Achieving MLOps Excellence with Google Cloud and Equinix Collaboration
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In recent years, machine learning (ML) has gained tremendous popularity as a powerful tool for solving complex problems across various domains. However, building and deploying ML models at scale can be challenging, as it involves a range of tasks such as data preparation, feature engineering, model training, deployment, monitoring, maintenance and so on.
According to “The Art of AI maturity” report published by Accenture “87% of data science projects never make it into production.” This is where MLOps comes in – it can help to address the core challenges by providing a framework for managing the entire ML lifecycle, from data collection and preparation to model development, testing, and deployment. It also reduces the time from ML model development to production and increases the success rate of ML projects.
In Google Cloud, we understand how important MLOps is to successfully productionize ML models. So we collaborated with Equinix, the world’s digital infrastructure company™ and a leader in global colocation data center market share, with 248 data centers in 27 countries on five continents. We helped them by providing the advisory services on the MLOps best practices and architecture.
Let’s take a sneak peek at the MLOps requirements at Equinix and the final architecture that was proposed.
What does MLOps mean for Equinix?
After multiple discovery sessions with the Equinix Team, we identified the core requirements and pain points to address in their new MLOps architecture:
- Reusability: Components such as features and pipeline components should be reused across projects to reduce costs and improve efficiency.
- Foundations: The foundations of the infrastructure, such as environments, folder structure, and project hierarchy, should be well-designed to support scalability and reliability.
- Early identification of problems: Problems should be identified early by including data validation, notifications, and retry mechanisms.
- Cost optimization: Costs should be optimized by paying only for what is used.
- Enterprise CI/CD requirements: Enterprise CI/CD requirements should be met by integrating with GitHub and GitActions.
- Scaling: The infrastructure must scale to support future growth.
- Security: Enterprise security requirements should be met in terms of IAM roles, network, etc.
MLOps Architecture
Based on the above requirements from Equinix, key design considerations were made for example – using Vertex AI Feature Store instead of Big Query for online feature serving, using DataFlow for pre-processing vs using the existing python based pre-processing and so on. After carefully assessing all the alternatives, the below reference architecture for MLOps in GCP was proposed:
Reference architecture for MLOPs using GCP is illustrated in Figure 1. This architecture includes the following pipeline stages:
- Vertex AI Workbench enables data scientists to quickly explore new ideas and develop/experiment new models. The source code is saved in GitHub repository
- Github Actions with self-hosted runners are integrated with Github as the source code repository. This enables continuous integration of code, quality and security scans. The artifacts generated during the process are saved in Artifact Registry and Google Cloud Storage. These artifacts are deployed to implement the pipeline.
- Unit tests and integration tests can be performed during the continuous integration in Github Actions with self-hosted runners. End-to-end tests are performed on demand in the continuous integration pipeline.
- Metadata about the artifacts is generated and saved in Vertex ML Metadata.
- Automated triggers can be enabled to run pipelines. For example, one trigger is the availability of new training data. These triggers can run the model training pipeline and the new trained model can be pushed to Vertex AI Model Registry.
- To train a new ML model with new data, the deployed Vertex AI Pipeline is executed.
- To train a new ML model with new implementation, a new pipeline will be deployed through CI/CD pipeline.
“The proposed architecture design covers the requirements and scenarios that we were looking for. As our AI and ML portfolio is growing in scale and complexity, it’s important to follow a clear and up-to-date architecture if we want to keep increasing the value delivered by our solutions. As part of the process, the team also acquired the skills required to fully implement it” according to Bernardo Fernandes, Data Science Senior Manager at Equinix.
Below is the snapshot of the features before and after MLOps implementation at Equinix:
As businesses increasingly rely on machine learning to gain a competitive edge, MLOps has become a critical component of their strategy. By adopting MLOps practices, organizations can achieve faster time-to-market, better performance, and higher ROI for their machine learning initiatives.
Fast track end-to-end deployment with Google Cloud AI Services (AIS)
The partnership between Google Cloud and Equinix is just one of the latest examples of how we’re providing AI-powered solutions to solve complex problems to help organizations drive the desired outcomes. To learn more about Google Cloud’s AI services, visit our AI & ML Products page.
We’d like to give special thanks to Nitin Aggarwal, Vijay Surampudi, Parag Mhatre and Anantha Narayanan Krishnamurthy for their support and guidance throughout the project. We are also grateful to the super awesome collaboration with the Equinix Team (Ravi Pasula and Brendan Coffey, Bernardo Fernandes, Łukasz Murawski, Jakub Michałowski, Sonia Przygocka-Groszyk, Marek Opechowski, Daria Bondara, Nila Velu, Vijay Narayanan, Dharmendra Kumar, Shailesh Sukare, Arunraj Kumar Raje, Seng Cheong Lee).
