| Mar. 2021 |
Tecton Web Demo |
u_L_V2HQ_nQ |
| Apr. 2021 |
apply() - the ML data engineering conference |
OtIU7HsHCE8 |
| Apr. 2021 |
apply() - the ML data engineering conference |
bVwLjSsBSc8 |
| May. 2021 |
apply() Conference 2021 | Scaling Online ML Predictions to Meet DoorDash Growth |
_iipJI4HKf0 |
| May. 2021 |
apply() Conference 2021 | Machine Learning is Going Real-Time |
6xhLKq_KR-I |
| May. 2021 |
apply() Conference 2021 | Apache Arrow and the Next Generation of Data Analytics Systems |
-ZikPi2nmSI |
| May. 2021 |
apply() Conference 2021 | Rethinking Feature Stores with Feast and Tecton |
qh7bh4YVI2E |
| May. 2021 |
apply() Conference 2021 | Financially Responsible Feature Engineering |
mVVZLCJzRvc |
| May. 2021 |
apply() Conference 2021 | What is the MLOps Community? |
7XucZyMfAUU |
| May. 2021 |
apply() Conference 2021 | Best Practices for Productionalizing Data & ML Projects |
9lA0kAtt3z8 |
| May. 2021 |
apply() Conference 2021 | Real-time Personalization of QuickBooks using Clickstream Data |
Fzjq-101_7E |
| May. 2021 |
apply() Conference 2021 | ML Design Patterns for Data Engineers |
TdSJJRqz80U |
| May. 2021 |
apply() Conference 2021 | Building a Best-in-Class Customer Experience Platform |
AZsmcHpuke8 |
| May. 2021 |
apply() Conference 2021 | The Only Truly Hard Problem in MLOps |
mizpURvWLu0 |
| May. 2021 |
apply() Conference 2021 | Apache Kafka, Tiered Storage and TensorFlow for Streaming Machine Learning |
LSUT5q6OKN0 |
| May. 2021 |
apply() Conference 2021 | Feature Stores at Tide |
ExFjh1Hl58M |
| May. 2021 |
apply() Conference 2021 | MLOps Done Right with Centralized Model Performance Management |
04hpriv23kg |
| May. 2021 |
apply() Conference 2021 | Evolution and Unification of Pinterest ML Platform |
8Swp9xM-rLY |
| May. 2021 |
apply() Conference 2021 | Redis as an Online Feature Store |
rlOq-baAZgY |
| May. 2021 |
apply() Conference 2021 | Tackling Fraud with Tecton |
8Vwy7rHX9sY |
| May. 2021 |
apply() Conference 2021 | Hamilton: a Micro Framework for Creating Dataframes |
B5Zp_30Knoo |
| May. 2021 |
apply() Conference 2021 | Data Observability: The Next Frontier of Data Engineering |
c_Q6xij-OZw |
| May. 2021 |
apply() Conference 2021 | Supercharging our Data Scientists’ Productivity at Netflix |
vJRqoVwD9ik |
| May. 2021 |
apply() Conference 2021 | Third Generation Production ML Architectures |
hzW0AKKqew4 |
| May. 2021 |
apply() Conference 2021 | Feature Stores at Spotify: Building & Scaling a Centralized Platform |
mItriAtSrgs |
| May. 2021 |
apply() Conference 2021 | Building a Feature Store to Reduce the Time to Production of ML Models |
1_FeIk0hHkE |
| May. 2021 |
apply() Conference 2021 | Programmatic Supervision for Software 2.0 |
mXfLaTFrgeU |
| May. 2021 |
apply() Conference 2021 | Scaling a Machine Learning Social Feed with Feature Pipelines |
1PXaOxxWNbU |
| May. 2021 |
apply() Conference 2021 | Towards Reproducible Machine Learning |
Sn25zwkc9Vg |
| May. 2021 |
apply() Conference 2021 | Exploiting the Data Code |
rrvN_6aRRNU |
| May. 2021 |
apply() Conference 2021 | Reusability in Machine Learning |
M1F0FDJGu0Q |
| May. 2021 |
apply() Conference 2021 | Panel: Challenges of Operationalizing ML |
JdACsTONNWA |
| May. 2021 |
apply() Conference 2021 | Towards a Unified Real-Time ML Data Pipeline, from Training to Serving |
s1DyAppdNmQ |
| May. 2021 |
apply() Conference 2021 | Conference Fun |
RRofLTjfhTc |
| May. 2021 |
apply() Conference 2021 | Bringing Feast 0.10 to AWS |
6yeCKnteFY8 |
| May. 2021 |
