Chaos Genius’ Post

I've finally had a chance to dig into DBRX, and it's great! In this article, I discuss how someone can utilize this state-of-the-art open-source LLM, Databricks' AI-generated comments, and views from your information schema (and why it's perfect for this use case) to create a simple notebook that allows you to speak to your data using natural language. Running the AI-generated code via Spark, using Model Serving, and safely executing code within Unity Catalog make this whole process seamless. #Databricks #DBRX

Snowflake has unveiled an innovative open-source large language model Here are the key highlights: - Snowflake has launched Arctic, an open-source large language model (LLM) designed to enable users to build enterprise-grade AI models and applications. - Arctic uses a unique Dense-MoE Hybrid transformer architecture. It combines a 10B dense transformer model with a residual 128×3.66B MoE MLP resulting in 480B total and 17B active parameters  - Snowflake claims Arctic is the most open, enterprise-grade LLM available, and also one of the most powerful. - The model is available under an Apache 2.0 license, with its weights, code, data recipes and research insights open-sourced.(Thats best thing) - Snowflake says Arctic was built in less than three months and at roughly one-eighth of the training costs of similar LLMs. - Arctic (17B active parameters) can have up to 4x less memory reads than Code-Llama 70B, and up to 2.5x less than Mixtral 8x22B (44B active parameters), leading to faster inference performance. - They also released embedding model but did not mentioned in article. - One downside I feel is that the model's size is too large, requiring 963.07 GB of storage. Article : https://lnkd.in/d-EtSHhg try Now : https://lnkd.in/dsaCj6RV HuggingFace : https://lnkd.in/dATUwS-3

Snowflake Arctic, a large language model (LLM), utilizes a Dense-MoE Hybrid transformer architecture to enhance training efficiency and reduce costs. It employed a three-phase training curriculum focused on enterprise metrics like SQL generation and coding, allowing it to perform competitively with models trained on much higher budgets. Arctic’s architecture combines a 10B dense transformer with a 128x3.66B MoE MLP, enabling efficient training and inference by leveraging system designs that minimize communication overhead among the model’s experts. Open-sourced under the Apache 2.0 license, Arctic provides access to its model weights and training techniques, aiming to support the development of custom models within the AI community https://lnkd.in/gunTtEzM

Snowflake Launches Arctic, An Open ‘Mixture-Of-Experts’ LLM To Take On DBRX, Llama 3 Snowflake announced the launch of Arctic, a large language model (LLM) optimized for complex enterprise workloads such as SQL generation, code generation and instruction following. Touted as the “most open enterprise-grade LLM” in the market, Arctic taps a unique mixture of expert (MoE) architecture to top benchmarks for enterprise tasks while being efficient at the same time. It also delivers competitive performance across standard benchmarks, nearly matching open models from Databricks, Meta and Mistral at tasks revolving around world knowledge, common sense, reasoning and math capabilities. https://lnkd.in/ghAZ5jNn

𝐔𝐧𝐥𝐞𝐚𝐬𝐡𝐢𝐧𝐠 𝐭𝐡𝐞 𝐏𝐨𝐭𝐞𝐧𝐭𝐢𝐚𝐥 𝐨𝐟 𝐑𝐀𝐏𝐈𝐃𝐒 🚀 𝐓𝐋;𝐃𝐑 With the advent of RAPIDS, the landscape of data science and analytics is undergoing a seismic shift, empowering professionals to harness the full potential of GPU acceleration for faster insights and decision-making. Have you ever envisioned a realm where data processing and analytics operate at lightning speed, revolutionizing workflows and outcomes? Let's explore the realm of RAPIDS! ➡ 𝐓𝐡𝐞 𝐆𝐢𝐚𝐧𝐭 𝐀𝐦𝐩𝐥𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧: RAPIDS stands tall as the powerhouse of GPU-accelerated libraries and frameworks. ➡ 𝐓𝐡𝐞 𝐓𝐫𝐚𝐝𝐢𝐭𝐢𝐨𝐧: Traditional data processing methods bow before the speed and efficiency of RAPIDS. 🌟 𝘞𝘩𝘢𝘵'𝘴 𝘵𝘩𝘦 𝘮𝘢𝘨𝘪𝘤? RAPIDS transforms data science workflows, turbocharging them with the parallel computing prowess of GPUs, delivering results at unprecedented speeds. 💡 𝘒𝘦𝘺 𝘛𝘢𝘬𝘦𝘢𝘸𝘢𝘺𝘴 𝐏𝐞𝐫𝐟𝐨𝐫𝐦𝐚𝐧𝐜𝐞: RAPIDS redefines what's possible in data science, enabling practitioners to push the boundaries of innovation and discovery. 𝐒𝐦𝐚𝐫𝐭 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠: With RAPIDS, data scientists can accelerate their workflows, delivering insights and value to stakeholders in record time. 𝐈𝐧𝐧𝐨𝐯𝐚𝐭𝐢𝐯𝐞 𝐒𝐩𝐥𝐢𝐭-𝐖𝐨𝐫𝐤: RAPIDS empowers organizations to stay ahead of the curve, leveraging GPU-accelerated computing to gain a competitive edge in the digital age. It's exciting, isn't it? 🌟 🔎 𝘈𝘤𝘤𝘦𝘭𝘦𝘳𝘢𝘵𝘪𝘯𝘨 𝘵𝘩𝘦 𝘗𝘰𝘵𝘦𝘯𝘵𝘪𝘢𝘭 - https://rapids.ai/ _______________ Extra Python libraries: • 𝐃𝐚𝐬𝐤: a low-level scheduler and a high-level partial Pandas replacement, geared toward running code on compute clusters. (https://lnkd.in/gTgd9-wj) • Ray: a low-level framework for parallelizing Python code across processors or clusters. (https://lnkd.in/gTUYUGC9) • 𝐌𝐨𝐝𝐢𝐧: a drop-in replacement for Pandas, powered by either Dask or Ray. (https://lnkd.in/gX9wJtrj) • 𝐕𝐚𝐞𝐱: a partial Pandas replacement that uses lazy evaluation and memory mapping to allow developers to work with large datasets on standard machines. (https://lnkd.in/ghy5rUT4) ______ #RAPIDS #DataScience #Analytics #GPU #Acceleration

