Enterprise AI: from pilot to production
Intel® Vision Event 2024: Bringing AI EVERYWHERE — A Brief Summary of My Panel Discussion on Enterprise AI, from Pilot to Production
This year’s Intel® Vision was themed “Bringing AI EVERYWHERE,” a fitting theme for our time. We are living in an era where artificial intelligence permeates every aspect of our lives, from industrial applications to consumer electronics. AI solutions are now integral in various industries, enhancing everything from healthcare diagnostics to autonomous driving, and making our gadgets smarter and more efficient.
Intel® Vision 2024 showcased an impressive array of AI solutions, demonstrating their capabilities across different environments — node, cluster, supercluster, and mega-cluster. The highlight of the event was undoubtedly Intel® Gaudi® 3 AI Accelerator (“Gaudi 3”), a powerful AI processor designed to supercharge AI training and inference workloads. Gaudi 3 captivated attendees with its promise to revolutionize AI training and inference, setting new benchmarks in performance and efficiency.
At byteLAKE, we have been closely collaborating with Intel® and Lenovo teams for many years, ensuring that our AI products deliver maximum performance, scalability, and reliability using their cutting-edge technologies. Therefore we have optimized our portfolio of AI products for a wide range of processors, accelerators, and computing solutions, from industrial PCs and edge servers to high-performance computing (HPC) systems. As we prepare to release a whitepaper summarizing the performance of our AI products on the latest tech from our partners, I am particularly excited about getting my hands on Gaudi 3. I am eager to see how it will enhance the AI training performance for our latest product, Data Insights, which is tailored for Smart Factory and Smart City applications.
I was honored to be invited to join a panel discussion at Intel® Vision, where we delved into the challenges industries face when advancing their AI solutions from the pilot phase to full-scale production deployment. Here are some of the key points we discussed:
Going from POC to Production: What Are the Challenges?
Transitioning from Proof of Concept (POC) to production deployment in AI projects involves several challenges. The primary goal of a POC is to demonstrate the feasibility and value of an AI solution within a specific use case. This phase allows us to test and refine our AI models, ensuring they meet the intended objectives. However, when moving to full-scale production, the focus shifts to scalability, reliability, and performance optimization.
Achieving these goals requires addressing several technical and organizational aspects:
- Data Integration and Infrastructure: Ensuring seamless data integration and having robust infrastructure in place are crucial. This involves preparing the data so the AI can correctly understand and capture all relevant nuances. For instance, in a Smart City application, combining IoT data with weather forecasts and historical data can help predict maintenance tasks or optimize city heating systems.
- Organizational Readiness: Moving from pilot to production also demands organizational readiness. Involving cross-functional teams, iterating on the solution based on feedback, and providing proper training for staff are essential steps. These practices help ensure the AI solution is effectively integrated into the operational workflow.
Where to Run AI Workloads: Cloud vs. Local Infrastructure
Deciding where to run AI workloads — whether in the cloud or on local infrastructure — depends on several factors:
- Cloud Solutions: Offer flexibility, scalability, and accessibility. They are ideal for applications requiring vast computational resources and where data privacy and security can be managed within the cloud infrastructure.
- Local Infrastructure: Provides greater control, customization, and performance, especially for applications with stringent data privacy, low latency, and specific regulatory requirements.
- Hybrid Approaches: Combining cloud and on-premises solutions can offer the best of both worlds, meeting specific business needs while balancing performance, cost, and security.
At byteLAKE, the majority of our AI deployments currently revolve around Edge AI, and for good reason. In manufacturing environments, where every second counts, Edge AI offers unparalleled advantages. Its real-time responsiveness and low latency ensure swift decision-making, critical for optimizing processes and enhancing productivity. Moreover, Edge AI prioritizes privacy, safeguarding sensitive data by processing it locally rather than transmitting it to external servers. This approach not only enhances security but also reduces the risk of data breaches. Additionally, Edge AI proves to be cost-effective, eliminating the need for constant internet connectivity and high bandwidth.
However, while Edge AI holds immense potential, we recognize the importance of a hybrid approach for certain industries. Take, for example, energy utility companies and factories managing diverse energy sources across widespread locations. In such scenarios, a hybrid solution that combines Edge AI with cloud or on-premises servers offers the best of both worlds. Critical tasks, such as optimizing supply temperatures or prioritizing energy sources based on weather forecasts and production plans, can leverage Edge AI for immediate decision-making. Meanwhile, cloud or on-premises servers provide supplementary backup capabilities, facilitate data transfers between locations, and ensure overall synchronization of operations. By embracing a hybrid model, organizations can achieve optimal efficiency, resilience, and scalability in their AI deployments, paving the way for transformative outcomes in the energy and manufacturing sectors.
The Future of AI: What’s on the Horizon?
As AI adoption continues to grow, organizations are increasingly interested in emerging trends and technologies. During the panel, we discussed the potential of AI to drive innovation, efficiency, and competitive advantage. We also addressed common concerns, such as ethical considerations, bias in algorithms, and the impact on jobs. My perspective is that the next wave of byteLAKE’s AI deployments will lean towards self-improving systems that can automatically retrain and adapt to changing environments.
At byteLAKE, we have successfully implemented such solutions in sound analytics, where our products retrain AI models on defined schedules to capture changes in environmental conditions and design nuances. This approach ensures continuous improvement and high-quality outcomes. Additionally, reducing the amount of initial data required for training will be a focus, leveraging generative AI and other advanced technologies.
In conclusion, AI is evolving in diverse and exciting directions, finding synergies between machines and humans to deliver exceptional results across industries. As we continue to innovate and deploy AI solutions, the future promises even greater advancements and opportunities for transformative impact.
Last but definitely not least, events like Intel® Vision are not just about technological advancements and future outlooks. They are also about networking, connecting with clients and partners, and catching up with friends from around the world! Huge thanks to the entire Intel® team for putting it together!
Here are a few more articles from my blog that you might enjoy exploring:
- Energy and Utility Companies are Ready for AI — Let’s Explore the Benefits. | by Marcin Rojek | May, 2024 | Becoming Human: Artificial Intelligence Magazine
- How AI Delivers Value for Energy Utilities Companies and Smart Factories | by Marcin Rojek | May, 2024 | Medium
- A Comprehensive Guide to Deploying AI in Industries: From Idea to Deployment | by Marcin Rojek | Apr, 2024 | Medium
