It’s not easy to identify a picture of a cat. Don’t be fooled by the seemingly simple premise of accurately identifying a specific item (e.g., a cat) in a random set of images. Until recently, this task stumped even the most sophisticated algorithms. A large image dataset is required to train a convolutional neural networks (CNNs) which is used to learn and then identify items in the real world.
Today, machine learning engines with sophisticated AI libraries working on massive datasets can make such feats not only possible but relatively straightforward. The HPE Machine Learning Development Environment (MLDE) offers a full AI development suite to make the model training and storing process easy.
The benefits of the Cloudian / HPE MLDE integration are:
- Faster Training: HPE MLDE can significantly speed up the training process for your machine learning models. This allows data scientists and ML engineers to iterate more quickly and get better results.
- Reduced Complexity: HPE MLDE simplifies the process of setting up, managing, and securing AI compute clusters. This frees up IT administrators from these tasks and allows them to focus on other priorities.
- Improved Collaboration: HPE MLDE includes features that make it easier for data science teams to collaborate on projects. These features include experiment tracking and simpler model reproducibility.
- Scalable Storage: Cloudian HyperStore scales to accommodate massive datasets required for training complex machine learning models. Directly integrating with MLDE allows data scientists to train their models on massive data sets thereby improving model accuracy.
- Framework Compatibility: The integration works with popular frameworks like TensorFlow, PyTorch, and Spark ML, optimizing performance for parallel training from object storage.
- Streamlined Data Pipelines: Acts as a central storage for various AI components like feature stores, model storage, and vector databases, enabling a single platform for data pipelines.
- Cost-Effectiveness: Cloudian offers a cost-effective solution for storing large volumes of data compared to traditional file systems often used in ML.
In an upcoming blog, we will showcase a working example of training a model to used to classify cat images with MLDE and Cloudian.
Amit Rawlani, Senior Director of Solutions & Technology Alliances, Cloudian