Puneet Jindal

Question : Why does an AI product manager need to monitor #AI model❓ Once AI model is developed and deployed, the job doesnt end there. Model encodes pattern of the data it has seen during training phase. There may be several patterns or edge cases which are not part of training data. If model encounters any such patterns in testing or production phase, it will produce results which wont make sense without giving any warning about it. 👉 Data drift is a common challenge faced by AI-powered products. Over time, the distribution of input data can shift, leading to a mismatch between the model's training data and the real-world data it encounters. This can result in degraded model performance, unexpected outputs, and even complete model failure. 👉 The consequences of data drift can be severe, ranging from suboptimal user experiences to regulatory compliance issues. Product managers must be vigilant in detecting and addressing these challenges to ensure the continued success of their AI-powered offerings. 👉 Sudden changes in metrics can indicate input data drift. Additionally, monitoring the distribution of model outputs can reveal output drift, which may signal a need to retrain or fine-tune the model. Detecting data drift early is crucial. Product managers can leverage techniques such as A/B testing, anomaly detection, and continuous monitoring to identify and address these issues. Once detected, the product team can take appropriate actions, such as retraining the model, adjusting the data pipeline, or even considering a model architecture change. 💡 Quick Tip: Integrate model monitoring into your product roadmap and allocate dedicated resources to ensure the long-term success of your AI-powered offerings. Establish a comprehensive model monitoring dashboard to gain real-time visibility into the health and performance of your AI models. #artificialintelligence #productmanagement #datascience #productmanager

5

🌟 Data Science in Action: Real-World Success Stories 🌟 Data science is transforming industries with its powerful applications. Here are some highlights: **1. Healthcare:** Predictive analytics in heart disease prevention is saving lives and cutting costs by enabling early interventions. **2. Retail:** Companies like Amazon use data to personalize recommendations, boosting customer satisfaction and sales. **3. Finance:** Banks detect fraud in real-time using advanced analytics, protecting assets and maintaining trust. **4. Entertainment:** Netflix and Spotify keep users engaged with personalized content recommendations. **5. Agriculture:** Farmers optimize yields with predictive analytics, improving efficiency and sustainability. **6. Transportation:** Uber and Lyft enhance ride experiences by optimizing routes through data analysis. **Success Spotlight:** A healthcare provider reduced readmission rates by 20% using machine learning to predict and manage patient care needs. Data science is driving innovation and creating smarter solutions across sectors. Let's harness its potential for a brighter future! #DataScience #AI #MachineLearning #BigData #Innovation #SuccessStories --- Share your thoughts or success stories below! 🚀

1

Spartificial has published it's new Tableau Course on Udemy and it's available for FREE. A complete Tableau data analysis and visualization walkthrough with multiple projects and a fine touch of Astronomy. Check the link in comments to access the full course for free. #tableau #dataanalysis

31

2 Comments

Question : Why does quoting just the accuracy numbers do not make sense for an AI model❓ Its a common practice among data science practitioners to quote accuracy of an #AI model. While it is easier to understand accuracy numbers, this practice can be misleading and fail to capture the nuances of real-world scenarios, leading to misaligned expectations among business stakeholders. 👉 Accuracy alone does not provide a comprehensive picture of a model's capabilities. It fails to account for crucial factors such as the size and distribution of the training, testing, and validation datasets, the presence of class imbalances, and potential biases. Relying solely on accuracy can mask underlying issues and lead to false confidence in the model's performance. 👉 To truly understand a model's effectiveness, it is essential to consider a broader range of metrics. Precision, recall and F1 score offer valuable insights into a model's ability to correctly identify positive and negative instances, handle imbalanced data, and discriminate between classes. 👉 However, even these technical metrics may not resonate with business stakeholders who are primarily concerned with the practical implications of the model's performance. It is crucial to translate these metrics into business-relevant terms that directly align with the product's objectives and success criteria. For example, in a fraud detection system, precision could be framed as the percentage of flagged transactions that are indeed fraudulent, while recall could represent the proportion of actual fraudulent transactions correctly identified. By presenting these metrics in a business context, stakeholders can better understand the trade-offs and make informed decisions about the model's deployment and ongoing monitoring. 💡 Quick Tip: Dont quote accuracy numbers without mentioning the size of training and testing dataset. Consider a diverse set of metrics tailored to your specific use case and business objectives. Communicate these metrics in a language that resonates with stakeholders, fostering a shared understanding and aligning expectations. #artificialintelligence #productmanagement #datascience #productmanager

6

That's lot of hardwork put in this book #ai #python #searchingalgo #visualization