ML Workflow Made Simple: How to Automate ML Experiment Tracking with Vertex AI Experiments Autologging
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Practical machine learning (ML) is a trial and error process. ML practitioners compare different performance metrics by running ML experiments till you find the best model with a given set of parameters. Because of the experimental nature of ML, there are many reasons for tracking ML experiments and making them reproducible including debugging and compliance.
But tracking experiments is challenging: you need to organize experiments so that other team members can quickly understand, reproduce and compare them. That adds overhead that you don’t need.
We are happy to announce Vertex AI Experiments autologging, a solution which provides automated experiment tracking for your models, which streamlines your ML experimentation
With Vertex AI Experiments autologging, you can now log parameters, performance metrics and lineage artifacts by adding one line of code to your training script without needing to explicitly call any other logging methods.
How to use Vertex AI autologging
As a data scientist or ML practitioner, you conduct your experiment in a notebook environment such as Colab or Vertex AI Workbench. To enable Vertex AI Experiments autologging, you call aiplatform.autolog() in your Vertex AI Experiment session. After that call, any parameters, metrics and artifacts associated with model training are automatically logged and then accessible within the Vertex AI Experiment console.
Here’s how to enable autologging in your training session with a Scikit-learn model.
# Enable autologging
aiplatform.autolog()
# Build training pipeline
ml_pipeline = Pipeline(...)
# Train model
ml_pipeline.fit(x_train, y_train)This video shows parameters and training/post-training metrics in the Vertex AI Experiment console.
Vertex AI SDK autologging uses MLFlow’s autologging in its implementation and it supports several frameworks including XGBoost, Keras and Pytorch Lighting. See documentation for all supported frameworks.
Vertex AI Experiments autologging automatically logs model time series metrics when you train models along multiple epochs. That’s because of the integration between Vertex AI Experiments autologging and Vertex AI Tensorboard.
Furthermore, you can adapt Vertex AI Experiments autologging to your needs. For example, let’s say your team has a specific experiment naming convention. By default, Vertex AI Experiments autologging automatically creates Experiment Runs for you without requiring you to call `aiplatform.start_run()` or `aiplatform.end_run()`. If you’d like to specify your own Experiment Run names for autologging, you can manually initialize a specific run within the experiment using aiplatform.start_run() and aiplatform.end_run() after autologging has been enabled.
What’s next
You can access Vertex AI Experiments autologging with the latest version of Vertex AI SDK for Python. To learn more, check out these resources :
- Documentation: Autolog data to an experiment run
- Github: Get started with Vertex AI Experiments autologging
While I’m thinking about the next blog post, let me know if there is Vertex AI content you’d like to see on Linkedin or Twitter.
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How Google’s Customer Data Platform Helps Retail Brands Offer Data-driven , Personalized CX
Retail companies need customer insights to deliver personalized experiences that impact revenue generation and cost savings. Watch how Google Cloud’s customer data platform helps brands integrate and build holistic view of data in silos to drive marketing and customer service success.
Everything You Want to Know About Google Cloud’s AI-Enabled Talent Solution: From What It Is to How to Use it
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First, What is Google Cloud Talent Solution?
Cloud Talent Solution is a service that brings machine learning to the job search experience, returning high quality results to job seekers far beyond the limitations of typical keyword-based methods. Once integrated with your job content, Cloud Talent Solution automatically detects and infers various kinds of data, such as related titles, seniority, and industry.
Show Me How it Works
Try it online now.
Show Me an Example of Who’s Using It
There’s a number of enterprises leveraging this service. Here are a few easy-to-watch examples
Watch how FedEx Ground Employs Google Cloud Talent Solution
Read how Johnson & Johnson is Reimagining Recruiting with Jibe and Google
How Much Does it Cost?
Ok, Let’s See How it Works
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AutoML Vision: Among the Fastest and Easiest Way to Adopt AI for Your Enterprise
What’s among the largest impediment to the adoption of AI within enterprises? Not enough access to skills. According to 80 percent of business respondents to an EY survey, the top challenge to an enterprise AI program is the lack of requisite talent.
What companies need today is a way to facilitate—and therefore accelerate—the adoption of AI. There are a few challenges that need to be overcome. Two of the most critical challenges include the data science skills required to create customized models, and the right IT skills to power the underlying infrastructure.
Both of these skills are hard and expensive to come by.
There are ways around this problem. Google Cloud’s recently launched AutoML Vision is one such solution. It significantly lowers the amount of IT and data science heavy lifting required to start customized machine learning applications around computer vision.
That’s possibly one of the reasons why Google is the most popular cloud provider for data scientists, according to the State of Data Science.
In this short and simple-to-understand video, Yufeng Guo, Developer and Machine Learning Advocate at Google Cloud, walks you through a real use case of AutoML Vision.
