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Submitted URL: https://tecton.ai/
Effective URL: https://www.tecton.ai/
Submission Tags: analytics-framework
Submission: On April 25 via api from US — Scanned from DE
Effective URL: https://www.tecton.ai/
Submission Tags: analytics-framework
Submission: On April 25 via api from US — Scanned from DE
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<legend class="hs-field-desc" style="display: none;"></legend>
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Text Content
Tecton apply(risk): Join us for a free virtual event on data engineering for ML risk and fraud detection systems! Register now Dismiss mobile-menu * Product * Feature Platform * On-Demand Demo * What’s New? * FAQ * Tecton & Feast Comparison * On-Demand DemoDiscover how Tecton accelerates your path to real-time machine learning * Solutions * Customers * Documentation * Learn * Blog * Resources * apply() Conference * Feature Store * Community Slack * The Buyer’s Guide to Evaluating ML Feature Stores and PlatformsGet the eBook * Company * About us * Careers * Contact Us * Building vs. Buying a Feature StoreLearn what’s right for you * Request a Demo * Free Trial Request a Demo Free Trial apply(risk): Join us for a free virtual event on data engineering for ML risk and fraud detection systems! Register now Dismiss mobile-menu * Product * Feature Platform * On-Demand Demo * What’s New? * FAQ * Tecton & Feast Comparison * On-Demand DemoDiscover how Tecton accelerates your path to real-time machine learning * Solutions * Customers * Documentation * Learn * Blog * Resources * apply() Conference * Feature Store * Community Slack * The Buyer’s Guide to Evaluating ML Feature Stores and PlatformsGet the eBook * Company * About us * Careers * Contact Us * Building vs. Buying a Feature StoreLearn what’s right for you * Request a Demo * Free Trial Request a Demo Free Trial TRANSFORM. SERVE. SCALE. FEATURE PLATFORM FOR REAL-TIME MACHINE LEARNING Power all your models with production-ready batch, streaming, and real-time data pipelines UTM Campaign UTM Source UTM Medium UTM Content UTM Term All URL parameters Referrer URL Or contact sales From the creators of Uber Michelangelo, the ML Platform powering every model at Uber. Tecton is trusted by leading ML teams. * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * MORE THAN JUST A FEATURE STORE THE ALL-IN-ONE SYSTEM TO BUILD, AUTOMATE, AND CENTRALIZE FEATURE WORKFLOWS FOR PRODUCTION ML PRODUCTION PIPELINES IN MINUTES Create robust data pipelines from just a few lines of code — Tecton automatically builds, orchestrates, and maintains them so your teams don’t have to INCREASED MODEL ACCURACY Automate robust pipelines that materialize fresh features consistently across training and serving environments to ensure improved model performance IMPROVED COLLABORATION Standardize machine learning data workflows in one platform so teams can easily share and reuse features across use cases MISSION-CRITICAL RELIABILITY Serve features in production at extreme scale for batch, streaming, or real-time ML with the confidence that systems will always be up and running THE PROBLEM WE SOLVE THE PROBLEM WE SOLVE DATA ENGINEERING FOR REAL-TIME ML IS HARD * Training / serving skew * Point-in-time correctness * Productionizing notebooks * Real-time transformations * Melding batch + real-time data * Complicated backfills * Latency constraints * Ensuring service levels at scale A COMPLETE PLATFORM DESIGN AND MANAGE THE ENTIRE ML FEATURE LIFECYCLE From simple declarative transformation logic to fresh feature values you can store, serve, and monitor, in real-time. Feature Management Feature Logic Feature Repository Feature Engine Feature Store Explore the product FEATURE MANAGEMENT: DISCOVER, USE, MONITOR, AND GOVERN END-TO-END FEATURE PIPELINES With Tecton, anyone on the team can discover and use existing features all the while monitoring associated data pipelines, serving latencies, processing costs, and underlying systems of their machine learning applications. FEATURE LOGIC: DESIGN AND DEFINE ML FEATURES By using the SDK in a notebook or any other Python environment, users can define feature logic in SQL, PySpark, SnowPark, or Python, and rely on Tecton to execute complex data transformations like time-window aggregations or generate training data with accurate backfills. Batch Streaming (near real-time) Real-time Batch # Specify inputs, entities and compute configuration @batch_feature_view( description="Whether the user has a good credit score, updated daily", sources=[credit_scores], entities=[user], feature_start_time=datetime(2020, 10, 1), batch_schedule=timedelta(days=1), ) # Define transformation logic def user_credit_quality(credit_scores): return f''' SELECT USER_ID, IF (CREDIT_SCORE > 700, 1, 0) AS USER_HAS_GOOD_CREDIT, TIMESTAMP FROM {credit_scores} ''' Streaming (near real-time) # Specify inputs, entities, and compute configuration @stream_feature_view( description="Mean