Legacy analytics platforms such as Excel, MATLAB and SAS require specialty skillsets, have expensive licensing, or don’t provide the automation and scale you are looking for. Migrate to a modern data science platform on Microsoft Azure or Databricks to reduce your costs and provide better integration with your enterprise systems.
Many data scientists work on their own laptops, bringing security concerns, preventing at-scale workloads, and making it hard to integrate data science workloads with the wider enterprise. Migrating workloads to a modern data science platform on Azure can free your data scientists with scalable compute and accessible data, while reducing the risk of data breaches.
Excel is a powerful tool, but your team may be running into its limitations with data sizes and automation. Take your analyst team to the next level by converting your complex spreadsheets and Visual Basic scripts into modern python code, and take advantage of Tallan’s expert data science mentoring approach to ease the transition and upskill your people for the future.
Older third party platforms such as MATLAB and SAS are powerful, but come with expensive licensing restrictions and limited cloud compute options, and today it’s harder to find those skillsets. Migrating to Azure and Databricks removes the capital expenditure of licenses, so you only pay for what your team uses, and gives you access to a wider hiring pool.
Microsoft Azure is the perfect base for your modern data science platform. Interconnected platform services enable productive collaboration between data scientists and engineers, and secure data services reduce the risk of data breaches while enabling your people to access what they need, when they need it.
Azure Machine Learning Studio offers secure managed compute resources and data access alongside straightforward ways of tracking and deploying your data science workloads in batch or real time. These features help reduce your time to ROI by months.
Databricks provides a unified analytics platform with a powerful Apache Spark engine, offering near limitless scale for your data science workloads alongside a user friendly python, spark, and SQL notebook experience. It also has inbuilt workload and experiment tracking, and simple deployment options.