Understanding Evidently-AI “Evidently helps evaluate, test, and monitor data and ML-powered systems.” It does this through 3 core components: 1️⃣ Reports.  ✔ We can view as inline in jupyter notebook cells ✔ Can also export to JSON, HTML, Dictionary, DataFrames 2️⃣ Test suites ✔ A lot of presets for the commonly used generic ones ✔ Returns a Pass / Fail  3️⃣ Dashboards ✔ Individual report cards as panels or combine individual tests ✔ Use the Cloud or Self-host ✔ Impute into database via scripts to export to external solutions like Prometheus, Grafane, etc  If you’ve used the ydata_profiling library (renamed from pandas-profiling) for pandas dataframes before, the context is pretty similar. In module-05 Monitoring of #mlopszoomcamp, we integrate 3 services into a Docker Container: ✔ Postgres database to store the metrics data ✔ Adminer for UI to our postgres database ✔ Grafana for external dashboarding examples The baseline example notebook had us use 2 months of data. First month's dataset is split to train and validate our model, and the val_data is then used as our reference. The second month's dataset is then used to simulate current data to be compared against the refence and generate some metrics for our report. Which we then save as a dictionary so that we can extract the pertinent metrics for our Dashboard. These extracted metrics can also be routed to a #postgress database that our #grafana Dashboards rely on. There’s still so much to learn about #evidentlyai, more study is required while I continue on to the next module. #mlopszoomcamp #mlops #zoomcamp #DataTalksClub image credits: from module 6.1 of Open-source ML observability course by Evidently AI

Just finished Exploring Data Science with .NET using Polyglot Notebooks & ML.NET! Check it out: https://lnkd.in/gzGqh5Ss #polyglot #netframework #datascience

Notion shares an insightful peek into their massive data growth, covering lessons learned and best practices building their org-wide data-lake: During their large-scale data journey Notion transitioned from a single Postgres instance to a sharded architecture and built an in-house data lake using S3, Kafka, Debezium, and Apache Hudi. This infrastructure was introduced to reduce costs and data ingestion times, whilst supporting update-heavy workloads and enabling advanced AI and Search features without fully replacing existing solutions like Snowflake and Fivetran. https://lnkd.in/dEYxAZDQ -- If you liked this article you can join 60,000+ practitioners for weekly tutorials, resources, OSS frameworks, and MLOps events across the machine learning ecosystem: https://lnkd.in/eRBQzVcA #ML #MachineLearning #ArtificialIntelligence #AI #MLOps #AIOps #DataOps #augmentedintelligence #deeplearning #privacy #kubernetes #datascience #python #bigdata

🌐 Just wrapped up an insightful webinar on "Leveraging RAG in Snowflake Cortex and Streamlit for Real-World AI Solutions." A big thanks to the esteemed panelists Arjun Srinivasan, Saravana Omprakash, Shashikiran Mavinakere Lokesh, Shankar Narayanan SGS, and Vivekanandan Srinivasan for their valuable insights. Key takeaways: 🔍 Data's Role in AI: Emphasized the importance of data quality, consistency, security, and management. High-quality data is crucial for building trustworthy pipelines and ensuring ethical AI practices. 🚀 Gen AI Challenges: Discussed the hurdles faced with data integration, legacy systems, and the necessity of upscaling skills to overcome resistance. 💡 Scalability: Highlighted the significance of scalable data platforms and data lakes in AI development. Special focus was given to the integration of LLMs and RAG (Retrieval-Augmented Generation) within the Snowflake ecosystem, showcasing how Snowpark technologies enhance real-time, context-aware AI solutions. For those interested in diving deeper into Snowpark, the link for the book which was mentioned in the session is "https://lnkd.in/gNbGFtE3" Looking forward to applying these learnings in future projects! #AI #GenerativeAI #Snowflake #LLMs #RAG #DataScience #Webinar

Snowflake Inc. has introduced a new open-source large language model (LLM), named Arctic. Snowflake asserts that Arctic is exceptionally powerful due to its “mixture of experts” architecture and boasts unmatched openness in its Apache 2.0 licensing, which allows broad usage across personal, research, and commercial endeavors. Newly appointed CEO Sridhar Ramaswamy highlighted this as a pivotal moment for Snowflake, marking its expanded focus from primarily data warehousing to pioneering in generative #AI.