MLOps Consulting

Automate machine learning pipelines, implement advanced Machine Learning Operations or AutoML platforms, and make sure you’re using ML models at scale.

We support companies in the field of consulting and implementation of MLOps solutions.

What is MLOps?

MLOps stands for Machine Learning Operations. This is the DevOps approach used for ML-based applications.

MLOps is the main function of machine learning design and it aims to improve and optimize the process of implementing machine learning models into production, as well as their maintenance and monitoring.

MLOps helps businesses develop data science and implement high-quality ML models 80% faster

Why should your company invest in MLOps consulting?

High-end investments by the market players and the growing IT sector is expected to propel the global MLOps Market growth till 2026

According to Mr. Karan Chechi, Research Director of TechSci Research

Data training icon

MLOps provides a better balance between different parts of the life cycle, from key business performance indicators to data training.

MLOps services icon

MLOps consulting services provide a common infrastructure for each stakeholder in the life cycle.

business insights in data engineering services

MLOps consulting allows you to generate the best ideas and insights for your company and implement them effectively.

MLOps implementation process

1) Team integration

One of the keys to a successful project is building a professional team. The number of machine learning engineers, data engineers, and DevOps engineers required is always determined by the complexity and requirements of the project.

2) ETL step

It is essential to extract data from all sources and create a pipeline that will ensure uninterrupted data extraction in the system.

3) Version control

You can change various parameters when running a model. And this, in turn, leads to different results. Therefore, version control allows you to revert to the previous parameter set if necessary.

4) Testing

Model testing involves checking for bad rates, accuracy, ROC, area under the curve, population stability index (PSI), characteristic stability index (CSI), etc.

5) Monitoring

An integral step in MLOps consulting projects – periodic monitoring of machine learning model performance.

AI and machine learning can transform the way business is done, but only if organizations can fundamentally reshape organization structures, cultures, and governance frameworks to support AI

According to Jeff Butler, director of research databases at the Internal Revenue Service

Why choose Addepto for MLOps implementation?

Addepto is a dynamically growing company specializing in solutions based on artificial intelligence. We have experience in working on MLOps consulting projects for companies from various industries.

Some of our successful collaborations you can find on our profile on Clutch.

The Addepto team consists of passionate professionals who are always open to new challenges.

reliable solution

We put your business needs first and we make every effort to provide your company with a reliable solution within the agreed deadline.

Technologies that we use

MLOps Applications

Revolut: Preventing fraud and ensuring safe transactions with MLOps

Revolut is a well-known British company offering banking services to clients from various countries. MLOps plays 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 Revolut 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 Revolut 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 Revolut is deploying models to production

Revolut conducted training for production using Google Cloud Composer. Models are cached in memory to keep latency low and deployed as a Flask application.

Additionally, Revolut 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, Revolt 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.

Uber: Data-driven decision making with MLOps solutions

Uber is the largest ride-sharing company worldwide.

Its services are available through the Uber mobile app, which connects users to the nearest drivers and restaurants.

Machine learning operations enable key functions such as estimating driver arrival time and determining the optimal toll based on user demand and driver supply.

Michelangelo platform

This platform is specifically designed to enable Uber teams to create, deploy, and maintain MLOps.

Michelangelo’s main goal is to cover the holistic machine learning workflow while supporting traditional models such as deep learning and time series forecasting.

The platform model goes from development to production in three steps:

1) Online forecasts in real-time.
2) Offline predictions based on trained models.
3) Embedded model deployment on mobile phones.

Moreover, the Michelangelo platform has useful features to track the data and model lineage, as well as to conduct audits.

How Uber is deploying models to production

The Uber model is being successfully transitioned from development to production via the Michelangelo platform thanks to Embedded Model Deployment and Online & Offline Predictions.

Online forecasting mode is used for models that make real-time forecasts.

Trained models are divided into multiple containers and run as clustered online predictive services.

This is crucial for Uber services that require a continuous flow of data with many different inputs, such as driver-drive pairing, etc.

Offline predictive models are particularly used to handle internal business challenges where real-time results are not required.

Models trained and deployed offline run batch forecasts when a recurring schedule is available or upon customer request.

 

If you want to know more about MLOps consulting solutions like this, please contact our experts.

Monitoring model’s performance in production

There are several ways Uber monitors countless models on a large scale through Michelangelo.

