Today: December 8, 2023

AI Lifecycle Synergy; Thriving In The Digital Era

4 mins read

Fatihah Ramzi, DigitalCFO Asia | 3 August 2022

An End-to-End Solution is paramount in ensuring your business’ success in the digital age.

Nearly every industry is being disrupted by digital technology, which is driven by artificial intelligence. By 2024, businesses using artificial intelligence (AI) will be able to react to customers, rivals, regulators, and partners 50% faster than their counterparts thanks to proactive, hyperspeed operational modifications and market reactions.

By 2023, IDC predicts that global AI spending would total $97.9 billion. IDC study indicates that although AI adoption is slow, it is about to change. The main barriers to AI initiatives include the expense of AI solutions, a shortage of data science skilled employees, data quality, quantity, and access, algorithm explainability, and algorithm selection. Only 10% of PoCs are deployed in production, and 50% of AI projects are unsuccessful. 

Businesses claim that rather than genuine data science work, more than 50% of the time being spent on an AI project is spent on data administration, integration, and solution deployment. To deploy AI and get better business outcomes more quickly, a company needs an end-to-end solution that addresses every stage of the AI lifecycle.

The Top 3 Key Performance Indicators for AI Initiatives

Customer Satisfaction, Faster Time to Market and Improvement in Productivity: The business goals that drive an organization’s investment in AI efforts strike a balance between tactical and strategic aspirations. Faster speed to market is a greater focus for smaller firms, whereas larger organizations tend to focus more on customer satisfaction.

Faster Access to Information, Revenue Generation and Personalization: The effectiveness of AI solutions depends on their key performance indicators (KPIs). Companies must monitor KPIs and seek out solutions that will move them closer to a certain value addition to their company. You will fail AI and AI will fail you if you want AI but don’t understand why. Faster information access promotes corporate flexibility and ongoing competitive advantage.

IT Automation Is by Far the Top Use Case for Build out of AI Applications: Every aspect of IT will be impacted by AI. It is utilized to provide self-configurable, self-healing, and self-optimizing infrastructure and data management that aids in proactive performance improvement, problem prevention, and resource optimization. Resolving staff tech support difficulties, automating the process of adopting new systems or apps to help increase company efficiency, minimizing hazards, and promoting overall cost reduction are further fascinating applications.

Top Drivers for Using Machine Learning as a Service Platform

Businesses use MLaaS to take use of the cloud-hosted machine learning processing capability, cutting out the extra time, expense, and risk involved in developing on-premise solutions. For any size project, MLaaS offers quick access to data and any amount compute. Instead of competing for resources with on-premise deployment, businesses may scale to gigantic size GPUs instantaneously.

Since data scientists can work from a centralized, controlled, project-based environment instead of a dispersion of open-source tools and having to support and oversee a range of environments, ML platforms help simplify these developments and result in lower operating expenses.

Unlike traditional development, which demands significant upfront investments that are largely ineffective until completely ramped-up, MLaaS just charges subscribers for time or space consumed, substantially reducing operational expenses. By offering pre-built algorithms, scalable data management, and validated data analytics, it offers a significant competitive edge. Because providers are required to follow all governances and rules, conduct regular vulnerability checks, and hire top security specialists, MLaaS is typically more secure than on-premise solutions.

End-to-End Lifecycle Management Challenges

The biggest obstacles include a lack of cross-persona synergy, infrastructure cost/performance, security, a lack of automation, and a lack of assistance for the development of polyglot microservices. Predictive monitoring is essential for larger firms, even if lack of automation and synergy across the model creation lifecycle is a bigger barrier for small and medium-sized businesses. Complete lifecycle management is a significant barrier for ModelOps and AI app developers, whilst data scientists find it difficult to rely on IT.

Top AI Model Development Challenges

The volume, velocity, variety, and validity of data drive the AI/ML lifecycle. Applications and AI models are not the same thing. Model effectiveness is impacted by data drift patterns, necessitating training, retraining, and redeploying. The tools and techniques used for data integration and DevOps today are restricted. Businesses must provide data scientists and engineers with self-service capabilities that facilitate safe data integration and use MLOps to hasten the deployment of AI applications.

Data Integration Challenges

To facilitate data discovery for data engineers and to enable IT to implement policies to maintain data security, organizations must make use of ML-powered data catalogs. Companies may use ML to profile, categorize, and cooperatively maintain data assets for training the models thanks to ML-powered data catalogs, which also provide appropriate governance and access control and do away with human metadata management.

Without one, the only method to demonstrate the organization’s data holdings in order to verify compliance with standards like the General Data Protection Regulation, HIPPA, and others is to manually categorize everything, which is unachievable for today’s petabyte-scale businesses.

Data Management Challenges

If new gear must be bought and set up in a customer’s datacenter, creating a new large-scale database could take weeks. As data sets expand rapidly and workload patterns alter over time, a database/analytics system requires ongoing tuning for optimum performance. A system’s uptime and the generation of business value are directly related. 

Automatic updates, security patches, and bug fixes must be implemented without any downtime. Businesses must implement autonomous data management systems to improve service levels through automation and fewer human errors, as well as to reduce operational expenses by eliminating expensive and time-consuming manual administration. After data is maintained, a data catalog is crucial so that analysts and data scientists may quickly find the data they require.

You can find the full report here.

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