MLOps+Innotescus: How to find AI success where 80% of businesses fail
Machine Learning Operations (MLOps) is rapidly gaining new momentum amongst Data Scientists, ML Engineers and AI enthusiasts. While MLOps is supposed to improve AI applications, only 22% of businesses succeed in incorporating MLOps into their pipelines. Our platform is part of the solution; Innotescus’ enhanced features for Data Ingestion, Data Analysis & Curation, Data Labeling, and Data Validation solve key challenges keeping businesses from incorporating MLOps into their workflow.
Defining MLOps
A shorthand for machine learning operations, ML Ops is a set of best practices for businesses to run AI successfully.
Before data became abundant, managing small amounts of information and the few ML algorithms that used them was easily achieved. Now, as information becomes an inexhaustible resource and models become more complex, MLOps is proving to be the way forward.
MLOps is an overlap of three areas: DevOps, Data Engineering, and Machine Learning.
- Data Engineering—getting and preparing data
- DevOps—the infrastructure required to bring a model to production
- Machine Learning—creating models that can be trained to make complex decisions
The combination of these approaches has created a new discipline capable of achieving complex business goals through the application of machine learning.
How Innotescus improves the MLOps Cycle
Data Ingestion
After data is collected, it has to be prepared for use later in the ML pipeline. With Innotescus, you can easily import data from an array of sources—local storage, cloud services, even generic URLs—and maintain full visibility over each item’s source throughout the development cycle. For even greater control and integration, the Innotescus API offers users the ability to automate all of these steps, whether for a single upload or for the ingestion of a continuous stream of data.
Data Analysis & Curation
In the black box of Machine Learning, cause-and-effect is not easy to observe.. Innotescus solves this problem by providing a host of metrics that are calculated on each image and annotation from the moment they enter the platform.
Data Labeling
Both the most vital and least glamorous part of any machine learning model, data labeling is not a step in the process to overlook. Necessary for computer vision, natural language processing, and speech recognition, the goal of data labeling is to provide the structured information necessary for training a model, including any metadata that may prove useful.
Data Validation
After annotation, data must be inspected to ensure that it is usable and correct, not just for model use, but for repeated use in profiling and analysis, validation, testing and more. The cycle of MLOps isn’t called a cycle for nothing.
