A machine learning framework is an interface that allows developers to build and deploy machine learning models faster and easier. A tool like this allows enterprises to scale their machine learning efforts securely while maintaining a healthy ML lifecycle.
Features of machine learning framework
Machine learning framework allows enterprises to deploy, manage, and scale their machine learning portfolio. Algorithmia is the fastest route to deployment, and makes it easy to securely govern machine learning operations with a healthy ML lifecycle.
With Algorithmia, you can connect your data and pre-trained models, deploy and serve as APIs, manage your models and monitor performance, and secure your machine learning portfolio as it scales.
A flexible machine learning framework connects to all necessary data sources in one secure, central location for reusable, repeatable, and collaborative model management.
- Manage source code by pushing models into production directly from the code repository
- Control data access by running models close to connectors and data sources for optimal security
- Deploy models from wherever they are with seamless infrastructure management
Machine learning models only achieve value once they reach production. Efficient deployment capabilities reduce the time it takes your organization to get a return on your ML investment.
- Deploy in any language and any format with flexible tooling capabilities.
- Serve models with a git push to a highly scalable API in seconds.
- Version models automatically with a framework that compares and updates models while maintaining a dependable version for calls.
Manage MLOps using access controls and governance features that secure and audit the machine learning models you have in production.
- Split machine learning workflows into reusable, independent parts and pipeline them together with a microservices architecture.
- Operate your ML portfolio from one, secure location to prevent work silos with a robust ML management system.
- Protect your models with access control.
- Usage reporting allows you to gain full visibility into server use, model consumption, and call details to control costs.
A properly scaled machine learning lifecycle scales on demand, operates at peak performance, and continuously delivers value from one MLOps center.
- Serverless scaling allows you to scale models on demand without latency concerns, providing CPU and GPU support .
- Reduce data security vulnerabilities by access controlling your model management system.
- Govern models and test model performance for speed, accuracy, and drift
- Multi-cloud flexibility provides the options to deploy on Algorithmia, the cloud, or on-prem to keep models near data sources.
Popular machine learning frameworks
Arguably, TensorFlow, PyTorch, and scikit-learn are the most popular ML frameworks. Still, choosing which framework to use will depend on the work you’re trying to perform. These frameworks are oriented towards mathematics and statistical modeling (machine learning) as opposed to neural network training (deep learning).
- TensorFlow and PyTorch are direct competitors because of their similarity. They both provide a rich set of linear algebra tools, and they can run regression analysis.
- Scikit-learn has been around a long time and would be most familiar to R programmers, but it comes with a big caveat: it is not built to run across a cluster.
- Spark ML is built for running on a cluster, since that is what Apache Spark is all about.
KDD Process Mode
Data Cleaning: Data cleaning is defined as removal of noisy and irrelevant data from collection.
- Cleaning in case of Missing values.
- Cleaning noisy data, where noise is a random or variance error.
- Cleaning with Data discrepancy detection and Data transformation tools.
Data Integration: Data integration is defined as heterogeneous data from multiple sources combined in a common source.
- Data integration using Data Migration tools.
- Data integration using Data Synchronization tools.
- Data integration using ETL(Extract-Load-Transformation) process.
Data Selection: Data selection is defined as the process where data relevant to the analysis is decided and retrieved from the data collection.
- Data selection using Neural network.
- Data selection using Decision Trees.
- Data selection using Naive bayes.
- Data selection using Clustering, Regression, etc.
Data Transformation: Data Transformation is defined as the process of transforming data into appropriate form required by mining procedure.
Data Transformation is a two step process:
- Data Mapping: Assigning elements from source base to destination to capture transformations.
- Code generation: Creation of the actual transformation program.
Data Mining: Data mining is defined as clever techniques that are applied to extract patterns potentially useful.
- Transforms task relevant data into patterns.
- Decides purpose of model using classification or characterization.
Pattern Evaluation: Pattern Evaluation is defined as identifying strictly increasing patterns representing knowledge based on given measures.
- Find interestingness score of each pattern.
- Uses summarization and Visualization to make data understandable by user.
Knowledge representation: Knowledge representation is defined as technique which utilizes visualization tools to represent data mining results.
- Generate reports.
- Generate tables.
- Generate discriminant rules, classification rules, characterization rules, etc.