P R E D I C T L Y . C O

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Around 90% of machine learning models never make it to production only because it lacks the proper management of machine learning life cycle, lack of data and reproducibility. 

So how can you solve this?

Welcome to Predictly ML OPS!!! Predictly ML Ops Platform manages the complete life cycle of machine learning projects starting from understanding your business problem to deploying machine learning assisted solutions into production. Deploy your ML enabled application at ease with Predictly ML OPS.

Predictly ML OPS Platform

1
Problem Statement Understanding and Value Proposition

A 3W analysis to understand the problem statement and determining the ideal way of solving the problem with Machine learning and what will be the cost impact

2
Data Collection, Annotation and Versioning

1. Data Collection:

Finding out the right data sources for the problem statement and what will be the best storage for the collected data.

2. Annotation:

Building a high quality dataset is as important as building the right model. We will use our Predictly Data Annotation Platform to effectively annotate data in a much less time and cost.

3. Data Versioning:

To keep track of the additional data and unlabeled data we will keep versioning data using DVC tools.

3
Data Analysis and Experimentation

Run multiple experiments and data analysis techniques to understand the data more and prepare features. Figuring out the best metric, creating metadata are one of the tasks that will be curated in this phase.

4
Machine Learning Pipeline and Model Versioning

1.

Building Machine Learning Model

2.

Training Machine Learning Model

3.

Testing Machine Learning Model

4.

Model Versioning

5
Model Deployment and Serving

Predictly ML OPS provides various ways to deploy models and A/B testing according to the client’s requirements. Predictly ML OPS uses model serving to serve the model on cloud.

6
Model, Data and Application Monitoring.

Predictly ML OPS provides monitoring even after deploying the application. It constantly monitors the application and the deployed model to improve performance in real world production. It also runs a feedback loop for the data to make the Model smarter.

Advantages

  • Managed Infrastructure: A completely managed platform to deploy machine learning models on Cloud or on premise with minimum setup.
  • Accuracy: All deployed models went through rigorous testing to achieve best metric performance and make it work under real world conditions.
  • Optimized Workflow: An optimized flow of machine learning operations to achieve the best performance in minimum time and less turbulence.
  • Privacy: Predictly ML OPS committed to protect its user data and it’s intellectual property with utmost priority.

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