Mavent Analytics’ Post

Evolution of the Data Scientist's Role in the era of GenAI LLMs   Common insights we hear... "GenAI can now understand, code, reason, analyze... With a quick turnaround for application development, GenAI-based application building normally takes 3-6 months with the only skill needed being application development, while data science projects take longer and require specialized, expensive data scientists..."   While this is partially true, companies with experts who have specialized skills and billions of dollars in investments have made GenAI models smart enough for widespread use. We can now utilize them in various ways and pay for their usage, effectively democratizing AI in a real sense.   Today, anyone with Wi-Fi and an email address can access these powerful tools and use them as companions. They can expedite their learning, research, and coding, organize information the way they want, and even build agents for repetitive tasks.   As We Mature… As these models evolve, we may need them to become more nuanced and specialized for specific domains like legal, finance, medical, government, education, etc. This will again require specialized skills to better understand data and model architecture for fine-tuning or building models from scratch. Recently, we've seen job postings for "Generative AI Data Scientists."   Why Data Science Projects Take Longer… It's not a data scientist issue, nor an ML models issue. The main problem is data. Normally, data scientists spend 70-80% of their time gathering and understanding data (sometimes not even getting the required data). The rest is spent on business understanding and model building.   We also know that the current state of GenAI is only possible because of decades of great research in ML, deep learning, and reinforcement learning fields.   Embracing GenAI GenAI is a great tool. Data scientists must learn to use these tools to increase productivity and understand model training, fine-tuning, and evaluation.   How the Role of Data Scientists Evolves in the era of GenAI Looking ahead, here are some ways generative AI will shape the role of data scientists, [1] Elevating Analytical Skills: Less grunt work allows for more focus on insights, metrics, and creative thinking. [2] Increasing Business Collaboration: Aligning contributions with business goals and fostering cross-functional partnerships. [3 ]Enabling Specialization: Gaining expertise in solution development and building deep knowledge base. [4] Embracing Hybrid Teams: Collaborating closely with AI experts to improve generative systems. [5] Addressing Ethical Considerations: Prioritizing fairness, accountability, privacy, and human oversight."   Generative AI will significantly boost data science team productivity. Data scientist's must re-skill in these new technologies.   With industry-wide adoption, generative AI creates more opportunities. The future is bright for versatile data experts who embrace AI as a partner, not a threat.  

As Generative AI takes center stage, its transformative applications in Data Engineering become increasingly apparent. Here’s a cool project that I recently worked on: 🍽️ Introducing "BITEBUDDY" : Make informed dining choices with AI 🍽️ Are you tired of menu anxiety and indecision? Say hello to BiteBuddy, your trusted crowdsourced dining companion! 🌟 🚀 Key Features: Personalized Recommendations: Uncover hidden gems and avoid menu duds with tailored suggestions. Data-Driven Decisions: Make informed choices based on real user experiences and crowdsourced data. Simple and Free: Easy to use, and the best part? It's completely free! Overview: BiteBuddy leverages Data Engineering, AI and crowdsourced data from various sources, including Google reviews, to recommend not just the best dishes at any restaurant but also provide a wholesome dining experience right from finding a restaurant, exploring the restaurant and post-dining feedbacks. No more relying on upselling waiters or blindly picking dishes. BiteBuddy empowers you to make informed dining choices with confidence! BiteBuddy also ventures into the world of multimodality through Google's Gemini Pro. 🌐 Project Highlights and Learnings: - Innovative "What to Order?" App: Empowering users with personalized food recommendations. - Data Exploration: Extensive exploration of approaches, from Google Local Data to Synthetic Data using LLMs. - Post-Processing Magic: Refining and organizing data for enhanced analytical insights. - Data: Journey through the essential threads in training Large Language Models (LLMs). - Approaches Explored: Uncover the strategies behind gathering diverse and reliable data. - Exploratory Data Analysis: Understand the meticulous preprocessing and cleaning of data. - Large Language Models: Selecting the right LLM and the post-processing magic for refined insights. Learn about the LLM testing approach and pricing insights. - Prompt Engineering: Continuous testing model for optimal performance. - Recommendation Score: Crafting a robust score for reliable meal recommendations. - Tech-Stack: The powerful technologies behind BiteBuddy - Snowflake, Python, Streamlit, Airflow, SerpAPI. Eat Like a Local with BiteBuddy! 🍣🍕 Thank you to professor Kishore Aradhya for your invaluable guidance throughout the progress and development, and Jared Videlefsky and Bhakti Ukey for being awesome teammates! Know more about the technicalities here : https://lnkd.in/epccuc2f I'm currently looking #opentowork for full-time opportunities in the DATA realm starting May 2024! Feel free to connect with me via email at shah.harsh7@northeastern.edu #GenerativeAI #DataScience #DataEngineering #dataanalytics #datagovernance #DataIngestion #Snowflake #ArtificialIntelligence #dataquality #Python #Data #GeminiPro #googlegemini #multimodality #llms #RecommendationSystems 

