

Machine learning developers face an expanded set of management issues beyond merely getting the code right, including the testing and validation of data used in ML models while handling an additional set of infrastructure dependencies. After deployment, those models will degrade over time as use cases evolve.
In response to growing calls for standardization of machine learning operations, cloud and tool vendors are promoting new services aimed at making life a bit easier for data scientists and machine learning developers. Among them is Google Cloud, which this week dropped a batch of cloud AI tools that include data pipelines, metadata and a “prediction backend” for automating steps in the MLOps workflow.
“Creating an ML model is the easy part—operationalizing and managing the lifecycle of ML models, data and experiments is where it gets complicated,” Craig Wiley, director of product management for Google’s cloud AI platform, noted in a blog post unveiling the MLOps services.
The “MLOps foundation” is perhaps the most compelling of the cloud AI tools unveiled this week by the public cloud and AutoML vendor (NASDAQ: GOOGL).
For starters, Google said Tuesday (Sept. 1) it would release a managed service for machine learning pipelines in October. Introduced earlier this year, the service uses pre-built TensorFlow components and templates to develop ML pipelines—a feature the company asserts would reduce the time and labor required to deploy and manage models.
Google noted that machine learning has complicated traditional DevOps practices such as continuous integration and delivery. ML models also require constant training and monitoring; the former for retraining candidate models for testing and deployment; the latter for error detection along with monitoring inference data and tracking the performance of production models.
“Our goal is to make machine learning act more like computer science so that it becomes more efficient and faster to deploy,” Wiley said.
The expanded AI platform also includes a machine language “metadata management” service designed to help developers monitor MLOps workflows and “model lineage.” Google said its metadata service is scheduled to preview by the end of September.
As for machine learning applications, the AI platform package also includes development tools for conversational AI applications such as chatbots and interactive voice response (IVR) bots. In one contact center AI use case, IVR bots can hand off customer service calls to live agents.
The development suite dubbed DialogflowCX provides app developers with access to machine learning models for natural language processing and text-to-speech. It also connects to telecom and cloud platforms, including, of course, Google’s.
The AI platform package also addresses the proliferation of internal MLOps systems among hyper-scalers like LinkedIn and Uber. Data science vendors such as Cloudera and Anaconda have noted that managing machine learning models in production has proven difficult due to “technology sprawl” and the relatively short shelf-life of production ML models.
Hence, MLOps advocates are seeking to place continuous model training and monitoring on the same plane as open source software and application interfaces.
Others such as Algorithmia are also offering MLOps suites with controls and features designed to monitor machine learning models in production.
Recent items:
An Open Source Alternative to AWS SageMaker
Growing Focus on MLOps as AI Projects Stall
It’s Time for MLOps Standards, Cloudera Says
January 27, 2023
January 26, 2023
- Berkeley Lab Scientists Create ML Pipeline for Interpreting Large Tomography Datasets
- Cognizant and CoreLogic Extend Relationship with $1B, 10-Year Services Agreement
- Dremio Now Available as Part of AWS Marketplace Vendor Insights
- AtScale Expands Databricks Integration with Support for Databricks SQL and Availability in Partner Connect
- UT Austin Launches Online Master of Science in AI with edX
- Domino Data Lab Enhances Partner Program with New Offerings
- Matillion Highlights Major Company Growth and Product Evolution in 2022
January 25, 2023
- AWS Announces General Availability of Amazon OpenSearch Serverless
- Deci Delivers Breakthrough Inference Performance on Intel’s 4th Gen Sapphire Rapids CPU
- MSCI Partners with Google Cloud to Build a Secure Global Investment Data Platform in the Cloud
- GigaOm Report Reveals Advantages of Modern Approach to Data Access Control
- Appen Releases State of AI Automotive Report
- Moogsoft Announces Key Competitive Wins and 6 Quarters of Continued Growth
- IDC Survey Finds Data Sovereignty and Compliance Issues Shaping IT Decisions
January 24, 2023
- New Study: AI-inferred Personality Scores Predict Human Performance Equal to or Better Than Traditional Assessments
- Next Pathway Launches SHIFT Cloud to Improve Cloud Migrations
- Simon Data Launches Activate for Cloud Data Platforms
- Yugabyte Releases YugabyteDB Voyager to Accelerate Cloud Native Adoption
- Domino Data Lab Announces Partnership with TD SYNNEX
Most Read Features
- Are Databases Becoming Just Query Engines for Big Object Stores?
- The Drawbacks of ChatGPT for Production Conversational AI Systems
- Data Mesh Vs. Data Fabric: Understanding the Differences
- What’s Up with Cloud in 2023? Industry Predictions
- Hallucinations, Plagiarism, and ChatGPT
- Large Language Models in 2023: Worth the Hype?
- Big Things Ahead for AI in 2023: Predictions
- Big Data File Formats Demystified
- Microsoft Seeks $10B Investment in OpenAI: Report
- AI Is Coming for White-Collar Jobs, Too
- More Features…
Most Read News In Brief
- Microsoft Announces ChatGPT-powered Bing, Google CEO Declares ‘Code Red’
- Mathematica Helps Crack Zodiac Killer’s Code
- Confluent to Develop Apache Flink Offering with Acquisition of Immerok
- Observability Primed for a Breakout 2023: Prediction
- OpenAI’s New GPT-3.5 Chatbot Can Rhyme like Snoop Dogg
- Progress Announces Plans to Acquire MarkLogic
- UC Berkeley Launches SkyPilot to Help Navigate Soaring Cloud Costs
- Deloitte Tech Trends 2023 Shows Past IT Lessons Shaping the Future
- Data Prep Still Dominates Data Scientists’ Time, Survey Finds
- Big Growth Forecasted for Big Data
- More News In Brief…
Most Read This Just In
- Google Cloud and Deloitte Boost Grocery Associate Productivity and Improve Customer Experience for Kroger
- Databricks Partners with Crisp to Help Retailers Improve Supply Chain Visibility
- Oracle Announces Oracle Database 23c Beta
- AWS Announces General Availability of Amazon OpenSearch Serverless
- Google Cloud Unveils New AI Tools for Retailers
- New Research Looks at the Future of ChatGPT and Generative AI in the Enterprise
- McKinsey Acquires Iguazio, a Leader in AI and ML Tech
- Arcion Introduces SAP Sybase ASE Connector for Ultimate Migration Flexibility
- Mizuho Bank Selects Bloomberg’s BQuant Enterprise Solution to Enhance and Streamline Its Operations
- BigDATAwire (Formerly Datanami) Reveals Winners of 2022 Readers’ and Editors’ Choice Awards
- More This Just In…
Sponsored Partner Content
Sponsored Whitepapers
Contributors
Featured Events
-
AI Summit West
February 15 - February 16San Francisco CA United States -
Future of Mining Sydney 2023
February 20 @ 8:00 am - February 21 @ 5:00 pm -
CDAO Financial Services 2023
March 1 @ 8:00 am - March 2 @ 5:00 pm -
The Connected Worker Summit 2023
March 28 @ 8:00 am - March 30 @ 5:00 pm -
AI in Finance Summit NY
April 19 - April 20New York NY United States