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What is AI data management? October 2, 2024 AI data management is the practice of using artificial intelligence (AI) and machine learning (ML) in the data management lifecycle. Examples include applying AI to automate or streamline data collection, data cleaning, data analysis, data security and other data management processes. Both traditional, rules-based AI and more advanced generative AI models can help with data management. Modern enterprises own vast amounts of data on everything from financial transactions and product inventory to employee records and customer preferences. Organizations that use this data to inform decision-making and drive business initiatives can gain significant advantages over their competitors. However, the challenge comes from making these large datasets accurate, reliable and accessible enough for people to use them in practice. This week, we’ll take you through the importance of high-quality data for AI. 👉 Explore data management tools, benefits and solutions Is Data Management the Secret to Generative AI? Every conversation starts with generative AI, but ends with data. Why? Because there is no AI without data. To understand how enterprises use generative AI to contribute to competitive advantage, we must think about the relationship between gen AI and data. In this episode of AI Academy, explore why high-quality data is essential for the successful use of generative AI. ⤵ https://lnkd.in/dfvxDSGr Optimizing Data Governance “A more precise view of the makeup of a data set can enable organizations to have more confidence in the insights and decisions coming from their AI systems. So it’s absolutely critical that any provider of AI systems understands the provenance of the data they’re using," says Lee Cox , VP for Integrated Governance and Market Readiness, IBM Office of Privacy and Responsible Technology. 👉 Discover the need for greater data transparency Fostering Greater Data Ecosystem Transparency IBM partnered with the Data & Trust Alliance and 18 other enterprises to co-create and test the Data Provenance Standards, the first cross-industry standards for metadata to help describe the data origin, lineage, and suitability for purpose. These standards can help fill a critical gap, enabling greater transparency about data provenance and foster the development of trustworthy and responsible AI across all industries. 👉 Read the study now Using AI and Data Ethically to Make More Efficient Decisions https://lnkd.in/dPRfQR3g 👁️🐝Ⓜ️ Let us help you keep up with the speed of AI. Tune into our AI in Action Podcast to learn how to put AI into practice: ibm.biz/BdKFgN

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After experimenting with multiple configurations, here are the key settings that saved the day and helped us process massive datasets efficiently without Spark giving up on us! 🚀 spark.conf.set("spark.sql.adaptive.enabled", "true") 💡 Adaptive Query Execution (AQE) for Smarter Query Planning spark.conf.set("spark.sql.adaptive.enabled", "true") spark.conf.set("spark.sql.adaptive.coalescePartitions.enabled", "true") spark.conf.set("spark.sql.adaptive.skewJoin.enabled", "true") 💡Dynamic Partition Pruning spark.conf.set("spark.sql.optimizer.dynamicPartitionPruning", "true") 💡Broadcast Join Optimizationspark.conf.set("spark.sql.autoBroadcastJoinThreshold", "104857600") 💡Increase Shuffle Partitions for Parallelism spark.conf.set("spark.sql.shuffle.partitions", "1500") 💡 Efficient Serialization spark.conf.set("spark.serializer", "org.apache.spark.serializer.KryoSerializer") 💡 Control Partition Size for Faster Reads spark.conf.set("spark.sql.files.maxPartitionBytes", "250MB") 💡Increase parallelism spark.conf.set("spark.default.parallelism", "1000")

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I have no interest in Pyspark‼️ Don't ever say this, if you want to become a Data Engineer. Here are 𝗧𝗢𝗣 𝟭𝟬 free playlists to master Pyspark. 1. PySpark Interview Series https://lnkd.in/gCaU7P8W 2. Databricks and PySpark Full Course https://lnkd.in/gg6T6aiJ 3. Spark Fundamentals https://lnkd.in/gyEqQRYV 4. Spark with Python https://lnkd.in/gbnBrTHu 5. PySpark - Zero to Hero https://lnkd.in/gY5PkTFR 6. Pyspark in Hindi https://lnkd.in/gkfDsBsm 7. Databricks | Spark Learning Series https://lnkd.in/gif8bPjF 8. Pyspark Transformations https://lnkd.in/gVbXRiXg 9. Pyspark Tutorial for Begineers https://lnkd.in/giekg94q 10. Spark Streaming with Pyspark. https://lnkd.in/gT5pra5r 𝗚𝗲𝘁 𝘁𝗵𝗲 𝗙𝘂𝗹𝗹 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 𝗽𝗿𝗲𝗽 𝗸𝗶𝘁 𝗳𝗼𝗿 𝗗𝗮𝘁𝗮 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝘀 𝗵𝗲𝗿𝗲 - https://lnkd.in/gNH-trFm If you've read so far, do LIKE the post👍

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🦙 Tool calling with Ollama We now have a partner package with Ollama to help you perform tool calling, which is now natively supported in Ollama. Tools are utilities (like APIs or custom functions) that enhance an LLM's capabilities. However, local LLMs struggle with both selecting the right tool and providing the correct input. In the video below, we use the new Ollama partner package to perform tool calling w/ the recent Groq fine-tune of Llama-3 8b. See how to create a simple tool calling agent in LangGraph with web search and vector-store retrieval tools that run locally. 🎥 Video: https://lnkd.in/erppcmdY 🐍 Partner package (Python): https://lnkd.in/ej7KUQCr 🦏 Partner package (JavaScript): https://lnkd.in/eY8giWBY 📓 Notebook: https://lnkd.in/ewfNM_dc

Meta releases Lamma 3.1 The models will be available on all major clouds including AWS, Azure, Google, Oracle, and more. Meta working with a range of companies to grow the broader ecosystem. Amazon, Databricks, and NVIDIA are launching full suites of services to support developers fine-tuning and distilling their own models. https://lnkd.in/g9JHmiH6