In today’s fast-paced digitalized landscape, businesses need to redefine their tech-driven data strategy to stay ahead of the curve. Machine Learning (ML) is one of the key aspects of this strategy that businesses can leverage to harness the power of data and analytics.
By adopting powerful Machine Learning tools to analyze data smartly, companies can make better decisions that are specifically based on quality data insights. However, to accomplish the desired outcomes, businesses need a Data Strategy that is integrated with a Machine Learning approach, which is the key to achieving the much-needed competitive advantage.
Why should businesses consider integrating the ML approach into their Data Strategy?
Data Strategy driven by a Chief Data Officer generally encompasses integrated processes and technology toolsets to acquire, store and govern Data as an Asset. With the Modern Data Architectures that delivers elastic scaling, high availability, best data security, streaming analytics, collaboration, automation, and we also get extended capabilities like Visualization and ML.
Azure Synapse Analytics is a Unified Analytics Platform that not just acquires, stores and governs data but as well delivers insights for the business through the integrated capabilities of Visualization and Machine Learning.
With a Unified Analytics Platform building out ML solutions is much easier, quicker and costs less. Business should be able to realize the value of data earlier by integrating Data Strategy and Machine Learning.
ML enables businesses gain valuable insights and drive innovation, making them better equipped to navigate today’s complex business landscape. An integrated ML approach as part of a business’s data strategy can have the following benefits:
- Accelerate ML adoption in decision-making: By interconnecting the ML use cases and the data that gets onboarded into a Data Lake, we can apply ML algorithms within the data platform. Businesses can get early access to the benefits of ML leading to better decision-making. By expediting ML adoption, the customers of an enterprise would see a direct value with personalized offerings and more targeted benefits.
- Maximize the leverage on Cloud Services and processes: The effort for platform capabilities configuration, evangelization and cloud services consumptions can be optimized with the integrated ML approach. Automation processes like DevOps, MLOps can be looked at together reducing the need for manual intervention, saving time, and increasing efficiency.
Machine Learning with Azure Synapse Analytics
One of the techniques used for training machine learning models is Automated ML, which does not require extensive prior knowledge of the field. It involves a process that trains multiple models and allows the Data Scientists to choose the best model based on predefined metrics. Integration of Azure Synapse Notebooks and Azure Machine Learning, allows the Data Scientists to access Azure Machine Learning workspace from Synapse, making the process even more streamlined.
Azure Synapse has integrated capabilities that enables the activities across the ML lifecycle all in one unified platform. Capabilities include right from data acquisition, exploration, preparation, model building, training, scoring and deployment.
Azure Data Factory (Synapse Pipelines) an integral component of Azure Synapse offers a robust suite of resources for data ingestion and orchestration pipelines, enabling users to construct data pipelines effortlessly. With these tools, we can acquire and transform data into a machine-readable format, making it easier to utilize for machine learning purposes.
Data can be explored using Apache Spark or Serverless SQL pools directly over data in the data lake. Synapse Studio has built-in visualizations.
Azure Synapse Spark Pools can be used to train machine learning models with PySpark/Python, Scala, or .NET and Spark MLIB ML algorithms. Additionally, the integration with Azure Machine Learning allows for easy implementation of Automated ML in Synapse.
Azure OpenAI, which is part of the Azure Cognitive Services stack is a suite of natural language processing (NLP) models developed by OpenAI which are applied for text generation, summarization and translation. Now Azure OpenAI’s GPT models are accessible through SynapseML.
Synapse empowers SQL professionals familiar with T-SQL to deploy machine learning models and as well invoke the models for scoring with T-SQL construct. In Dedicated SQL pool ‘ TSQL PREDICT’ function runs predictions within the data warehouse.
How data exploration and building quick ML pilots are made easier on Synapse?
The Knowledge Center in Azure Synapse Analytics brings together popular reusable assets, blueprints for data warehouse and analytics together. The Browse gallery provides predefined templates with various objects like Database, Datasets, Notebooks, SQL scripts, Pipelines, etc.
The Knowledge Center also provides numerous sample datasets for example Bing Covid-19 data, New York City safety data, Covid Tracking Project, Public holidays, etc. We can add these datasets in the Synapse studio to analyze further or create visuals.
These templates and toolsets enable us to rapidly build and run ML models with the data contextual to our business.
Building a Machine Learning app on Azure Synapse is easier but for it to become an enterprise scalable model as setting up a ML COE will require us to adopt MLOps to build a collaborative and scalable ML team.
Check out our Webinar series for enterprises adopting ML using Azure ML Studio and Azure Synapse Analytics. Watching the recording will provide you with an understanding and key insights about the MLOps toolsets as well as the options available on Azure to address the challenges of operationalizing and monitoring ML models in production.
To learn more about Azure Synapse Analytics architecture and how it can help modernize an Enterprise Data Estate please check Demystifying Data Estate Modernization with Azure Synapse Analytics.
Contact us to learn how we can help you build a complete end-to-end Advanced Analytics solution using Microsoft Azure Synapse Analytics.