Ninestars: Headlining the FIBEP World Media Intelligence Congress 2023 in Singapore

The world of media intelligence is on the brink of an extraordinary convergence, as the International Association for Media Intelligence, FIBEP, gears up to host the 2023 World Media Intelligence Congress from October 25th to 27th in the vibrant city of Singapore. This event, boasting over 130 business members from more than 60 nations, marks a pivotal moment in our journey as we step into the global spotlight. With immense joy and pride, we’re thrilled to announce that Ninestars isn’t just an attendee but the headline sponsor of the FIBEP World Media Intelligence Congress 2023. Join us as we embark on this remarkable adventure in Singapore, one of the Four Asian Tigers known for its rapid economic growth and status as a foreign financial hub.

At the core of this congress lies a theme that’s bound to resonate with every media intelligence enthusiast: “Future Proof Your Media Intelligence.” As the world grapples with an ever-evolving landscape and the challenges it presents, the FIBEP World Media Intelligence Congress 2023 promises to be an arena of insights and innovation. Prepare to be engaged, enlightened, and enriched through a tapestry of presentations, thought-provoking panel discussions, interactive roundtable talks, and the sharing of best practices.

We’re elated to announce that our Chief Strategy Officer, Mohan Doshi, will be gracing the stage as a distinguished speaker at this prestigious event. It’s a moment of immense pride for us, and we’re eager to share his wisdom with our global peers. Mohan will be speaking on “Machine Learning Operations for Enterprise: Transition from Research to Production” on October 27 from 10:15 AM to 11:00 AM. Join us to listen to him talk about bridging the formidable gap between cutting-edge research and the intricate, unpredictable, and unregulated operationalization of deep tech in the real world.

Joining him are some of our finest minds in the industry, including our Chairman Gopal Krishnan, VP Suresh Kumar and our COO Chinni Krishnan.

But that’s not all! We’re not just attending; we’re actively participating. Ninestars will have its booth space at FIBEP, where we’re set to unveil our tech-powered media intelligence solutions to the global community. We invite you to connect with our team and explore the ever-evolving landscape of media intelligence. Book a meeting with us, email us at to embark on this exciting journey together.

As we celebrate 25 years in business, Ninestars takes immense pride in being the headline sponsor for this year’s FIBEP World Media Intelligence Congress. Whether you’re curious about our operationalization of deep tech in media intelligence or simply want to extend your congratulations for our anniversary, we welcome you to drop by and say hi. We’ve always cherished our relationship with the FIBEP community, and this year, we’re ready to elevate it to new heights.

Let’s make this an extraordinary journey together. See you in Singapore!

Find out more about the FIBEP World Media Intelligence Congress here.

Stay connected with us: #WMIC2023 #Singapore #MediaIntelligence #MediaAnalytics

Revolutionizing Media Intelligence: How Generative AI Is Reshaping the Landscape 

In today’s dynamic and data-driven media environment, staying ahead is not merely an advantage; it’s a necessity. Media intelligence teams are tasked with monitoring, analyzing, and interpreting vast amounts of information in real-time. However, the scale and complexity of modern media content have presented unprecedented challenges. This is where Generative AI emerges as a transformative force, reshaping the media intelligence landscape, and enabling teams and companies to not only thrive but to scale their operations efficiently.

The Evolution of Media Intelligence

Media intelligence, once a manual and time-consuming process, has evolved significantly to keep pace with the digital era. Today, it involves the collection, analysis, and interpretation of diverse media data, including news articles, social media posts, multimedia content, and more. The need for real-time insights and accurate reporting has never been greater.

Challenges in Traditional Media Intelligence

Traditional methods of media intelligence has several critical challenges:

Information Overload: The sheer volume and diversity of media content overwhelmed human analysts, making it impossible to process everything effectively.

Timeliness: In a world where information travels at the speed of light, delays in accessing insights could be detrimental to decision-making.

Subjectivity: Human analysts’ interpretations could introduce bias, impacting the objectivity of reports.

 Scalability Issues: As data volumes grew, scaling traditional media intelligence operations required significant resources and often led to inefficiencies.

Generative AI: The Catalyst for Transformation

Generative AI, a subset of artificial intelligence, has emerged as a catalyst for change in media intelligence by addressing these challenges:

Automated Content Generation: Generative AI automates the generation of summaries, articles, and reports from extensive datasets, dramatically reducing the time and effort required for analysis.

Multimodal Analysis: It can process and analyze multimedia content, including images, videos and audio, providing a holistic understanding of media data.

Real-time Insights: Generative AI processes data at incredible speeds, delivering real-time insights that empower organizations to respond swiftly to emerging trends and events.

Objectivity: Generative AI operates without inherent bias, ensuring more objective analysis of media data.

Scalability: It scales effortlessly, handling large datasets efficiently, and allowing media intelligence teams and companies to expand their reach.

