Feature Engineering for Machine Learning

Feature Engineering for Machine Learning

Feature engineering is a crucial aspect when it comes to designing machine learning models, and it plays a big role in creating top-notch AI systems. Features are attributes that represent the problem of the machine learning use case and contribute to the model’s prediction. The process of feature engineering involves creating relevant and useful features from raw data combined with existing features, adding more variables and signals to improve the model’s accuracy and performance. It starts manually and can be accelerated by adding automated feature engineering tools and techniques. Follow the steps of feature engineering to optimize your machine learning models and create innovative products.


Machine Learning in IoT: Advancements and Applications

Machine Learning in IoT: Advancements and Applications

The Internet of Things (IoT) is rapidly changing various industries by improving processes and products. With the growth of IoT devices and data transmissions, enterprises are facing challenges in managing, monitoring, and securing devices. Machine learning (ML) can help generate intelligence by working with large datasets from IoT devices. ML can create accurate models that analyze and interpret the data generated by IoT devices, identify and secure devices, detect abnormal behavior, and prevent threats. ML can also authenticate devices and improve user experiences. Other IoT applications benefiting from ML include predictive maintenance, smart homes, supply chain, and energy optimization. Building ML features on IoT edge devices is possible with TensorFlow Lite.

DataOps: Cutting-Edge Analytics for AI Solutions

DataOps: Cutting-Edge Analytics for AI Solutions

DataOps is an essential practice for organizations that seek to implement AI solutions and create competitive advantages. It involves communication, integration, and automation of data operations processes to deliver high-quality data analytics for decision-making and market insights. The pipeline process, version control of source code, environment isolation, replicable procedures, and data testing are critical components of DataOps. Using the right tools and methodologies, such as Apache Airflow Orchestration, GIT, Jenkins, and programmable platforms like Google Cloud Big Query and AWS, businesses can streamline data engineering tasks and create value from their data. Krasamo’s DataOps team can help operationalize data for your organization.

What Is MLOps?

What Is MLOps?

MLOps are the capabilities, culture, and practices (similar to DevOps) where Machine Learning development and operations teams work together across its lifecycle

ETL Pipelines and Data Strategy Overview

ETL Pipelines and Data Strategy Overview

Data is a primary component in innovation and the transformation of today’s enterprises. But developing an appropriate data strategy is not an easy task, as modernizing and optimizing data architectures requires highly skilled teams.

About Us: Krasamo is a mobile-first Machine Learning and consulting company focused on the Internet-of-Things and Digital Transformation.


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