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Empowering Future Innovators in AI & Python

A hands-on workshop on “Create Smart AI Systems with Python” was conducted for 1st-year B.Tech CSE (AI/ML) students at the School of Engineering & Technology, SDGI Global University on 11 April 2026.

The session commenced with the felicitation of the resource person, Mr. Ravikant Tyagi (General Manager, Xebia), by Prof. (Dr.) Jaideep Kumar (Director, SET) and Dr. Kamlesh Kumar Gautam (HoD/Professor, CSE) as a token of appreciation.

This was followed by an inspiring address by Director, SET, who encouraged students to explore emerging technologies and emphasized the importance of skill-based learning in the field of Artificial Intelligence.

The workshop was led by the Resource Person, who provided practical insights into AI concepts and Python-based implementations. Students gained hands-on experience with AI tools, learned the fundamentals of building smart systems, and enhanced their programming, analytical, and problem-solving skills through interactive sessions.

Hon’ble Chancellor Mr. Mahendra Agrawal appreciated such initiatives, highlighting the university’s commitment to industry-oriented learning. Pro-Chancellor Mr. Piyush Srivastava termed it a significant step towards skill development and industry readiness. Vice Chancellor Prof. (Dr.) Parsanjeet Kumar emphasized that such workshops play a crucial role in enhancing students’ technical competencies.

The event was successfully coordinated by Mr. Suvayan Chatterjee and Mr. Keshav Kumar, Assistant Professor, SET, ensuring an engaging learning experience. The session was hosted by Ms. Reshu Bansal, with support from Ms. Savita.

Artificial intelligence has progressed rapidly in the last few years. Businesses are no longer only experimenting with AI tools that answer questions or automate small tasks. In 2026, companies are beginning to deploy systems that can perform complex workflows, make context-based decisions, and adapt to dynamic environments. Read More:

Artificial Intelligence has come a long way in understanding and generating human language, thanks largely to advancements in large language models. Among the leading techniques that elevate these models’ capabilities are Retrieval-Augmented Generation and Fine-Tuning. Although both aim to improve AI responses, they do so through very different approaches, each with its own strengths, challenges, and ideal use cases.

The Basics: Tailoring Intelligence vs. Fetching Fresh Knowledge

At its core, Fine-Tuning is about customization. Starting with a broadly trained LLM, fine-tuning adjusts the model’s internal parameters using a specialized dataset. This helps the AI learn domain-specific terminology, nuances, and context, enabling it to understand and respond accurately within a particular field. For example, a fine-tuned model in healthcare would grasp medical abbreviations, treatment protocols, and patient communication subtleties far better than a general-purpose model.

In contrast, Retrieval-Augmented Generation enhances an AI’s answers by combining a pre-trained language model with a dynamic retrieval system. Instead of relying solely on what the model “knows” from training, RAG actively searches external knowledge bases or documents in real-time, pulling in up-to-date or proprietary information. This enables the AI to generate answers grounded in the latest data- even if that data wasn’t part of the original training corpus.

How Fine-Tuning Shapes AI Understanding

Fine-tuning involves carefully retraining the model on a curated dataset, often domain-specific. The process tweaks the model’s neural network weights to improve accuracy and reduce errors like hallucinations or irrelevant responses. Importantly, it uses a lower learning rate than the initial training to preserve the model’s general language capabilities while specializing it.

This method excels when the task demands deep familiarity with specialized language. For instance, healthcare fine-tuning enables the model to correctly interpret abbreviations like “MI” as “Myocardial Infarction” and provide contextually precise answers about diagnosis or treatment. However, fine-tuning can be resource-intensive and might not adapt quickly to new information after training.

Why RAG Brings Real-Time Intelligence

RAG models address a key limitation of static LLMs: outdated or missing knowledge. Since it retrieves relevant documents on demand, RAG allows AI to incorporate fresh, specific data into its responses. This is invaluable in fast-evolving domains or cases requiring access to confidential enterprise data not included during model training.

Imagine querying about the interactions of a novel drug in a healthcare assistant. A fine-tuned model may understand the medical terms well, but might lack details on the latest drug interactions. RAG can fetch current research, patient records, or updated guidelines instantly, enriching the answer with real-world, dynamic information.

The Power of Combining Both Approaches

The real magic happens when fine-tuning and RAG are combined. Fine-tuning equips the model with a strong grasp of domain language and concepts, while RAG supplements it with the freshest and most relevant data.

Returning to the healthcare example, the fine-tuned model decodes complex medical terminology and context, while the RAG system retrieves up-to-date clinical studies or patient data about the drug’s effects. Together, they produce responses that are both accurate in language and comprehensive in knowledge.

