Collaborative Data Science | DatabricksSkip to main contentPlatformThe Databricks Lakehouse PlatformDelta LakeData GovernanceData EngineeringData StreamingData WarehousingData SharingMachine LearningData SciencePricingMarketplaceOpen source techSecurity and Trust CenterWEBINAR May 18 / 8 AM PT
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Learn about LLMs like Dolly and open source Data and AI technologies such as Apache Spark™, Delta Lake, MLflow and Delta SharingExplore sessionsCustomersPartnersCloud PartnersAWSAzureGoogle CloudPartner ConnectTechnology and Data PartnersTechnology Partner ProgramData Partner ProgramBuilt on Databricks Partner ProgramConsulting & SI PartnersC&SI Partner ProgramPartner SolutionsConnect with validated partner solutions in just a few clicks.Learn moreCompanyCareers at DatabricksOur TeamBoard of DirectorsCompany BlogNewsroomDatabricks VenturesAwards and RecognitionContact UsSee why Gartner named Databricks a Leader for the second consecutive yearGet the reportTry DatabricksWatch DemosContact UsLoginJUNE 26-29REGISTER NOWCollaborative Data ScienceA unified experience to boost data science productivity and agilityGet startedData Scientists face numerous challenges throughout the data science workflow hindering productivity. As organizations continue to become more data-driven, a collaborative environment for easier access and visibility into the data, models trained against the data, reproducibility, and insights uncovered within the data is critical.The ChallengeData exploration at scale is difficult and costlySpending too much time managing infrastructure and DevOpsNeed to stitch together various open source libraries and tools for further analyticsMultiple handoffs between data engineering and data science teams are error prone and increase risksHard to transition from local to cloud-based development due to complex ML environments and dependenciesThe SolutionQuick access to clean and reliable data for downstream analyticsOne click access to pre-configured clusters from the data science workspaceBring your own environment and multi-language support for maximum flexibilityA unified approach to streamline the end-to-end data science workflow from data prep to modelling and insights sharingMigrate or execute your code remotely on pre-configured and customizable ML clustersDatabricks for Data ScienceAn open and unified platform to collaboratively run all types of analytics workloads, from data preparationto exploratory analysis and predictive analytics, at scale.WorkspaceWorkspaceOne central place to store and share notebooks, experiments, and projects backed with role-based access control.NotebooksData StoreDashboardsExperiments TrackingCollaborative Data Science at ScaleCollaboration across the entire data science workflow, and moreCollaboratively write code in Python, R, Scala, SQL, explore data with interactive visualizations, and discover new insights with Databricks notebooks.Confidently and securely share code with co-authoring, commenting, automatic versioning, Git integrations, and role-based access controls.Keep track of all experiments and models in one place, capture knowledge, publish dashboards, and facilitate hand-offs with peers and stakeholders across the entire workflow, from raw data to insights.Learn moreFocus on the data science, not the infrastructureYou don’t have to be limited by how much data fits on your laptop anymore, or how much compute is available to you.Quickly migrate your local environment to the cloud with Conda support, and connect notebooks to auto-managed clusters to scale your analytics workloads as needed.Learn moreUse PyCharm, Jupyter Lab or RStudio with scalable computeWe know how busy you are… you probably already have hundreds of projects on your laptop, and are accustomed to a specific toolset.Connect your favorite IDE to Databricks, so that you can still benefit from limitless data storage and compute. Or simply use RStudio or Jupyter lab directly from within Databricks for a seamless experience.Learn moreGet data ready for data scienceClean and catalog all your data in one place with Delta Lake: either batch, streaming, structured or unstructured, and make it discoverable to your entire organization via a centralized data store.As data comes in, quality checks ensure data is ready for analytics. As data evolves with new data and further transformations, data versioning ensures you can meet compliance needs.Discover and share new insightsYou’ve done all the work and identified new insights with built-in interactive visualizations or any other supported library like matplotlib or ggplot.Easily share and export results by quickly turning your analysis into a dynamic dashboard. The dashboards are always up to date, and can run interactive queries as well.Cells, visualizations, or notebooks can also be shared with role-based access control and exported in multiple formats including HTML and IPython Notebook.Learn moreSimple access to the latest ML frameworksGet going fast with one-click access to ready-to-use and optimized Machine Learning environments including the most popular frameworks like scikit-learn, XGBoost, TensorFlow, Keras and more. Or effortlessly migrate and customize ML environments with Conda. Simplified scaling on Databricks helps you go from small to big data effortlessly, so that you don’t have to be limited with how much data fits on your laptop anymore.The ML Runtime provides built-in AutoML capabilities, including hyperparameter tuning, model search, and more to help accelerate the data science workflow. For example, accelerate training time with built-in optimizations on the most commonly used algorithms and frameworks, including Logistic Regression, Tree-based Models, and GraphFrames.Learn moreAutomatically track and reproduce resultsAutomatically track experiments from any framework, and log parameters, results, and code version for each run with managed MLflow.Securely share, discover, and visualize all experiments across workspaces, projects, or specific notebooks across thousands of runs and multiple contributors.Compare results with search, sort, filter, and advanced visualizations to help find the best version of your model, and quickly go back to the right version of your code for this specific run.Learn moreOperationalize at scaleSchedule notebooks to automatically run data transformations, modelling, and share up to date results.Set up alerts and quickly access audit logs for easy monitoring and troubleshootingLearn more
Customer Story 

Saving millions in inventory management
Shell has deployed a data science tool globally to help it manage and optimise the $1 billion in spare part inventory it holds in case something breaks on its assets.
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