Machine Learning Accelerator: 6-Week Implementation

MAQ Software

Transform your business by implementing machine learning accelerators

Machine Learning (ML) helps in making accurate predictions based on patterns in data, and with ML accelerators, the process is made faster by enhancing the performance of the system. These accelerators can perform matrix operations and other common computations used in machine learning much more quickly than general-purpose CPUs. In this 6-week engagement, we will implement a highly scalable machine learning solution in Azure by leveraging different azure-based tools like ADF (Azure Data Factory), Databricks, and ADLS (Azure Data Lake Storage) that will help in enhancing the performance of processes such as ingestion, storage, transformation, and processing.

Target Customers

  • Data scientists
  • Cloud service providers
  • Software developers
  • Business leaders

Agenda

  • An initial assessment of your current data infrastructure and machine learning needs
  • Designing and implementing a data pipeline tailored to your organization's specific requirements
  • Performing data quality checks and data preprocessing techniques
  • Data integration with your existing systems
  • Applying data governance and security measures
  • Hands-on training for your team on the best practices for data pipeline management and maintenance

Deliverables

  • Data Ingestion and Preprocess of data to make it ready for ML models
  • Exploratory Data Analysis and Model development
  • Evaluate the performance of the ML model and optimize its speed
  • A POC of the application that demonstrates the capabilities of the accelerators

Benefits

  • A streamlined and efficient data pipeline that enables faster and more accurate machine learning model development
  • Improved data governance, security, and accessibility
  • Reduced data preprocessing time and increased data accuracy
  • Assistance with deploying and monitoring the model
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https://store-images.s-microsoft.com/image/apps.47075.f355e052-dea5-486d-8c3a-748593f0a22c.49187b12-fed1-402f-8e54-87cd781e40b6.5d836b1e-87e3-4f22-aa20-5b98ccc31829