Influence The Food Industry By Leveraging Machine Learning In your App just like Uber

Anyone working in an organization must have noticed the success and convenience which comes along with the adoption of latest technology in the particular field of work.

Anyone working in an organization must have noticed the success and convenience which comes along with the adoption of latest technology in the particular field of work.

With implementation of technology the companies can make the success or could break the success it completely depends on its implementation and the way it is been done.

With the help of machine learning, Uber is making remarkable moves in the market.

To integrate Machine Learning in a correct manner, the company majorly focuses on three areas such as Use of Technology, Processes which are placed perfectly to manage the organizational structure.

Leveraging Machine Learning Technology at Uber

Uber just like facebook made revolutionary changes and the booming look of their journey by scaling Machine Learning and Artificial Intelligence infrastructure across all the business units.

Deployment of Machine Learning can even grow to hundreds of use cases with thousands of models which get deployed in the production as well as predictions which are made every second.

They have grown their ML staff by hiring hundreds of data scientists, engineers, product managers and even the researchers in the past 3 years.

Introduction of Michelangelo

The company was able to perform this through the advanced Machine Learning platform which is known as Michelangelo.

Michelangelo consists of the mix of open source systems and even the components used in it when open source solutions were not sufficient for all the use cases.

The primary open sourced components such as spark, Smaza and much more were used by the company.
The company prefers mature open source options where it appears possible and they contribute to all the open-source libraries when needed.

To communicate with Michelangelo, a special web UI and APIs gets provided. The teams deployed and working for the integration of Machine Learning also had timely interactions with Michelangelo during the overall workflow of Machine Learning.

Collecting all the open-source technologies and combining them with all the custom in-house tools cannot help you to provide a machine learning platform that can boost up your scale to 40 million for the active riders of the month.

Uber also ensures that they fetch the most crucial technological considerations in an accurate manner which are the end-to-end machine workflow, treating machine learning as software engineering, model development velocity and maintaining the modular and tiered architecture.

The Uber team found the same general workflow prevails across most of the ML use cases regardless of its classification, regression, time series forecasting.

They have also made several designs for their standard workflows which are to be implemented to permit the ease of expanding the support for new algorithms and even frameworks with the capability to perform better in both the modes whether it is online or offline.

The standardised workflow can be explained in 6 simple steps:

1. Manage Data

Providing standard tools for building data pipelines to give rise to all the features and label the data sets for training and for making predictions.

Such tools have deep integration with the Uber data lakes, warehouses and the online data of the company and its sebring systems.

Machine learning team found that collecting good features is also considered as the toughest part of ML as managing and building the data pipelines can be considered as one of the most costliest pieces to completely provide the machine learning solution.

2. Train models

The distributed chain in anything defines the tasks and roles of the individuals and the distributed model system with the front-end API can scale up the small datasets upto the large data sets with the billion of samples.

For each and every model which gets trained, Michelangelo has the unique model store which keeps up the attributes right from who trained the model, start and end time of the training, references to all the different training data sets, model accuracy metrics and even the choices that are learned of the model and even the statistics of the model visualization.

Michelangelo engineering team can merely add new algorithms for making responses to the customers requirements and allows the customer teams to connect their own models and various code for flexibility.

3. Evaluate Models

API and Web UI can be made available through visualizations along with model accuracy and feature reports which can assist all the data scientists by making an inspection of all the details of the individual model and even compare one or even more models against the other model.

4. Deploy Models

Michelangelo also has end-to-end support for the management model deployment and the UI or API which supports all the three models and these 3 models are: Offline, Online and library.

5. Making certain predictions

ALl the various services make API based interferences against all the online and offline models to get the predictions which are based on the featured data which gets loaded from the data pipeline or from the client service directly.

6. Monitor Predictions

All the uses and their ongoing live measurements of the model accuracy while making monitoring predictions. This ensures Uber’s data pipelines and even it keeps on continuing to send the most accurate data and all the production environments that have not been changed to all the points of the models which are no longer even accurate.

ML as Software Engineering

The most crucial principle of the Michelangelo Team made the adoption and thought of machine learning as software engineering.

This also means that running a ML platform with all the same iterative,rigorous,tested and methodological processes which are used in software engineering.

The Last Sentence

All the models have their own roles to play to complete the workflow in a standardized manner.

The company needs to channelize the need of adopting ML in its functioning and how it will solve the technical queries of the clients and customers.

Creation of Michelangelo is the example of the perfect adoption of machine learning which has been integrated by Uber.

Author’s Bio
James Vargas is an experienced business expert, startup business consultant, assistance in trademark registration, Food Delivery App Development and marketing head at Get Everything Delivered. With the 1.5-decade corporate experience, he is now sharing his guidance to start-ups to grow with corporate team building activities and project delivery solution

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