transaction amount of last hour, 24h and 72h, updated every 10 min", source=transactions, entities=[user], feature_start_time=datetime(2020, 10, 1), # Define logic for time-window aggregations aggregation_interval=timedelta(minutes=10), aggregations=[ Aggregation(column='AMOUNT', function='mean', time_window=timedelta(hours=1)), Aggregation(column='AMOUNT', function='mean', time_window=timedelta(hours=24)), Aggregation(column='AMOUNT', function='mean', time_window=timedelta(hours=72)), ], ) # Define query to use in the transformation def mean_transaction_amount(transactions): return f''' SELECT USER_ID, AMOUNT, TIMESTAMP FROM {transactions} ''' Real-time # Indicate schema of incoming request-time data (e.g. live transaction data) transaction_request = RequestSource([Field('amount', Float64)]) # Specify inputs and schema of the output @on_demand_feature_view( description="Whether the current transaction amount is higher than the user's weekly average.", sources=[transaction_request, historical_metrics], schema=[Field('transaction_amount_is_higher_than_average', Bool)], ) # Define transformation logic def transaction_amount_is_higher_than_average(transaction_request, historical_metrics): result = {} result['transaction_amount_is_higher_than_average'] = ( transaction_request['amount'] >= historical_metrics['amount_7d_mean']) return result FEATURE REPOSITORY: REGISTER AND COLLABORATE ON FEATURE DEFINITIONS Tecton’s feature repository lets users manage feature definitions as files in a git-like repository. With Tecton, users define features in code, version control them in git, unit test them, and roll them out safely using Continuous Delivery pipelines. With Tecton, bring battle-tested DevOps software practices to feature engineering. FEATURE ENGINE: TRANSFORM RAW DATA INTO FRESH FEATURE VALUES Tecton integrates with existing data processing and storage infrastructures to automatically compile the underlying data pipelines that compute batch, streaming, or real-time features, insulating the end user from their complexity. FEATURE STORE: STORE AND SERVE FRESH FEATURE VALUES Based on a user’s pre-defined requirements, Tecton helps organizations scale compute, storage, and serving independently to adjust to usage patterns, leverages an offline store for large-scale and low-cost retrieval (training) and an online store for low-latency retrieval (online serving). Tecton provides uninterrupted access to fresh features on demand. Training Online Serving Training Online Serving CUSTOMER STORIES Hear Cash App’s journey through various generations of its core machine-learning capabilities and how Tecton’s real-time feature platform helps them deliver world-class recommendations. Watch the Video Atlassian reduced the time to deploy new features from 1-3 months to 1 day using Tecton, powering recommendations in Jira and Confluence. Read Case Study Tide used Tecton to launch automatic fraud detection during new customer onboarding, replacing manual reviews of new accounts and saving $600K. Read Case Study Hear Cash App’s journey through various generations of its core machine-learning capabilities and how Tecton’s real-time feature platform helps them deliver world-class recommendations. Watch the Video Atlassian reduced the time to deploy new features from 1-3 months to 1 day using Tecton, powering recommendations in Jira and Confluence. Read Case Study Tide used Tecton to launch automatic fraud detection during new customer onboarding, replacing manual reviews of new accounts and saving $600K. Read Case Study Hear Cash App’s journey through various generations of its core machine-learning capabilities and how Tecton’s real-time feature platform helps them deliver world-class recommendations. Watch the Video Atlassian reduced the time to deploy new features from 1-3 months to 1 day using Tecton, powering recommendations in Jira and Confluence. Read Case Study Tide used Tecton to launch automatic fraud detection during new customer onboarding, replacing manual reviews of new accounts and saving $600K. Read Case Study Previous Next BATTLE TESTED FOR THE ENTERPRISE RELIABILITY AND SCALE YOU EXPECT FROM A PRODUCTION SYSTEM FULLY MANAGED AND CLOUD NATIVE Tecton makes it easy to deploy and operate machine learning with a managed, cloud native service. BUILT FOR AVAILABILITY AND SCALE Tecton is built for scale, delivering median latencies of ~5ms and supporting over 100,000 of requests per second. INTEGRATES WITH YOUR EXISTING STACK Tecton is not a database or a processing engine. It plugs into and orchestrates on top of your existing storage and processing infrastructure. SECURE AND COMPLIANT Tecton authenticates users via SSO and includes support for access control lists. We support GDPR compliance in your ML applications, and are SOC2 Type 2 certified. Learn More BUILD FEATURES, NOT PIPELINES Request a Free Trial LET'S KEEP IN TOUCH Receive the latest content from Tecton! 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