Quote of the Day: "But in many ways, consumer AI is a distraction. The game-changing opportunities for data science careers lie in enterprise-scale automation." Cassie Kozyrkov's article "How Can Organizations Think Differently to Get the Most Out of AI?" emphasizes the critical importance of focusing on enterprise-scale automation rather than consumer AI (= the uncountable tools we are exposed to every day). Everyone wondering how to organize Data/AI in their company to create scaled impact should keep that in mind when wondering if you should licence another tool or work on a solution that will scale. It is also relevant for every Data Expert - I would not limit it to Data Scientists - wondering about their future and how (and yes it will happen) their role will change. Data experts are crucial in identifying automation opportunities, designing testing and monitoring approaches, and managing the end-to-end AI implementation process. Their role ensures that enterprise-scale automation systems work effectively and safely. For data experts to fulfill this role, it means focusing not just on coding skills but also excelling in optimizing end-to-end technical communication and translating business needs into data-driven decision-making and AI applications. This strategic thinking is essential for solving the right problems and achieving meaningful business outcomes. Full article can be found here: https://lnkd.in/eVHwdPKb #AI #DataStrategy #BusinessTransformation #Inspiration #Leadership DAIN Studios

These should be on your radar! Today’s top AI job opportunities have landed, and we’ve curated the best just for you. 🔥 1. Berkeley Research Group - Data Engineer What's the role? Join BRG's Global Applied Technology team to build scalable data pipelines, automate data processes, and tackle data quality issues across industries. Collaborate with data experts and innovate data solutions. Why Berkeley Research Group? Berkeley Research Group is expanding fast with 30% headcount growth. Engineering up 20%, HR team strong – they care for talent. Focused on innovation, a solid place for AI job seekers! 🔥 2. Fearless - Data Engineer III What's the role? Coach teams on data-driven decision-making, advocating for best practices, and taking initiative for personal leadership growth. Design and maintain data pipelines and machine learning models, manage data quality, and collaborate with stakeholders to improve data systems and support their data infrastructure needs. Why Fearless? Fearless Digital specializes in creating user-friendly digital solutions that drive positive change. Their iterative development process minimizes risk for clients and ensures the delivery of responsive and efficient technology. 🔥 3. EA Team Inc - Big Data Developer What's the role? Manage AWS services, create ETL processes, and handle big data with Spark and Scala. Collaborate with global clients to define solutions and optimize data lakes. Why EA Team Inc? EA Team With 205 employees is experiencing solid growth! HR up 40%, showing focus on team well-being. Engineering boosted by 30%, indicating big tech priorities. Great for AI talent! 🔥 4. Machina Labs - Machine Learning Engineer What's the role? Develop and deploy machine learning models to predict manufacturing process parameters at Machina Labs. Analyze data, clean it, and support automation efforts. Collaborate with engineers and monitor model performance. Why Machina Labs? Machina Labs, with 81 employees, is thriving! They've ramped up engineering by 40%, signaling tech is a priority. HR is solid too, showing they value their people. A prime spot for AI pros! 🔥 5. @Arthur - Machine Learning Engineer What's the role? Enhance the performance, security, and robustness of large language models (LLMs) and other ML models, ensuring accurate and comprehensive responses to various user interactions. Additionally, develop tools for ML model monitoring, conducting experiments, and collaborating with teams to deploy ML-enabled features for customers. Why Arthur? Arthur is driven by a passion for creating AI that's not only powerful but also transparent, equitable, and accessible. Their cutting-edge platform empowers businesses to harness the full potential of machine learning while ensuring ethical and responsible AI practices. And as always, check the first comment for links to apply🫡 Last thing, no sponsorships here - Just our team scouting the best opportunities across the AI space.