Scaling with Generative AI

Rather than making media intelligence teams and companies redundant, Generative AI enhances their capabilities and helps them scale effectively:

Efficiency: By automating repetitive tasks, Generative AI frees up human analysts to focus on higher-value tasks such as strategic analysis and decision-making.

Cost Savings: Reduced human effort and increased efficiency translate into cost savings for media intelligence operations.

 Real-time Monitoring: Generative AI enables real-time monitoring of a vast array of media sources, ensuring that nothing important is missed.

Competitive Advantage: Organizations that embrace Generative AI gain a competitive advantage by accessing insights faster and more comprehensively.

Personalization: Generative AI tailors insights to specific requirements, providing personalized and actionable data.

 A Bright Future for Media Intelligence

Generative AI is not rendering media intelligence teams and companies obsolete; it’s enabling them to thrive in an era of information abundance. As it continues to evolve, media intelligence operations will become more efficient, insightful and scalable, ensuring that organizations can navigate the ever-changing media landscape with precision and confidence.

Ninestars, with its decades of experience in media monitoring and media intelligence, is at the forefront of integrating Generative AI with enterprises. We are working tirelessly to harness the power of Generative AI and tailor it to the unique needs of media intelligence teams and companies. Our goal is to ensure that this transformative technology works seamlessly, enhancing your operations, and empowering you to scale your media intelligence efforts. Contact us today to explore how Generative AI, combined with our expertise, can elevate your media intelligence to unprecedented results.

What is MLOps? A Beginner’s Guide

MLOps, short for Machine Learning Operations, represents a crucial paradigm shift in the field of data science and machine learning. It’s the bridge between the exciting world of machine learning research and the practical, real-world deployment of ML models in businesses and enterprises.

MLOps can be likened to DevOps in its rigor. It aims to enhance communication and cooperation between data scientists responsible for creating machine learning models and operations teams tasked with managing these models in production. MLOps accomplishes this by automating repetitive tasks and enhancing feedback loops.

Key Components of MLOps

Continuous Integration and Continuous Deployment (CI/CD): CI/CD is the backbone of MLOps. Just as software developers use CI/CD pipelines to automate the testing and deployment of software updates, data scientists and ML engineers use similar pipelines to streamline the deployment of machine learning models. CI/CD helps ensure that changes to ML models can be quickly and safely deployed into production.

Model Deployment and Monitoring: Deploying a machine learning model into a production environment is not a one-time event. It’s an ongoing process. MLOps involves setting up systems that can deploy models automatically, monitor their performance in real-time, and trigger alerts if anything goes awry. This proactive approach ensures that models continue to perform as expected.

Data Management and Version Control: In MLOps, data is as critical as code. Proper data management and version control are essential to ensure that the right data is used for training and that the data used in production remains consistent. This prevents issues caused by data drift, where the data distribution in production differs from what the model was trained on.

Collaboration Across Teams: MLOps encourages collaboration among data scientists, machine learning engineers, and IT operations teams. It breaks down the silos that often exist between these groups, enabling them to work together seamlessly. This collaboration is crucial for successful model deployment and maintenance.

Model Governance and Security: Ensuring the security and ethical use of machine learning models is paramount. MLOps includes practices for model governance, such as access control and auditing, to prevent unauthorized use or abuse of models. It also addresses ethical concerns related to bias and fairness in machine learning.

Scalability and Resource Management: As businesses grow, the demands on machine learning systems increase. MLOps provides strategies for scaling ML models to handle larger datasets and higher workloads. It also helps manage resources efficiently to control costs.

Advantages of MLOps

  • Efficiency and Agility: MLOps streamlines the process of deploying and maintaining ML models. This means that businesses can respond more quickly to changing market conditions and make data-driven decisions faster.
  • Reduced Risk: By automating deployment and monitoring, MLOps reduces the risk of human error, which can lead to costly mistakes in production. It also helps with model governance and security, reducing the risk of data breaches or unethical use of AI.
  • Scalability: As businesses grow, the demands on machine learning systems increase. MLOps provides the tools and practices needed to scale ML operations efficiently, ensuring that models can handle larger datasets and workloads.
  • Cross-Functional Collaboration: MLOps encourages collaboration between data science and IT teams. This collaboration is essential for successful model deployment and maintenance, as it combines the expertise of both groups.
  • Adherence to Regulatory Compliance: In industries like healthcare and finance, regulatory compliance is critical. MLOps helps ensure machine learning models meet regulatory requirements by providing traceability, auditing, and security measures.


MLOps represents a fundamental shift in how machine learning is practiced. It’s not just about building models; it’s about deploying, monitoring, and maintaining them in the real world. As businesses increasingly rely on AI and machine learning, MLOps is becoming a critical discipline for success.

At Ninestars, we understand the importance of MLOps in harnessing the power of AI for business growth. We’re actively implementing MLOps in our operations to streamline processes, enhance efficiency, and deliver top-notch solutions. If you’re looking to make the most of AI in your business, consider partnering with us to leverage the benefits of MLOps and drive your success forward.