What Are India’s Biggest Companies Doing on Google Cloud?
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In the last year, there’s been an upward trend in cloud adoption in India. In fact, NASSCOM finds that cloud spending in India is estimated to grow at 30% per annum to cross the US$7 billion mark by 2022.
At Google, in our conversations with customers, discussions have evolved beyond cost savings and efficiencies. While those are still very relevant reasons for adopting cloud technologies, Indian enterprises are looking to Google Cloud to help them drive digital transformation, identify new revenue generating business models, reach previously untapped consumer markets, and build customer loyalty through greater insight and personalization.
Here are some companies and their stories.
Tata Steel: Mining data and maximizing its power
Tata Steel is a great example of an established enterprise from a traditional industry that is modernizing and embracing cloud computing. With an ambition to be a leader in manufacturing in India and a digital-first organization by 2022, Tata Steel believes smart analytics is key to enhancing operational efficiency and gaining business advantage.
To organize data from siloed systems across the organization and make it easily accessible to all employees, Tata Steel is using Cloud Search and plans to scale it to more than one million documents and 28 disparate enterprise content sources including enterprise resource planning (ERP) and SharePoint. In fact, Tata Steel is one of the first Indian enterprises to harness the power of Cloud Search to meet some of the most aggressive ingestion demands, with indexing durations reduced from weeks to seconds.
They are also leveraging Google Cloud Platform (GCP) services like Google Cloud Storage and BigQuery to build their data lake and enterprise data warehouse so they can take advantage of advanced analytics and machine learning. Managed services such as AI Platform further enable Tata Steel to manage end-to-end AI/ML workflows within the GCP console. This complements their existing on-premise reporting and analytics tools, and brings data management to the forefront of everything they do—from forecasting market demand to predictive equipment maintenance.
“Digital is not just a goal, it’s become a way of life. We are digitizing everything from the deployment of factory vehicles to improving material throughput to marketing and sales. As a result, we have petabytes of structured and unstructured data that is not only waiting to be mined, but that we can generate intelligence from to create opportunities across our multiple lines of business using GCP,” said Sarajit Jha, Chief Business Transformation & Digital Solutions at Tata Steel.
Helping L&T Financial Services reach customers in rural communities
In rural communities, quick access to financial services can make a tremendous difference to livelihoods. L&T Financial Services provides farm-equipment finance, micro loans and two-wheeler finance to consumers across rural India backed by a strong digital and analytics platform. Their digital-loan approval app, which runs on GCP, makes it significantly faster and easier for people to apply for financial assistance to purchase important things such as farming equipment and two-wheelers. It also helps rural women entrepreneurs get quicker access to funds for their businesses through micro loans.
L&T Financial found G Suite to be a far better collaborative tool to help staff work together efficiently. Employees can interact with each other in real time using Hangouts Meet, and the task of information sharing is more seamless and secure through Drive. BigQuery also helps L&T Financial Services generate behavior scorecards to track credit quality of its micro-loan customers.
“Cloud is the technology that enables us to achieve scale and reach. Today there are countless data points available about rural consumers which enable us to personalize our products to serve them better. With access to faster compute power, we can also on-board consumers more efficiently. Our rural businesses have clocked a disbursement CAGR of 60% over the past three years.” said Sunil Prabhune, Chief Executive-Rural Finance, and Group Head-Digital, IT and Analytics, L&T Financial Services.
Creating conversational connections for Digitate’s customers
Digitate, a venture of TCS (Tata Consultancy Services), has integrated Dialogflow into its flagship brand ignio, an award-winning artificial intelligence platform for driving IT operations, workload operations and ERP operations for diverse enterprises. This integration is the next step in ignio’s product development journey, and will enable users to chat or talk with ignio to detect issues, triage problems, resolve them and even predict system behavior.
“ignio combines its unique self-healing AIOps capabilities for enterprise IT and business operations with Dialogflow’s AI/ML-based, easy to use, natural and rich conversational capabilities to create an unparalleled, intuitive and feature-rich experience for our customers,” says Akhilesh Tripathi, Head of Digitate.
Indian enterprises going G Suite
The base of Indian enterprises that are making the switch to G Suite to streamline their productivity and collaboration also continues to grow. Sharechat, BookMyShow, Hero MotorCorp, DB Corp and Royal Enfield are now able to move faster within their organizations, using intelligent, cloud-based apps to transform the way they work.
A hybrid and multi-cloud future in India
IDC predicts that by 2023, 55% of India 500 organizations will have a multi-cloud management strategy that includes integrated tools across public and private clouds. (IDC FutureScape: Worldwide Cloud 2019 Predictions — India Implications (# AP43922319). We look forward to sharing more success stories of Indian enterprises that have taken the next step in their digital transformation journey.
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