The growing impact of AI on data analytics is reshaping the landscape of jobs in the field. While AI is automating certain tasks and processes traditionally performed by data analytics professionals, it is also creating new opportunities and increasing the demand for specialized skills. Here are some key implications of AI on jobs in the data analytics field: 1. **Automation of Routine Tasks**: AI technologies automate routine and repetitive tasks such as data cleaning, preprocessing, and basic analysis. As a result, data analytics professionals may spend less time on manual data processing and more time on higher-value tasks that require human judgment and expertise. 2. **Shift in Skill Requirements**: The rise of AI in data analytics is driving a shift in skill requirements. Data analytics professionals are increasingly expected to have skills in AI, machine learning, deep learning, and programming languages such as Python and R to leverage AI technologies for advanced analytics and modeling. 3. **Emergence of New Roles**: The adoption of AI in data analytics is leading to the emergence of new roles such as data scientists, machine learning engineers, AI specialists, and data engineers. These roles require specialized skills in AI, machine learning, and advanced analytics to develop and deploy AI-powered solutions. 4. **Focus on Interpretation and Strategy**: With AI handling more of the data processing and analysis tasks, data analytics professionals can focus on interpreting insights, deriving actionable recommendations, and driving strategic decision-making based on the outcomes of AI algorithms. 5. **Continuous Learning and Upskilling**: The rapid evolution of AI technologies requires data analytics professionals to engage in continuous learning and upskilling to stay abreast of the latest trends, tools, and techniques in AI and data analytics. Professionals need to adapt to new technologies and methodologies to remain competitive in the field. 6. **Collaboration with AI Systems**: Data analytics professionals are increasingly collaborating with AI systems and algorithms to leverage their capabilities for data analysis, prediction, and optimization. Understanding how to work effectively with AI technologies is becoming a valuable skill in the data analytics field. 7. **Increased Demand for Data Literacy**: As AI plays a more prominent role in data analytics, there is a growing demand for data literacy skills among employees across various functions within organizations. Data analytics professionals can play a vital role in promoting data literacy and facilitating data-driven decision-making throughout the organization. Overall, while AI is changing the nature of jobs in the data analytics field, it is also creating opportunities for professionals to enhance their skills, take on more strategic roles, and drive innovation through the effective integration of AI technology.