12

2 Comments

Logarithm is a magical transformation. Before Calculators, Complex Multiplication, Division, were done using pre-computed Log-AntiLog Tables. Remember carefully checking the mantisa and the characteristic from last pages of standard- 6th Maths-textbook ? It helped converting complex Multiplication and Division, to much simpler operations - Addition and Subtraction. And, Slide-rule was a popular mechanical-device with logarithmic-scale for computation before our era of electronic calculators. Its believed, the device was used extensively in the first Moon-landing mission. See IBM's advertisement (1951) pitching how IBM-calculator can do work of 150 engineers with Slide-rules! PS: If you are a Modeler you might have been maximizing 'Joint-LOG-Likelihood' and not Just 'Joint-Likelihood' function, Simplifying multiplicative objective function into additive one and efficiently solving for parameter-coefficients in GLM. Moreover, Log is like a log stick, which turns a notorious mathematical function to a well behaved one ! There is a reason sab-log love log 🤗 #computation #statistics #datascience #predictivemodel

14

Question : Responsible AI deployment - what is that and why should an AI product manager bother about it❓ Ethical development of AI products is not just about algorithmic transparency or handling sensitive user data. One critical aspect of responsible AI deployment is the need for ongoing monitoring and evaluation. 👉 Product managers should establish robust mechanisms to continuously assess the performance and impact of their AI models, ensuring they remain aligned with intended use cases and do not exhibit unintended or harmful behaviors over time. 👉 Product managers must consider the potential for AI systems to amplify existing societal biases or create new forms of discrimination. Proactive measures, such as conducting bias assessments and implementing fairness-aware algorithms, are essential to mitigate these risks and promote inclusive and equitable AI solutions. 👉 Transparency and accountability are also crucial pillars of responsible AI deployment. Product managers should strive to provide clear and accessible information about their AI models, including their capabilities, limitations, and decision-making processes. 💡 Quick Tip: Track your model performance not only from technical point of view but also from ethical, social and regulatory implications of your AI product. #ArtificialIntelligence #productmanagement #productmanager #DataScience 

7

Unlocking the Power of Advanced RAG Techniques for Enhanced AI Retrieval 🔍✨ As organizations increasingly rely on Retrieval Augmented Generation (RAG) systems, it’s crucial to move beyond traditional RAG methods. By adopting advanced techniques, we can significantly enhance retrieval accuracy, response relevance, and adaptability. ###Key Techniques to Amplify Your RAG System’s Performance: 🔹 Input/Output Validation: Validate input and output to align precisely with use cases and organizational policies, ensuring results are contextually appropriate.   🔹 Guardrails: Implement compliance and audit trails in responses, fostering transparency and explainability.   🔹 Semantic Caching: Leverage caching for similar queries to increase efficiency while managing resources effectively.   🔹 Hybrid Search: Combine semantic and exact keyword matches to deliver the most relevant results, balancing depth with precision.   🔹 Re-ranking: Use data retrieval with re-ranking for heightened relevance and accuracy, enhancing end-user satisfaction.   🔹 Evaluations (Evals): Continuously refine responses using Reinforcement Learning from Human Feedback (RLHF) for adaptive, self-improving systems. ###Why Advanced RAG Matters: Traditional RAG can fall short in handling complex queries efficiently. With techniques like hybrid search, re-ranking, and semantic caching, advanced RAG addresses these limitations, optimizing both precision and contextual depth. Through RLHF and evaluation-driven feedback loops, advanced RAG models learn and adapt over time. This ensures an AI experience that is consistently accurate, context-rich, and aligned with evolving needs. Takeaway: Advanced RAG turns standard retrieval systems into dynamic, self-learning, and compliant AI solutions that not only retrieve accurate information but also evolve with each interaction. #jillanisofttech #jst #trendingpost #techtrend #llm #rag #retrievalaugmentedgeneration #nlp #hybridsearch #aicompliance #aicontinuouslearning #relevancymodel #guardrails #semanticcaching #selfimprovingai

6

Something I've written on how a T20 batting innings can be assessed. This framework can be extended to assessment of individual players as well, which I will do in the susequent posts. For now, please feel free to comment and if you like the content, please subscribe. #cricket #cricketanalytics #data #dashboards #datavisualization #sportsanalytics https://lnkd.in/gb7T-UzJ

28

6 Comments

Why Multimodal RAG is Important? 🔹It provides more accurate and comprehensive information. Multimodal RAG models combine the power of information retrieval and generative language models to produce answers that are not just accurate but also contextually enriched. This is because they can access and process a wider range of data sources, including text, images, and videos. 🔹It is more adaptable to different domains and tasks. Multimodal RAG models can be easily customized to provide information on a wide range of topics by leveraging different knowledge sources. This makes them ideal for use in a variety of applications, such as customer service, education, and research. 🔹It improves the overall user experience. Multimodal RAG models can provide more informative, reliable, and relevant responses, which can lead to a better user experience. This is because they can take into account the user's context and preferences, and they can provide explanations and citations for their answers. Check out this super interesting new course from Deeplearning AI in partnership with Weaviate. https://lnkd.in/d3H6tFsf

