This process ensures that Business Insights are generated in a timely manner against the most recent dataset. It also keeps the ML model up-to-date, detects anomalies, and merges daily changes with historical data. Let’s assume we have created the necessary workflows for data preparation, model training, model prediction, and analytical reports. Continuous Machine Learning can be implemented by creating a Training Pipeline and a Prediction Pipeline in Sparkflows. The Model Tr

Machine learning models provide powerful capabilities to make predictions and gain insights from data. However, developing accurate models is only the first step. To fully realize value, models need to be properly deployed and served. This allows them to be used in applications and drive business impact. Sparkflows provides MLOps capabilities to deploy models built within its platform or models developed externally. This post will explore how to serve Sparkflows models for bo

Dataset Collection Collect a dataset specific to the target domain or task you want the LLM to excel at. This dataset should be relevant and representative of the specific language patterns and concepts the model needs to learn. Preprocessing Preprocess the domain-specific dataset, cleaning the text, normalizing it, and tokenizing it into smaller units like words or subwords. Ensure that the preprocessing steps align with the format used during the pre-training of the LLM. Pr

In the realm of artificial intelligence, the convergence of cutting-edge technologies has opened up unprecedented opportunities for innovation. One such remarkable innovation is the Sparkflows Generative-AI Platform, a game-changer that empowers developers and data scientists to create intelligent applications by harnessing the power of Large Language Models (LLM). In this blog post, we will delve into the features and potential of the Sparkflows Gen-AI Platform, exploring it