5 AI tools that are making 50% of data analyst jobs obsolete (and how to stay relevant) But before you panic, remember: tools change, but insights are forever. Here's the scoop on the AI tools shaking up our world, and how to ride the wave instead of being washed away. The Game-Changers: 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗲𝗱 𝗘𝗧𝗟 𝗧𝗼𝗼𝗹𝘀 (ex: Alteryx, Trifacta) 𝗪𝗵𝗮𝘁 𝘁𝗵𝗲𝘆 𝗱𝗼: Clean and prep data in minutes, not hours. 𝗦𝗸𝗶𝗹𝗹𝘀 𝗮𝘁 𝗿𝗶𝘀𝗸: Manual data cleaning, basic SQL 𝗔𝘂𝘁𝗼 𝗠𝗟 𝗣𝗹𝗮𝘁𝗳𝗼𝗿𝗺𝘀 (ex: DataRobot, H2O.ai) 𝗪𝗵𝗮𝘁 𝘁𝗵𝗲𝘆 𝗱𝗼: Build and deploy machine learning models with minimal coding. 𝗦𝗸𝗶𝗹𝗹𝘀 𝗮𝘁 𝗿𝗶𝘀𝗸: Basic predictive modeling, model selection 𝗡𝗮𝘁𝘂𝗿𝗮𝗹 𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝗕𝗜 (ex: ThoughtSpot, IBM Analytics) 𝗪𝗵𝗮𝘁 𝘁𝗵𝗲𝘆 𝗱𝗼: Generate visualizations and insights from natural language queries. 𝗦𝗸𝗶𝗹𝗹𝘀 𝗮𝘁 𝗿𝗶𝘀𝗸: Basic dashboard creation, SQL querying 𝗔𝗜-𝗣𝗼𝘄𝗲𝗿𝗲𝗱 𝗗𝗮𝘁𝗮 𝗦𝘁𝗼𝗿𝘆𝘁𝗲𝗹𝗹𝗶𝗻𝗴 (ex: Automated Insights) 𝗪𝗵𝗮𝘁 𝘁𝗵𝗲𝘆 𝗱𝗼: Automatically generate written reports from data. 𝗦𝗸𝗶𝗹𝗹𝘀 𝗮𝘁 𝗿𝗶𝘀𝗸: Basic report writing, data summarization 𝗟𝗮𝗿𝗴𝗲 𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝗠𝗼𝗱𝗲𝗹𝘀 (ex: GPT-4, Claude) 𝗪𝗵𝗮𝘁 𝘁𝗵𝗲𝘆 𝗱𝗼: Perform a wide range of analytical tasks, from coding to interpretation. 𝗦𝗸𝗶𝗹𝗹𝘀 𝗮𝘁 𝗿𝗶𝘀𝗸: General-purpose analysis, basic coding Here's how to stay cool (and employed) in the AI era: 𝗦𝘁𝗮𝘆𝗶𝗻𝗴 𝗥𝗲𝗹𝗲𝘃𝗮𝗻𝘁: 𝗬𝗼𝘂𝗿 𝗔𝗰𝘁𝗶𝗼𝗻 𝗣𝗹𝗮𝗻 ✅𝗘𝗺𝗯𝗿𝗮𝗰𝗲 𝘁𝗵𝗲 𝗔𝗜 𝘀𝗶𝗱𝗲𝗸𝗶𝗰𝗸: Learn to use these tools. They're not your replacement, they're your superpower. ✅𝗟𝗲𝘃𝗲𝗹 𝘂𝗽 𝘆𝗼𝘂𝗿 𝗰𝗿𝗶𝘁𝗶𝗰𝗮𝗹 𝘁𝗵𝗶𝗻𝗸𝗶𝗻𝗴: AI can crunch numbers, but it can't ask "So what?" Focus on developing insights that drive business decisions. ✅𝗖𝗼𝗺𝗺𝘂𝗻𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗶𝘀 𝗸𝗲𝘆: Sharpen your ability to translate data into stories that non-technical stakeholders understand and act on. ✅𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁, 𝗱𝗼𝗻'𝘁 𝗷𝘂𝘀𝘁 𝗮𝗻𝗮𝗹𝘆𝘇𝗲: Move upstream. Focus on designing data strategies and architectures that AI can execute. ✅𝗦𝗽𝗲𝗰𝗶𝗮𝗹𝗶𝘇𝗲 𝗶𝗻 𝘁𝗵𝗲 𝗵𝘂𝗺𝗮𝗻 𝘁𝗼𝘂𝗰𝗵: Ethics, strategy, and contextual interpretation are your new best friends. AI can't navigate the grey areas of data science. ✅𝗕𝗲𝗰𝗼𝗺𝗲 𝗮 𝗹𝗶𝗳𝗲𝗹𝗼𝗻𝗴 𝗹𝗲𝗮𝗿𝗻𝗲𝗿: The only constant is change. Commit to continuous learning and stay ahead of the curve. Remember, in a world of AI, the most powerful tool is still the human mind. We're not just surviving the AI revolution – we're leading it. 💪 What's your take? Are these AI tools enhancing your work or keeping you up at night? #data #dataanalytics #ai #genai #tools

Gen AI changing the world of data visualization already very promising field in business science engineering R&D and AI itself and might be in market sentiment analysis and NLP.

