Build, Deploy, Scale and Manage AI @ Mosaic AI
Optimize end-to-end life cycle of Machine learning applications by simplifying MLOPs for both Data Scientists and Software/DevOps Engineer
Mosaic AI is a self-served, unified data analytics platform that abstracts the operationalization of the AI lifecycle, behind the scene. All key stakeholders can use this platform to extract business value from the data. The platform facilitates the AI everywhere paradigm for the enterprises and allows for massive adoption of AI across the enterprise. This solution takes care of the deployment, scaling and versioning of AI models, making the data scientists focus on creating AI models without worrying about deployments.
This post intends to give an introduction to Mosaic AI on the following section:
- Introduction to Mosaic AI
- Features
- Limitations
Anyone working in the IT industry over a Machine Learning use case, being a Data Scientist, Business Analyst, the IT team responsible for governance and compliance, the business executives or the analytics leaders need to go through a list of complicated and extraneous tasks. These steps may include data preparation, data cleaning, feature engineering, choosing and building the model, setting up the learning environment, training, tuning and debugging the model, managing versions of the models, deploying the model, monitoring the performance of the model, validating the results and eventually scaling for the production environment. A collection of different tools are used at various stages to achieve these complex workflows and it is not so easy to have a seamless development experience from experimentation to live production.
Mosaic AI supports features from Ingesting data from various sources to deploying the model into production. And it does not end here, you get the Model monitoring , managing, re-deployment capabilities and the list continues.
Biasness in AI is a common phenomena which increases with the change in data, change in usage of data and many other factors. To overcome bias, you need to apply the aspect of fairness. Mosaic AI provides this feature to make sure we do not end up in a biased model for now and times to come.
AI/ML Governance is defined as the process as how any organization controls accesses, implements policies and tracks activities of ML models. Good Governance and Security leads to minimizing risks and maximizing the ROI
Ability to scale up and scale down is the need of the hour. Today, with the changing nature of business, scalable architecture is something, any enterprise has to opt for. Mosaic AI’s capabilities in terms of GPU support, resource allocation, Auto-scaling gives the organization an edge.
All the above explained features makes Mosaic AI one of the leading product in the AI/ML domain.
Let’s talk about certain limitations :
- Mosaic AI is in the process of continuous improvement with the addition of new features and is evolving from what it already is capable of. And in this process, sometimes few of the processes becomes challenging in terms of speed. The team is continuously working on to cater this.
- The user interface is an area which I feel needs a make over and I think the team may release a brilliant interface in the upcoming releases.
Conclusion:
Any organization who is looking for a full fledged AI/ML platform from Data Ingestion to the deployment in production can try this out. Just to summarize the capabilities:
- Pre-built Connectors
- Metadata Catalog
- Data Exploration and Understanding
- Assisted Data Wrangling
- Auto Insights
- Multiple Model Frameworks
- Personalized Workspaces
- Git Integration of Code
- Self-served Infrastructure Setup
- BYOC(Bring Your Own Container)
- Automated ML
- Model Deployment and Versioning
- Model Usage and Accuracy
- Model Monitoring and Explanations
