Lityx Enables MLOps Best Practices

Easily Deploy, Manage, and Govern Your Machine Learning Models Flawlessly Every Time

Lityx understands the considerable investment in time and money needed to create a successful analytics program. As practicing Data Scientists, we know that many good ideas never reach deployment, leaving unrealized ROI on the table. That’s why adherence to MLOps best practices is so important, and it is why LityxIQ helps enable a robust MLOps process for your organization.

The LityxIQ platform supports an effective MLOps approach through our intuitive platform design that promotes collaborative model deployment and management across analytics teams. The platform enables effective MLOps best practices in key areas including:

  • Robust Model Deployment Capabilities: Includes robust scoring and authority to deploy regulation features
  • Model Management, Monitoring and Lifecycle: Organized model libraries, version tracking, model performance reporting
  • Model Governance and Security: Highly secure server environment and platform transparency

How Lityx Supports MLOps

You will notice that Model Development is not included, as MLOps is about everything else that is required to support and ensure value around the creation of machine learning models. MLOps is critical to the success of machine learning models. Like so many things, there is more to making use of a technology than first meets the eye. There are all the “what-abouts.” What about this, what about that, etc. MLOps covers all the things that need consideration. For example, it is all well and good to create a machine learning model. That is fun and exciting, but there is no value until you have implemented it into a production process and it works as intended and continues to work as intended. We have all experienced coming up with a great idea that upon further consideration cannot practically be implemented. Here are some examples of how LityxIQ facilitates MLOps:

Step 1 Step 2 Step 3

Approvals & Implementation

Use the drop down selected model menu to access the authority to deploy options.


Set up your model approvers.

Production Version

Request approval to move your model to production status.

Model Deployment: Authority to Deploy
Step 1 Step 2 Step 3

Create Scoring Catalog

Create a dataset in LityxIQ where model scores and segments will reside.

Create & Run Scoring Job

Set up a scoring job that will automatically perform model transformation and binning to apply your chosen model and append scores and segments based on the grouping method you choose, i.e. deciles.

Export Your Model Scores

Easily and automatically export your model scores and segments to a file or table across a wide variety of options, including your network, ftp server, sql server, Amazon, Google, HubSpot, Microsoft, Redshift, and Snowflake to name a few.

Model Deployment: Scoring Process
Step 1 Step 2 Step 3

Current and Production Versions

LityxIQ automatically updates the Current Version label with each update to the model. The second decimal indicates the number of unique variations created as part of an update such as when trying multiple algorithms or testing binning, autocorrelation options.

Historical Model Performance

Current and previous models including all iterations are always available for review of a wide variety of model build and performance information.

Setting Production Version

Located within Predict, Approvals, and Implementation you can select or remove which version of the model is assigned to Production.

Model Management: Version Tracking

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