30

1 Comment

TimesFM - A decoder-only foundation model for time-series forecasting from Google AI TimesFM is a forecasting model, pre-trained on a large time-series corpus of 100 billion real world time-points, that displays impressive zero-shot performance on a variety of public benchmarks from different domains and granularities. https://lnkd.in/d-EKDpPu

8

Question : Why should an #AI product manager need a strategy for model deployment in PoC stages ❓ Until a model gets deployed in the field, its efficiency cannot be judged. Hence, as soon as the model is created, AI product managers must develop a robust model deployment strategy. Deployment method could depend upon the application, business use case and ease of use but it could very well define the success and scalability of AI product. 👉 One popular approach is through API creation, leveraging frameworks like FastAPI to expose the AI model as a web service. This allows for seamless integration with existing software systems and enables easy scaling as the user base grows. Additionally, browser-based deployment can provide a user-friendly interface, making it accessible to a wider audience. 👉 For edge device deployment, product managers should evaluate the trade-offs between on-device processing and cloud-based inference. On-device deployment can offer lower latency and improved privacy, while cloud-based solutions may provide greater computational power and easier maintenance. 👉 Other deployment methods include containerization, which allows for consistent and reproducible environments across different platforms, and serverless architectures, which can provide cost-effective and scalable solutions for certain use cases. When it comes to the deployment environment, both local setups and cloud servers have their advantages. Local deployments can offer greater control and customization, while cloud-based solutions often provide scalability, redundancy, and easier maintenance. 💡 Quick Tip: Regardless of the deployment method, product managers should prioritize a flexible and modular architecture that allows for easy experimentation, iteration, and scaling during the PoC stages. This will ensure the AI product's long-term success and adaptability to evolving market needs. #ArtificialIntelligence #productmanagement #productmanager #DataScience 

1

India's Economic Transformation: A Vision for 2050 – Dr Vijay Varadi Abstract This article explores a comprehensive vision for India's economic transformation by 2050, driven by four strategic recommendations: eradicating caste and religious barriers, fostering peaceful relations and reducing trade dependency, eliminating direct taxation to encourage a consumption-based economy, and adopting new technologies within a secure framework. By addressing caste and religion-based discrimination, India can enhance social cohesion, reduce internal conflicts, and unlock the full potential of its diverse population, thereby boosting human capital and economic growth. Promoting peace with neighboring countries and focusing on internal production for export-led growth can create a stable geopolitical environment and enhance economic resilience. Shifting to a consumption-driven economic model by eradicating direct taxes can stimulate consumer spending and production activity, driving economic expansion. Embracing cutting-edge technologies can boost productivity and innovation, positioning India as a global technology leader. With the successful implementation of these recommendations, India is projected to become one of the top three global economies by 2050, marked by robust economic growth, social unity, and technological advancements. This article integrates insights from various academic and non-academic sources, providing a comprehensive and unique perspective on India's potential economic trajectory. read full paper here: https://lnkd.in/garn2We4

8

1 Comment

How to create Table, Listing, and Figure (TLF) Shells: The Statistical Analysis Plan (SAP) and associated TLF shells are 1A and 1B when it comes to the deliverables the biostatistician produces during the study. The SAP is the written language version of the statistician telling any reviewers of the clinical trial how the data was analyzed (what populations were used, what statistical techniques were implemented, how missing data was handled, etc.). The Shells then, are the physical representation of what the written language says. The Shells give the programming team something to program towards AND give the CSR development team (Sponsor, Medical Writer, Medical, etc.) an idea of what the final outputs will look like. Here is how to ensure you create shells that are clear and meaningful to everyone: 1️⃣ Start with the SAP - as you write the SAP and any time you specify a table, figure, or listing will be generated, make a mark in the SAP for yourself. That mark is to tell you that you are calling for an output to be created. You can then create a table of contents for all the outputs the SAP is calling for. Remember, the SAP and Shells should be a 1:1 mapping exercise - if the SAP calls for an output, there had better be a shell, and if there is a shell, there had better be text in the SAP about that shell. 2️⃣ Stand on the shoulders of giants - Very rarely have I created shells truly from scratch. I am more times than not creating shells from a template my company or the sponsor has, OR I am finding results/looking up other studies that have presented results similar to the type of results I plan on presenting - if you don't have a shell for what MMRM output should look like you can always go to clin trials dot gov and search for studies that have their statistical analysis plans or results available and see if there are any examples included (if not directly available from clintrials you can see if the drug was approved, and if it was, check out it's SBA on the FDA's website for the results and some inspiration). 3️⃣ Take your time - Often (for one reason or another) you may find yourself with a tight timeline to create TLF shells. If you have any doubts that you do not have enough time to create the shells - speak with your manager as early as possible. The shells are CRITICAL for the success of the study analysis and if you rush through them you run the risk of the programming team getting stuck because the shells are not clear, or worse, you may end up creating a shell that cannot be used by the CSR development team. 4️⃣ If ever you are not sure what to include in a shell - include as much as you can - even if it is overkill. It is much simpler to remove something than it is to add something later in the game. Shells are a huge topic and one that deserves more time to be covered - if you have any questions about creating them please feel free to send me a direct message. Happy Monday

