Kavitha R.

The exponential growth of machine learning models brings with it ever-increasing datasets. This data deluge creates a significant bottleneck in the Machine Learning Operations (MLOps) lifecycle, as traditional data preprocessing methods struggle to scale. The preprocessing phase, which is critical for transforming raw data into a format suitable for model training, can become a major roadblock to productivity.

To address this challenge, in this article, we propose a distributed data… Show more

The exponential growth of machine learning models brings with it ever-increasing datasets. This data deluge creates a significant bottleneck in the Machine Learning Operations (MLOps) lifecycle, as traditional data preprocessing methods struggle to scale. The preprocessing phase, which is critical for transforming raw data into a format suitable for model training, can become a major roadblock to productivity.

To address this challenge, in this article, we propose a distributed data preprocessing pipeline that leverages the power of Google Kubernetes Engine (GKE), a managed Kubernetes service, and Ray, a distributed computing framework for scaling Python applications. This combination allows us to efficiently preprocess large datasets, handle complex transformations, and accelerate the overall ML workflow. Show less

Open-source approaches provide organizations with the flexibility to deploy—and, if necessary, migrate—critical workloads to, across, and from public cloud platforms. The same applies to artificial intelligence (AI) and machine learning (ML).

Every organization and ML project has unique requirements, and Google Cloud provides multiple solutions to address different needs, including a variety of open-source options. For example, some customers choose Vertex AI, Google Cloud’s… Show more

Open-source approaches provide organizations with the flexibility to deploy—and, if necessary, migrate—critical workloads to, across, and from public cloud platforms. The same applies to artificial intelligence (AI) and machine learning (ML).

Every organization and ML project has unique requirements, and Google Cloud provides multiple solutions to address different needs, including a variety of open-source options. For example, some customers choose Vertex AI, Google Cloud’s fully-featured AI/ML platform to train, test, tune, and serve ML models, including gen AI solutions and support for open-source frameworks and models. Others choose to build a custom ML platform by combining open-source technologies with Google Cloud managed services for additional flexibility.

Feast, an ML feature store, is one such open-source technology. It helps store, manage, and serve features for machine learning models across the key stages of the ML model development process. It also integrates with multiple database backends and ML frameworks that can work across or off cloud platforms. Show less

Machine learning allows users to drive insights about their business. Many organizations recognize the benefits of ML for business decision making. They also realize the challenges of working with multidimensional data that make it difficult and time-consuming to be analyzed by humans.

Additionally, there are many business users who have deep knowledge of the data but insufficient data science and machine learning expertise to create ML models for their complex data.

The AutoML… Show more

Machine learning allows users to drive insights about their business. Many organizations recognize the benefits of ML for business decision making. They also realize the challenges of working with multidimensional data that make it difficult and time-consuming to be analyzed by humans.

Additionally, there are many business users who have deep knowledge of the data but insufficient data science and machine learning expertise to create ML models for their complex data.

The AutoML approach provides a solution to these situations by speeding up the process through the automation of the pipeline steps. Show less