This hybrid strategy balances the strengths and weaknesses of each technique, offering an AI assistant capable of nuanced understanding and adaptive learning—perfect for industries with complex, evolving needs.

Practical Takeaways

Fine-Tuning is best when deep domain expertise and language understanding are critical, and training data is available.

RAG shines in scenarios needing up-to-the-minute information or when dealing with proprietary, external knowledge.

Combining them provides a robust solution that ensures both contextual precision and knowledge freshness.

Final Thoughts

Intel Aurora Supercomputer

Together with Argonne National Laboratory and Hewlett Packard Enterprise (HPE), Intel announced at ISC High Performance 2024 that the Aurora supercomputer has broken the exascale barrier at 1.012 exaflops and is now the fastest AI system in the world for AI for open science, achieving 10.6 AI exaflops. Additionally, Intel will discuss how open ecosystems are essential to the advancement of AI-accelerated high performance computing (HPC).

Why This Is Important:

From the beginning, Aurora was intended to be an AI-centric system that would enable scientists to use generative AI models to hasten scientific discoveries. Early AI-driven research at Argonne has advanced significantly. Among the many achievements are the mapping of the 80 billion neurons in the human brain, the improvement of high-energy particle physics by deep learning, and the acceleration of drug discovery and design using machine learning.

Analysis

The Aurora supercomputer has 166 racks, 10,624 compute blades, 21,248 Intel Xeon CPU Max Series processors, and 63,744 Intel Data Centre GPU Max Series units, making it one of the world’s largest GPU clusters. 84,992 HPE slingshot fabric endpoints make up Aurora’s largest open, Ethernet-based supercomputing connection on a single system.

The Aurora supercomputer crossed the exascale barrier at 1.012 exaflops using 9,234 nodes, or just 87% of the system, yet it came in second on the high-performance LINPACK (HPL) benchmark. Aurora supercomputer placed third on the HPCG benchmark at 5,612 TF/s with 39% of the machine. The goal of this benchmark is to evaluate more realistic situations that offer insights into memory access and communication patterns two crucial components of real-world HPC systems. It provides a full perspective of a system’s capabilities, complementing benchmarks such as LINPACK.

How AI is Optimized

The Intel Data Centre GPU Max Series is the brains behind the Aurora supercomputer. The core of the Max Series is the Intel X GPU architecture, which includes specialised hardware including matrix and vector computing blocks that are ideal for AI and HPC applications. Because of the unmatched computational performance provided by the Intel X architecture, the Aurora supercomputer won the high-performance LINPACK-mixed precision (HPL-MxP) benchmark, which best illustrates the significance of AI workloads in HPC.

The parallel processing power of the X architecture excels at handling the complex matrix-vector operations that are a necessary part of neural network AI computing. Deep learning models rely heavily on matrix operations, which these compute cores are essential for speeding up. In addition to the rich collection of performance libraries, optimised AI frameworks, and Intel’s suite of software tools, which includes the Intel oneAPI DPC++/C++ Compiler, the X architecture supports an open ecosystem for developers that is distinguished by adaptability and scalability across a range of devices and form factors.

Enhancing Accelerated Computing with Open Software and Capacity

He will stress the value of oneAPI, which provides a consistent programming model for a variety of architectures. OneAPI, which is based on open standards, gives developers the freedom to write code that works flawlessly across a variety of hardware platforms without requiring significant changes or vendor lock-in. In order to overcome proprietary lock-in, Arm, Google, Intel, Qualcomm, and others are working towards this objective through the Linux Foundation’s Unified Acceleration Foundation (UXL), which is creating an open environment for all accelerators and unified heterogeneous compute on open standards. The UXL Foundation is expanding its coalition by adding new members.

As this is going on, Intel Tiber Developer Cloud is growing its compute capacity by adding new, cutting-edge hardware platforms and new service features that enable developers and businesses to assess the newest Intel architecture, innovate and optimise workloads and models of artificial intelligence rapidly, and then implement AI models at scale. Large-scale Intel Gaudi 2-based and Intel Data Centre GPU Max Series-based clusters, as well as previews of Intel Xeon 6 E-core and P-core systems for certain customers, are among the new hardware offerings. Intel Kubernetes Service for multiuser accounts and cloud-native AI training and inference workloads is one of the new features.

Next Up

Intel’s objective to enhance HPC and AI is demonstrated by the new supercomputers that are being implemented with Intel Xeon CPU Max Series and Intel Data Centre GPU Max Series technologies. The Italian National Agency for New Technologies, Energy and Sustainable Economic Development (ENEA) CRESCO 8 system will help advance fusion energy; the Texas Advanced Computing Centre (TACC) is fully operational and will enable data analysis in biology to supersonic turbulence flows and atomistic simulations on a wide range of materials; and the United Kingdom Atomic Energy Authority (UKAEA) will solve memory-bound problems that underpin the design of future fusion powerplants. These systems include the Euro-Mediterranean Centre on Climate Change (CMCC) Cassandra climate change modelling system.