🌟🚀 𝐀𝐈 𝐍𝐞𝐰𝐬 🚀🌟 🚀𝑴𝑰𝑻 𝒓𝒆𝒔𝒆𝒂𝒓𝒄𝒉𝒆𝒓𝒔 have introduced 𝐆𝐞𝐧𝐒𝐐𝐋, a generative AI system for databases that makes it easier for users to perform complex statistical analyses on tabular data without needing to understand the underlying technical details. 𝘾𝙖𝙥𝙖𝙗𝙞𝙡𝙞𝙩𝙞𝙚𝙨 𝙤𝙛 𝙂𝙚𝙣𝙎𝙌𝙇: - Helps users make predictions, detect anomalies, guess missing values, fix errors, or generate synthetic data with just a few keystrokes. - Can catch anomalies in data, like a low blood pressure reading for a patient who normally has high blood pressure. - Automatically integrates a tabular dataset and a generative probabilistic AI model to provide more accurate answers. - Can generate synthetic data that mimics real data, useful for situations where sensitive data cannot be shared. 𝘼𝙙𝙫𝙖𝙣𝙩𝙖𝙜𝙚𝙨 𝙤𝙫𝙚𝙧 𝙚𝙭𝙞𝙨𝙩𝙞𝙣𝙜 𝙖𝙥𝙥𝙧𝙤𝙖𝙘𝙝𝙚𝙨: - GenSQL was found to be 1.7 𝐭𝐨 6.8 𝐭𝐢𝐦𝐞𝐬 𝐟𝐚𝐬𝐭𝐞𝐫 than popular AI-based data analysis methods, while producing more accurate results. - The probabilistic models used by GenSQL are explainable, allowing users to read and edit them. 𝘾𝙤𝙢𝙗𝙞𝙣𝙞𝙣𝙜 𝙢𝙤𝙙𝙚𝙡𝙨 𝙖𝙣𝙙 𝙙𝙖𝙩𝙖𝙗𝙖𝙨𝙚𝙨: - GenSQL was built to bridge the gap between SQL, which is good at querying data, and probabilistic models, which can provide deeper insights on individual-level implications. - GenSQL allows users to query both a dataset and a probabilistic model using a single programming language. For more details checkout below link: https://lnkd.in/gK7WKMkg 🔥𝑭𝒐𝒍𝒍𝒐𝒘 𝑴𝒆 𝒇𝒐𝒓 𝒅𝒂𝒊𝒍𝒚 𝑨𝑰 𝑵𝒆𝒘𝒔 𝒂𝒏𝒅 𝑼𝒑𝒅𝒂𝒕𝒆𝒔! #genai #datascience #llm #machinelearning #deeplearning #database #gensql #sql #ai #ainews

🔓 Unlocking the Power of AI: Transforming Data Engineering with Advanced Tools Traditionally, data engineering involved building and maintaining data pipelines, integrating disparate data sources, and ensuring data quality and reliability. While these tasks remain essential, the rise of artificial intelligence (AI) has ushered in a new era of possibilities for data engineering. 🤖 Automating Data Integration and ETL Processes :- One of the key areas where AI is making a significant impact is in data integration and ETL . AI algorithms can analyze data usage patterns, identify relationships between different data sets, and automatically map data fields, streamlining the integration process. 🏅 Enhancing Data Quality Assurance :- Data quality is paramount for effective decision-making. However, maintaining data quality across diverse data sources can be a daunting task. AI-powered data engineering tools leverage machine learning algorithms to detect anomalies, identify patterns, and flag potential data errors or inconsistencies in real-time. 🔮 Predictive Analytics and Forecasting :- Predictive analytics is another area where AI is transforming data engineering. By analyzing historical data and identifying patterns, AI algorithms can generate accurate forecasts, enabling organizations to anticipate market trends, optimize resource allocation, and mitigate risks. 🎶 Natural Language Processing for Unstructured Data :- In today's data-driven world, a valuable information is locked away in unstructured data sources such as text documents, emails, social media feeds, and customer reviews. AI-powered data engineering tools equipped with natural language processing (NLP) capabilities can extract insights from unstructured data, uncovering hidden trends, sentiment analysis, and emerging topics. 🕹 Automated Machine Learning (AutoML) :- Building and deploying machine learning models traditionally required significant expertise in data science and machine learning. However, with the advent of AutoML, this barrier to entry is rapidly diminishing. AI-driven AutoML tools automate various aspects of the machine learning, including data preprocessing, feature selection,model training, hyperparameter tuning, and model evaluation. By democratizing machine learning, AutoML empowers data engineers to develop predictive models with ease, even without extensive domain knowledge. My view :- In today's fast-paced world, Developers are the pioneers of innovation. However, many organizations impose restrictions on employees for using AI tools. This tools can save countless hours of human effort. By utilizing AI-powered tools developers can unlock a wealth of possibilities. 📣 PS: Please share your views on the use of AI tools in domains like Data Engineering. Let's start a conversation! 🚀 #DataEngineering #DataPipeline #ETL #DataWarehousing #CloudDataEngineering #DataArchitecture #DataQuality #DataEngineeringJobs #DataEngineeringCareer