Tecton’s Post

The challenge of integrating data science and MLOps lies in the differing needs and best practices of each domain. Data science thrives on exploratory, iterative workflows, while MLOps requires a more rigorous engineering approach for production. Establishing a collaboration and handoff process between data scientists and MLOps engineers is crucial for successful ML products. Cross-functional teams can align incentives and facilitate communication, leading to more efficient and effective outcomes. Prioritizing this reorganization is essential for businesses to thrive in today's economic climate.

Recently completed a foundational course in Data Engineering – truly, data is the heartbeat of our world. Recognizing its crucial role in daily life, aiding organizations in informed decision-making, and laying the foundation for advanced AI systems has been enlightening. Taking it upon myself to master the skills of extracting, transforming both structured and non-structured data, loading, and refining it for enhanced analysis and structure. Excited about new goals in 2024! 🚀  #Data #DataEngineering #DataAnalysis #NewBeginnings Check it out: https://lnkd.in/dEST-KDT 

𝗗𝗮𝘁𝗮 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝘃𝘀 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲, 𝘄𝗵𝗲𝗿𝗲 𝗱𝗼 𝘁𝗵𝗲𝘆 𝗯𝗼𝘁𝗵 𝗳𝗶𝘁 𝗶𝗻 𝘁𝗵𝗲 𝗱𝗮𝘁𝗮 𝗲𝗰𝗼𝘀𝘆𝘀𝘁𝗲𝗺? Day 05/30 of the data engineering journey This is a long-going debate, people come up with their own perspectives. The majority of the experts claim that they are separate from each other but they do complement each other. Data Engineering sits upstream from Data Science, that is the inputs of the Data Scientists are actually the outputs of Data Engineers, yet they work together. Lets visualize their roles through the data science hierarchy of needs by Monica Rogati. Although many data scientists enjoy building up ML models, 70-80% of their time is spent dealing with the bottom three parts of the hierarchy: gathering, storing, and processing data, only a slice of their time is spent on ML and analysis. If companies want to build solid AI/ML models, they need to establish a solid data foundation(bottom three levels of hierarchy). In an ideal world, Data scientists work on top layers of the hierarchy - analytics, experimentation and analytics. The bottom layers are the responsibility of Data engineers. When they focus on the bottom layers, they build a solid foundation for data scientists to succeed. While data engineering sits between data collection and getting value from data, it is of equal importance as data science, playing a vital role in making data science successful in production. #DataEngineering #30DaysOfDataEngineering #FundamentalsOfDataEngineering

Reflecting on the "AI-Powered Data Engineering" workshop by Databricks, The rapid evolution of our field is remarkable. This session wasn't just about new tools - it was a glimpse into the future of #DataEngineering. Key insights: The convergence of AI and data engineering is reshaping our approach to ETL processes. Delta Live Tables (DLT) exemplify this, offering declarative pipelines that adapt to data changes, reducing manual intervention. #ServerlessCompute is more than a buzzword—it's a paradigm shift. The ability to scale resources automatically for workflows, notebooks, and DLT pipelines promises to dramatically reduce operational overhead and optimize costs. #DataGovernance is becoming increasingly critical. #UnityCatalog's integration across the Databricks ecosystem signals a move towards unified, AI-assisted data management - crucial in our age of proliferating data sources and regulatory scrutiny. The concept of a "Data Intelligence Platform" is intriguing. By leveraging AI to optimize data layouts, query performance, and even generate SQL, Databricks is blurring the lines between data engineer and data scientist roles. The introduction of #LakeFlow hints at a future where data pipelines are not just automated, but intelligent - potentially reshaping how we approach data transformation and quality control. This workshop challenged me to reconsider our current data architectural practices. Are we fully leveraging AI in our ETL processes? How can we transition to more declarative, self-optimizing pipelines? As data professionals, we must remain at the forefront of these technological advancements. This session emphasized that future success in our field will likely depend on the effective integration of traditional data engineering expertise with advanced AI capabilities. I look forward to applying these concepts regarding the modernization of our data infrastructure. #DataEngineering #AI #FutureOfData #Databricks #ContinuousLearning

