MLOps Consulting
Maximize the potential of Machine Learning with MLOps consulting. Streamline machine learning pipelines, integrate cutting-edge ML Operations, and deploy AutoML platforms for optimal efficiency.
Business benefits
What is MLOps?
- MLOps
- Why choose Addepto?
MLOps stands for Machine Learning Operations. This is the DevOps approach used for ML-based applications.
MLOps, short for Machine Learning Operations, follows the DevOps approach, specifically tailored for ML-based applications. Its primary function is to enhance and optimize the process of integrating machine learning models into production, along with their ongoing maintenance and monitoring.
By leveraging MLOps, businesses can accelerate data science development and implement high-quality ML models up to 80% faster. The transformative potential of AI and machine learning in reshaping business operations is significant. However, to fully harness these benefits, organizations must fundamentally restructure their frameworks, cultures, and governance to support AI.
Why choose Addepto for MLOps implementation?
- At Datable, a dynamically growing company specializing in AI-based solutions, we pride ourselves on our extensive experience in MLOps consulting across various industries. Our team of experts is equipped with market-proven skills, backed by a robust portfolio of successful MLOps projects.
- We prioritize your business needs above all else, ensuring that we deliver a dependable, tailor-made solution designed to meet your specific requirements within the agreed timeline. With Datable, you can trust that we are committed to providing you with the most reliable and efficient MLOps services.
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 a 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.
Development process
MLOps implementation process
Development process
Our process
Technologies
Technologies that we use
- Frameworks
- Software
- Platforms
- Library
MLflow – MLflow is an open-source platform to manage the complete machine learning lifecycle, including experimentation, reproducibility, deployment, and a central model registry.
Kedro – Kedro is an open-source Python framework for creating reusable, maintainable, and modular data science code.
Apache Airflow – Apache Airflow is an open-source tool to programmatically create, schedule, and monitor workflows, used by Data Engineers for orchestrating workflows or pipelines. It enables them can easily visualize their data pipelines' dependencies, progresses, code, tasks, and success status.
Apache Spark – Apache Spark is a data processing framework that can quickly perform tasks on large data sets. It can work alone but also distribute data processing across multiple computers.
Amazon Sagemaker – Amazon SageMaker is a machine learning service that enables data scientists and developers to speed up building and training machine learning models and directly deploy them into a production-ready hosted environment.
Kubeflow – Kubeflow is the open source machine learning toolkit on top of Kubernetes. It provides the cloud-native interface for your ML libraries, frameworks, pipelines and notebooks, interpreting stages in the created data science workflow into Kubernetes steps.
AutoKeras – AutoKeras is an open-source python package written in the deep learning library Keras. AutoKeras uses a variant of ENAS, an efficient and most recent version of Neural Architecture Search.
Key benefits
Why should your company invest in MLOps consulting?
Glossary
All you need to know about MLOps consulting
What are the main principles of MLOps?
- MLOps entails a set of methods and practices that foster collaboration between data specialists and operational specialists.
- These practices optimize the machine learning lifecycle from start to finish, serving as a bridge between design, model development, and operation.
- Adopting MLOps improves the quality, automates management processes, and optimizes 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 updates of multiple pipelines, scalability, and effective management of machine learning models.
- MLOps enables easy deployment of high-precision models and lowers the cost of error repairs.
- Growing trust and receiving valuable insights are also among the advantages.
What is the MLOps process?
The MLOps process involves several stages:
- 1. Defining machine learning problems based on business goals.
- 2. Searching for suitable input data and ML models.
- 3. Data preparation and processing.
- 4. Training the machine learning model.
- 5. Building and automating ML pipelines.
- 6. Deploying models in a production system.
- 7. Monitoring and maintaining machine learning models.
What is the MLOps process?
- MLOps is necessary to optimize the process of maturing AI and ML projects within a company.
- With the advancement of the machine learning market, effectively managing the entire ML lifecycle has become extremely valuable.
- MLOps practices are required for various professionals, including data analysts, IT leaders, risk and compliance specialists, data engineers, and department managers.
What are MLOps open source tools?
- Numerous open-source tools are available for MLOps, such as MLflow, Kubeflow, ZenML, MLReef, Metaflow, and Kedro.
- These tools serve as full-fledged machine learning platforms for data research, deployment, and testing.
How is MLOps different from DevOps?
- In MLOps, in addition to code testing, data quality maintenance throughout the machine learning project lifecycle is essential.
- The machine learning pipeline encompasses data extraction, data processing, function construction, model training, model registry, and model deployment.
- MLOps introduces the concept of Continuous Learning (CT), focusing on the automatic identification of different scenarios.
- MLOps varies in team composition, testing, automatic deployment, monitoring, and more compared to DevOps.
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We are proud to be among the top BI & Big Data Consultants in Los Angeles on Clutch
We are proud to be among the top BI & Big Data Consultants in Los Angeles on Clutch
We are proud to be among the top BI & Big Data Consultants in Los Angeles on Clutch
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