The outcome of the mixed-precision AI benchmark will serve as the basis for Intel’s Falcon Shores next-generation GPU for AI and HPC. Falcon Shores will make use of Intel Gaudi’s greatest features along with the next-generation Intel X architecture. A single programming interface is made possible by this integration.

In comparison to the previous generation, early performance results on the Intel Xeon 6 with P-cores and Multiplexer Combined Ranks (MCR) memory at 8800 megatransfers per second (MT/s) deliver up to 2.3x performance improvement for real-world HPC applications, such as Nucleus for European Modelling of the Ocean (NEMO). This solidifies the chip’s position as the host CPU of choice for HPC solutions.

The Future of 🧑‍💼 Work: How AI is Shaping the Job Landscape

𝐀𝐫𝐭𝐢𝐟𝐢𝐜𝐢𝐚𝐥 𝐈𝐧𝐞𝐥𝐥𝐢𝐠𝐞𝐧𝐜𝐞 is significantly transforming the Job Market. 👉While some Jobs may become obsolete, new roles will emerge that require skills in working with AI Systems, Data Analysis and Decision-Making.

AI Technologies are automating repetitive and mundane tasks, allowing employees to focus on more complex and creative work, which can lead to increased efficiency and productivity in many industries. AI is also changing how businesses operate and organizations function, impacting various industries, including coding, marketing, legal, healthcare administration and more.

Preparing for an AI-Integrated future is about a combination of upskilling, reskilling, staying informed and maintaining an adaptable mindset. While challenges are undeniable, those who are proactive in their approach to personal and professional development will be in the best position to capitalize on new opportunities.

Ecommerce has developed dramatically in the previous decade. New business structures and ecommerce trends have complicated the environment. During the COVID-19 epidemic, digital usage increased worldwide. Some estimates put internet shopping at 50% of retail sales by next year.

However, many organizations realized that increasing their internet presence or focus didn’t ensure success. McKinsey calls this “the e-commerce catch-22,” since many merchants with strong ecommerce sales growth in 2020 and 2021 saw margins fall.

Due to market saturation, electronics and home goods retailers increasingly compete internationally with tiny online businesses like Amazon. This may drive companies to lower prices to compete.

New markets may complicate shipping and logistics and disrupt global supply chains. Since they’re used to same-day delivery and tailored items, consumers anticipate a unified digital shopping experience. They intend to utilize their preferred payment method. These clients increasingly want free delivery and reward schemes. Businesses must also focus on sustainability to reach digital natives like Gen Z.

Businesses must be innovative about their digital strategy and how to build dynamic, interactive shopping experiences that increase consumer interactions in the ever-changing, fast developing ecommerce environment. Here are some ecommerce trends that will effect the global USD 3.3 trillion industry and improve customers’ digital experiences as consumers change how they buy products and services:

Artificial intelligence

Augmented reality

Live commerce

Online-to-offline ecommerce

Collective commerce

The voice helpers

Artificial intelligence trend

AI has greatly impacted digital commerce and is regarded as the fourth industrial revolution. Customers now demand personalized purchasing experiences. Digital optimization and automation solutions make using consumer or third-party data to create intelligent ecommerce sites cheaper and simpler. If used properly, AI-enabled marketing and product discovery technologies improve consumer engagement and retention.

Personalization

To provide individualized product suggestions and shopping experiences, AI systems can evaluate massive quantities of user data, including browsing history, purchase behavior, and demographics. AI-based customization recommends products, reminds consumers to reorder, and customizes shopping experiences. These tailored shopping experiences may be used on product sites, email marketing, and checkout.

Dynamic pricing

Dynamic pricing, popularized by ride-share firms but increasingly utilized elsewhere, lets shops modify rates in real time depending on demand, inventory, and rival pricing. Dynamic pricing may help certain ecommerce firms optimize income while staying competitive, but the AI’s settings must be carefully selected to prevent ridiculous price structures that may repel new consumers.

Virtual assistants, chatbots

Gartner predicts that by 2027, 25% of firms would use chatbots to provide customized customer assistance in natural language, answer inquiries, and resolve consumer problems in real time. To create a positive online purchasing experience, conversational AI-assisted customer service and its managers must work together, according to some academics.

Find and discover

Machine learning algorithms let AI-powered search and recommendation engines understand user intent, increase search relevancy, and identify products. To help buyers locate the right product by pattern or style, many retailers are using third-party AI to simplify natural language product searches.