One is the distribution of forecasts and publishing of metrics functions over time to assist dedicated systems or teams in determining anomalies.

The second is to record the model’s predictions and analyze the insights provided by the data pipeline to determine whether the model-generated predictions are correct.

Another way is to use model performance metrics to evaluate the accuracy of the model.

Large-scale data quality can be monitored with the Data Quality Monitor (DQM). It automatically finds anomalies in data sets and runs tests to raise an alert on the platform responsible for data quality issues.

 

DoorDash: Optimizing the experience of dashers, merchants, and consumers with MLOps

DoorDash enables local businesses to offer deliveries by linking them with consumers seeking delivery and dashers who are delivery personnel.

This company implements MLOps solutions in order to optimize the experience of dashers, merchants, and consumers. Machine learning technology plays the biggest role within DoorDash’s internal logistics engine.

ML models enable running forecasts and based on them, determining the necessary supply of dashers while observing the demand in real-time.

Moreover, machine learning helps with estimating the time of delivery, dynamic pricing, offering recommendations to clients and search ranking of the best merchants available for DoorDash.

How DoorDash is deploying models to production

The DoorDash team develops machine learning models to meet their production or research needs. They often use open source machine learning platforms such as PyTorch (tree-based models) and LightGBM (neural network models).

DoorDash uses the ML wrapper in the training pipeline. The metadata and files are then added to the model store, waiting to be loaded with the microservice architecture.

Sibyl, a specially designed prediction service, is responsible for providing output data to various use cases. The model service enables them to load models and cache them in memory.

When there is a forecast request, the platform tests to see if any features are missing, and if so, delivers them from the feature store. Predictions can be made available in a variety of ways, in real-time, in shadow mode, or asynchronously.

Responses obtained by forecasts are sent back to the user as a protobuf object in gRPC format. Additionally, forecasts are logged in the Snowflake datastore.

Monitoring model’s performance in production

The monitoring service used by the company tracks the forecasts provided by Sybilla to monitor model metrics. Additionally, the service analyzes feature distribution to monitor data drift as well as a log of all forecasts generated by the service.

To collect and aggregate monitoring statistics, as well as generate metrics that need to be watched, DoorDash uses the Prometheus monitoring platform.

To visualize this data in graphs and charts, the company uses Grafany.

Let’s discuss Machine Learning Operations for your business

FAQ

What are the main principles of MLOps?

MLOps is a set of methods and practices for collaboration between data specialists and operational specialists. These practices are needed to optimize the machine learning lifecycle from start to finish. They serve as a bridge between the stages of design, model development and operation.

Adopting MLOps helps improve the quality, automate the management process, and optimize the implementation of machine learning and deep learning models in large-scale production systems.

What are the benefits of MLOps?

The main benefits of MLOps include automatic update of multiple pipelines, scalability and management of machine learning models, easy deployment of high-precision models, lower cost of repairing errors, and growing trust and the opportunity to receive valuable insights.

What is the MLOps process?

The MLOps process is as follows:

Defining machine learning problems based on business goals.
Searching for suitable input data and ML models.
Data preparation and processing.
Training machine learning model.
Building and automating ML pipelines.
Deploying models in a production system.
Monitoring and maintaining machine learning models.

Who needs MLOps?

The usefulness of MLOps models comes from the fact that they are necessary to optimize the process of maturing AI and ML projects in the company. With the development of the machine learning market, it has become extremely valuable to effectively manage the entire life cycle of machine learning.

As a result, MLOps practices are required for many professionals, including: data analysts, IT leaders, risk and compliance specialists, data engineers, and department managers.

What are MLOps open source tools?

There are many open-source tools to choose from. MLflow, Kubeflow, ZenML, MLReef, Metaflow, and Kedro are among the best full-fledged machine learning platforms for data research, deployment, and testing.

How is MLOps different from DevOps?

In MLOps, in addition to code testing, it is also important to ensure that data quality is maintained throughout the machine learning project life cycle.

In MLOps, the machine learning pipeline may be needed to implement a machine learning system that includes data extraction, data processing, function construction, model training, model registry, and model deployment.

Continuous Learning (CT) is the third MLOps concept that DevOps does not have. This concept focuses on the automatic identification of different scenarios.

In addition, MLOps vary in team composition, testing, automatic deployment, monitoring, and so on.

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