MLOps Consulting
What is MLOps?
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Why should you invest in MLOps consulting and implementation?
The growing IT sector is experiencing high-end investments from businesses to enrich their business efficiency. The global market for MLOps will rise higher than ever by 2026, as per the research director of TechSci Research.
Why should you invest in MLOps consulting and implementation?
With MLOps, businesses improve their performance at every scale, gathering key insights into data training.
Stakeholders gain a common infrastructure in the life cycle with the help of MLOps consulting services.
MLOps generates efficient reports so that businesses can gain better insights for decision-making.
MLOps implementation process
1) Team integration
2) ETL step
3) Version control
4) Testing
5) Monitoring
Transform your business with the power of AI and Machine learning but ensure that organizations fundamentally reshape their structures, cultures, and governance frameworks that are a backbone to support AI, said Jeff Butler, director of research databases at the Internal Revenue Service.
Why choose Automation Factory for MLOps implementation?
Automation factory is a dynamic team of experts specialized in artificial intelligence. We have been serving businesses in MLOps consulting projects from various industries to maximize their business efficiency.
Our passionate team of experienced professionals loves challenges and delivering maximum results.
We believe in the model of prioritizing the client's requirements so that we can deliver reliable solutions on agreed deadlines.
Technologies that we use
MLOps Applications
FinTech Application: One of our client has arrived with an application that can prevent fraud and ensure safe transactions with MLOps
We have brilliantly utilized MLOps in the customer’s system where MLOps played an important role in securing user transactions and preventing fraud-related losses.
Sherlock system
This machine learning-based card fraud prevention system is used by our customer to monitor user transactions. Whenever Sherlock detects a suspicious transaction, it automatically cancels the transaction and blocks the card.
Immediately the user receives a notification in the customer app to confirm whether the transaction was fraudulent. You can easily unblock your card by confirming a secure transaction and continuing with your purchase.
On the other hand, if you do not recognize the transaction, the card will be terminated and users can order a free card replacement.
How customer is deploying models to production
Client conducted training for production using Google Cloud Composer. Models are cached in memory to keep latency low and deployed as a Flask application.
Additionally, Client app used an in-memory database dedicated to storing customer profiles called Couchbase.
The whole process can be described step by step:
1) After receiving a transaction via HTTP POST requests, Sherlock downloads the respective user and vendor profiles from Couchbase.
2) A feature vector is generated in order to produce training data and generate predictions.
3) The last step is sending JSON response directly to the processing backend – that’s where a corresponding action takes place.
Monitoring model’s performance in production
For monitoring their system in production, Customer used Google Cloud Stackdriver.
It shows data about operational performance in real-time.
If any issues arise, Google Cloud Stackdriver alerts team members by sending them emails and texts so that the fraud detection team can assess the threat and take appropriate action.
MLOps Applications
On-demand taxi-booking app: MLOps solution has enabled data-driven decision making in taxi-booking.
Our customer has build of the main taxi-booking and ride-sharing company services to their targeted audience.
Its services are accessible through the customer’s taxi-booking mobile application, which associates users to the closest drivers and restaurants.
Machine learning empower key capabilities, for example, assessing driver arrival time and deciding the ideal cost in light of user interest and driver supply.
Michelangelo platform
This platform is explicitly intended to empower Taxi-booking customer to make, send, and keep up with MLOps.
Michelangelo’s principal objective is to cover the all encompassing machine learning work process while supporting conventional models, for example, profound learning and time series modelling.
The platform model goes from development to creation in three stages:
1) Online forecast progressively.
2) Offline prediction in view of prepared models.
3) Embedded model deployment on smartphones.
In addition, the Michelangelo platform has valuable highlights to follow the information and model heredity, as well as to lead reviews.
Checking model’s performance underway
There are multiple ways our customer monitors endless models for a huge scope through Michelangelo.
One is the appropriation of conjectures and distributing of measurements capabilities over the long run to help committed frameworks or groups in deciding peculiarities.
The second is to record the model’s forecasts and investigate the experiences given by the data pipeline to decide if the model-produced expectations are right.
Another way is to utilize model execution measurements to assess the precision of the model.
Enormous scope data quality can be observed with the Data Quality Monitor (DQM). It naturally finds oddities in data collections and runs tests to raise an alarm on the stage liable for data quality issues.
Different models make our customer system effective:
Online forecasting mode is utilized for models that make real-time forecast.
Trained models are separated into various containers and run as cluster online predictive services.
This is crucial for our client’s services that require a consistent progression of data with a wide range of data sources, for example, driver-drive matching, and so on.
Disconnected prescient models are especially used to deal with inside business challenges where constant outcomes are not needed.
Models prepared and conveyed disconnected run group estimates while a repetitive timetable is accessible or upon client demand.
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