Technology-based ecommerce firms have also emerged from the AI revolution. Many subscription-based ecommerce firms like Blue Apron and BarkBox have grown over 1,000%. AI and advanced analytics help direct-to-consumer subscription companies choose products.

Augmented reality trend

AR and VR technology allow marketers to improve digital experiences and integrate digital touchpoints into conventional purchase channels. Consumers may minimize uncertainty and return rates by “trying it out” in their living room for purchases that are significantly reliant on environmental context, such as a new sofa. AR and VR may improve customer experiences in travel, hospitality, and consumer retail by providing the finest product previews before purchase.

Product visualizations improved

A vivid and immersive product preview is one of the main advantages of integrating AR and VR into ecommerce. Industries where customers make significant, contextually sensitive purchases might benefit from this feature. Several real estate businesses have tried remote property viewing technology. Recently, IKEA launched a successful AR smartphone app that lets shoppers explore its products in their homes.

Virtual try-ons

Properly designed virtual try-on experiences may alter fashion and beauty. This technology lets consumers remotely try on clothes, accessories, and cosmetics via cellphones or webcams. Some optical stores let customers “try on” glasses online. Sephora’s real-time cosmetics app is very successful.

Live-commerce trend

Live commerce started in China. Within six years after its 2016 launch, the sector was expected to generate USD 647 billion. The US market is still growing, expected to generate USD 32 billion by 2023.

Live commerce events allow consumers to connect with a host via livestream while buying things with digital wallets. Alibaba pioneered the sales strategy, which TikTok, Amazon, and Poshmark have emulated. Influencers or celebrities may promote items during shopping events, encouraging customers to speak about and buy them. Live commerce lets firms promote discounts and builds community around an event.

Online-to-offline ecommerce trend

Many online-to-offline (O2O) ecommerce solutions seamlessly combine digital channels with physical retail experiences, offering a comprehensive consumer journey across both channels. Though contradictory, several internet merchants prioritize luring consumers back to purchasing in-store. Offer chosen experiences in physical places to build client loyalty and find new markets.

Amazon bought Whole Foods and combined in-store food purchasing with its digital environment. From China’s Alibaba to Magnolia Market, a direct-to-consumer furniture brand, several other companies have launched physical storefronts based on their internet sites.

Online-to-offline ecommerce projects include:

Omnichannel purchasing involves ordering online and picking up in-store.

Furniture showrooms for online orders and home delivery

QR codes or bar codes for in-store product information

Social-commerce trend

Between 2021 and 2025, social commerce ecommerce that uses social media as a marketing tool and shopping destination is anticipated to rise by over 50%. It’s a prominent ecommerce trend that’s predicted to earn USD 1.2 trillion by 2025 and features mobile shopping like live commerce.

Organizations may utilize social media to promote product discovery, social buying, and consumer involvement. Facebook, Instagram, and TikTok have integrated social commerce to make it easier.

Seamless social media buying experiences use social behaviors to shorten the purchasing process and boost conversion rates. Social recommendation and discovery help shoppers identify relevant items. Social commerce firms often collaborate with influencers or leverage user-generated content to reach their audience. Clinique has engaged younger audiences with smart social media marketing and social commerce.

In trend: Voice assistants

Smart speakers and speech assistants simplify online purchasing and customer relations. This technique utilizes AI to search for items and submit orders using multiple payment options. Use voice commands to monitor shipments and obtain unique suggestions.

Voice search-assisted ecommerce makes purchasing easier for multitasking online customers and makes conventional interfaces more accessible. Grocery and home goods stores may benefit from voice ordering’s simplicity. For years, firms have been generating speech search-friendly product descriptions and back-end voice assistant solutions. Dominos has had a smartphone app for ordering pizza since 2017.

The future of ecommerce

Ecommerce is growing internationally, thus new digital commerce formats are sprouting quickly. China, which accounts for more than half of global digital retail sales, has developed social commerce and live commerce rapidly. Live commerce in China went from an emerging invention to a channel where two-thirds of people purchased a product in a year in less than five years.

Most companies now need more than just PayPal hookups or listing things from their brick-and-mortar shop on an ecommerce platform. The IBM Institute for Business Value found that 14% of 20,000 customers in 26 countries were happy with internet purchasing. Innovation is needed to make ecommerce enjoyable.

IBM and ecommerce trends

Trade is difficult. Business consultancy thrives on powerful ecommerce and AI-driven omnichannel commerce. IBM iX provides digital strategy, design, implementation, integration, and operations consultancy and world-class ecosystem connections.

IBM Consulting professionals help organizations achieve their objectives by using data, AI, best ecommerce practices, and the open-source IBM iX Experience Orchestrator. You get insight-led, outcomes-driven client experiences that establish trust and boost your company’ relevance.