Generative AI: A Game-Changer for Data Engineering in 2024 ? The world of data engineering is constantly evolving, and 2024 promises exciting new possibilities. Generative AI, with its ability to create realistic data and text formats, is poised to be a game-changer. By leveraging this innovative technology alongside advancements like vector databases, data engineers can unlock the true potential of data. Ready to explore the future of data engineering? Check out my full blog post for a deeper dive into the trends shaping the landscape in 2024: 👇 👇 https://lnkd.in/g64UwC3a

Data science relies on working with different types of data systems and architecture. For data scientists or analysts just getting started, some of the terminology can be confusing. In this post, we’ll dive into definitions and examples for five fundamental data science concepts: - 𝗗𝗮𝘁𝗮 𝗟𝗮𝗸𝗲: A centralized repository that stores huge amounts of raw, unstructured data in native formats. Provides flexible, scalable storage for diverse analytics and ML needs. - 𝗗𝗮𝘁𝗮 𝗠𝗮𝗿𝘁: A subset of company data focused on the needs of a specific team or use case. Enables access to tailored data without irrelevant info. - 𝗗𝗮𝘁𝗮 𝗣𝗶𝗽𝗲𝗹𝗶𝗻𝗲: Automates the flow and transformation of data from sources to destinations. Ingests, processes, and routes data for downstream uses. - 𝗗𝗮𝘁𝗮 𝗪𝗮𝗿𝗲𝗵𝗼𝘂𝘀𝗲: Integrates data from multiple sources into one unified repository. Structured for querying, analysis, and reporting. Provides a single source of truth. - 𝗗𝗮𝘁𝗮 𝗤𝘂𝗮𝗹𝗶𝘁𝘆: The accuracy, completeness, consistency, and relevance of data. Essential for reliable analytics and decisions. - 𝗗𝗮𝘁𝗮 𝗟𝗮𝗸𝗲𝗵𝗼𝘂𝘀𝗲: Combines data lakes and warehouses. Stores raw data like a lake along with preparing and structuring data like a warehouse. Provides flexibility for diverse data and uses. 👉 Follow Gina Acosta Gutiérrez 👩🏻💻 for more resources! ➡ Get the latest AI news, tools, tutorials and guides on using popular AI tools for FREE here: www.joinhorizon.ai #python #data #database #datascience #machinelearning #ai #artificialintelligence #programming #dataanalysis #analytics #coding #tech #developer #cloudcomputing #technology

Navigating the data science and machine learning projects often feels like a Catch-22 💔 Data science teams need to overcome significant challenges transitioning from "data science output" to "deployable data science output," especially in cost-optimised business environments. It's a predicament familiar to many—limited funding from business lines makes it challenging for data science teams to prove the potential uplifts their models can bring. This often results in a frenzied race to deliver tangible results, pushing teams to expedite the process without necessarily ensuring robustness. On the flip side, once the models are complete, businesses eagerly anticipate a swift transition to production. The need for speedy results puts pressure on data science and engineering teams, who might be dealing with hastily crafted notebooks and rushed deployments. We've witnessed too many instances where the collaborative efforts of data science and engineering teams suffer due to fragile engineering processes What’s worse is that most teams only come to realise the importance of these practices after they've already experienced the consequences of suboptimal MLOps workflows. At this point, trust has faltered, making it challenging for data science teams to regain the confidence of the business stakeholders. 📉 However, there is a path forward: embracing a proactive approach rather than settling for reactive fixes. 1️⃣ Review current practices and bottlenecks 2️⃣ Learn industry best practices and anti-patterns 3️⃣ Create a “gold standard process” for your team 4️⃣ Elect an MLOps champion 5️⃣ Slowly migrate the “bottlenecks” to “gold standards” By prioritising robust engineering and thoughtful deployment, we can foster a cycle of trust and efficiency, leading to better outcomes for all involved. #MelioAi #DeployableDataScience #ProactiveApproach #MLOpsProcesses #RobustEngineering #BuildingTrust

Reminds me my first steps in Datawarehousing/BI operations and lifecycle in late 90's early 2000's. Old terms, new/ approaches and tools (for example, data federation, unstructured data, tokenization, model training/validation/testing/deployment, ...).

Useful insights! Gaining familiarity with the right terms and having a clear concept is the main step toward career development and personal growth. 🌱💡 #CareerDevelopment #PersonalGrowth #ProfessionalDevelopment #LearningJourney 🚀

Basic topics for Data Engineering.