to make smarter decisions, improve collaboration, deliver new sources revenue and new business models and transforming customer experiences.

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Sherry J. Basler