108

3 Comments

"Reporting vs Analysis" Sharing a sneak peek from our book 'Data Science and Machine Learning with Python' by Gurpreet Singh Josan & Jagroop Kaur Get your copy today: https://rb.gy/y6hcjp Visit our website for more information: https://techsarworld.com/ #DataScience #MachineLearning #Python #Reporting #Analysis #Techsar #TechsarWorld #BookPublishingCompany #AcademicBooks

1

ggplot2 is the gold standard when it comes to data visualization. Here's why: ✔️ Consistent, intuitive syntax that makes it easy to learn and use across various plot types. ✔️ Seamless integration with other tidyverse packages, enabling smooth data workflows. ✔️ Efficient handling of large data sets, ensuring smooth and responsive plotting even with complex data. ✔️ Over 100 extensions that enhance its core capabilities, providing endless options for creative visualizations. ✔️ Trusted by more than 1,000 packages, ensuring reliability and broad support. The image in this post showcases examples of ggplot2 visualizations, demonstrating its versatility to create a wide range of plots with nearly limitless customization options. Want to learn more? Check out my online course, "Data Visualization in R Using ggplot2 & Friends," starting on November 25, 2024, for a deeper dive into creating stunning plots with ggplot2. More information: https://lnkd.in/ehRrzdJ7 #package #ggplot2 #datasciencecourse

645

31 Comments

** Does AI gonna be present everywhere ???🤔**   Prof. Dr. P K RAJPUT tells: "COVID was like "समंदर-मंथन", The AI and ML market is projected to skyrocket to a staggering $80 billion, transforming industries on a global scale. ******************** We recorded this episode way before than ChatGPT was launched, Prof. Dr. P K RAJPUT predicted huge intervention of AI and ML in every industry... ************************** **Are you prepared for the AI and ML revolution?**   ********************   In today’s rapidly advancing world, even a stable position or booming business is no longer the final frontier. To truly thrive, embracing AI and Machine Learning (ML) is essential. 🚀   Here’s why: - **Ubiquity of AI and ML**: AI and ML are becoming the backbone of every industry, reshaping processes and strategies. If you’re not learning and leveraging these technologies, you risk falling behind. 📈  - **Smarter Decision Making**: AI and ML empower you to make data-driven decisions, optimizing outcomes with precision. Whether in finance, healthcare, or retail, these technologies are critical. 🧠✨  - **Future-Proof Your Career**: Integrating AI/ML into your skillset ensures you're prepared for the technological transformations ahead, opening doors to innovation and leadership roles. 🌍🔑 **Don’t stop growing after success—embrace AI and ML to stay ahead of the curve!**  #AIFuture #MLRevolution #UpskillYourself #FutureProof #TechGrowth Josh Bersin Gary Vaynerchuk Simon Sinek Vaishnavi Seth Melissa Robbins

10

4 Comments

Time series forecasting using generative models continues to be a challenging area owing to multiple factors like complex temporal dynamics (trends, seasonality), effect of dimensionality curse (very high dimensional data), non-stationarity problem, sparse datasets etc to name a few. IBM research team has introduced Tiny Time Mixers (TTM), a compact and efficient pre-trained model designed for enhanced zero/few-shot multivariate time series forecasting. TTM addresses the limitations of large pre-trained models, which often require significant computational resources and overlook cross-channel and exogenous correlations. Leveraging a lightweight architecture based on TSMixer, TTM incorporates innovative techniques like adaptive patching, diverse resolution sampling, and multi-level modeling to handle diverse dataset characteristics with minimal computational requirements. TTM significantly outperforms existing benchmarks in accuracy while being more resource-efficient, making it highly accessible for broader applications, including those constrained by computational resources. The research paper highlights TTM’s versatility in various forecasting tasks and its potential for widespread adoption due to its effectiveness on both CPU and GPU environments. Paper can be accessed from the following link: https://lnkd.in/ekqw5Uwn

16