apply() Conference 2021 | A Point in Time: Mutable Data in Online Inference |
vHu5q0WcvyA |
| May. 2021 |
apply() Conference 2021| Top Talk Highlights |
p5NPjpjHMhY |
| Jul. 2021 |
Data scientist at Shopify feedback about Feast the open source Feature Store |
4LP_YrmziJk |
| Aug. 2021 |
apply() community meetup pregame - Interactive and natural interfaces // Karan Goel |
woVjNVjmBuE |
| Aug. 2021 |
Matt and Karan pre apply() chat |
2AerAbOxzVw |
| Aug. 2021 |
apply(meetup) - the ML data engineering conference |
pMFbRJ7AnBk |
| Sep. 2021 |
Building Malleable ML Systems through Measurement, Monitoring & Maintenance | apply() meetup 2021 |
C7YMWbhwWDk |
| Sep. 2021 |
Quickly performing Exploratory Data Analysis with Rule-based Profiling | apply() meetup 2021 |
DUsLbnPhFao |
| Sep. 2021 |
High Performance Feature Serving with Feast on AWS | apply() meetup 2021 |
EuXoH-phaF8 |
| Sep. 2021 |
How Shopify Contributed to Scale Feast | apply() meetup 2021 |
Qv4-AaPjJCg |
| Sep. 2021 |
Panel: Building High-Performance ML Teams |
kmD8e_LOzYw |
| Sep. 2021 |
ML Observability: Critical Piece of the ML Stack | apply() meetup 2021 |
MMXQnm7t760 |
| Sep. 2021 |
How Robinhood Built a Feature Store Using Feast | apply() meetup 2021 |
-DgInoUa0uI |
| Sep. 2021 |
Streaming Architecture with Kafka, Materialize, dbt, and Tecton | apply() meetup 2021 |
FGWTvdXaE5M |
| Sep. 2021 |
Kickoff - New Abstractions Enabling Operational ML | apply() meetup 2021 |
p1V4MdzyMgw |
| Feb. 2022 |
apply(meetup) - the ML data engineering conference |
GXqK6HlYG6M |
| Mar. 2022 |
Apply() Meetup | What Data Engineers Should Know About Real-Time Analytic |
KE-ZEuJDzlU |
| Mar. 2022 |
Apply() Meetup | Using Redis as your Online Feature Store |
ZRNqbgVNn6o |
| Mar. 2022 |
Apply() Meetup | Hamilton, a framework for creating dataframes, and its application at Stitch Fix |
CHfrT5OVjlM |
| Mar. 2022 |
Apply() Meetup | Model Calibration in the Etsy Ads Marketplace |
G3R3SufHJn4 |
| Mar. 2022 |
Apply() Meetup | Using Feast in a Ranking System |
Vvfit9Slb8U |
| Mar. 2022 |
Apply() Meetup | Twitter’s Feature Store Journey |
FX83PGh2qnU |
| Mar. 2022 |
Apply() Meetup | ML Projects Aren’t An Island |
FbtQfNOgOkA |
| Mar. 2022 |
Apply() Meetup | Managing Data Infrastructure with Feast |
1RoNyZI22C4 |
| Mar. 2022 |
Apply() Meetup | How to choose the right online store for your ML features. |
osxzKxiznm4 |
| Mar. 2022 |
Apply() Meetup | Data Engineering Isn’t Like Software Engineering |
rBrTqkNtY0M |
| Mar. 2022 |
Apply() Meetup | Data Transfer Challenges In Evaluating MLOps Platforms |
_9APN-SSobw |
| Mar. 2022 |
Apply() Meetup | The Data Engineering Lifecycle |
GsbTPLcl7MU |
| Mar. 2022 |
Apply() Meetup | Building the Data Stack for Operational ML |
ezcjIUxmy4o |
| Mar. 2022 |
Apply() Meetup | Building feature stores on Snowflake |
uSbS754XTeA |
| May. 2022 |
apply() - the ML data engineering conference |
jbnbjNkeBKk |
| May. 2022 |
apply() - the ML data engineering conference |
wOqBj1hst3M |
| May. 2022 |
apply() Conference 2022 | Lakehouse: A New Class of Platforms for Data and AI Workloads |
6j0MazSTLHg |
| May. 2022 |
apply() Conference 2022 | Intelligent Customer Preference engine with real-time ML systems |
WPKWRXU0bOQ |
| May. 2022 |
apply() Conference 2022 | Lessons learned from the Feast community |
o787CSFKaXU |
| May. 2022 |
apply() Conference 2022 | Weaver: CashApp’s Real Time ML Ranking System |
8KsPtzj1wa8 |
| May. 2022 |
apply() Conference 2022 | Machine Learning, Meet SQL: When ML Comes to the Database |
T1StmzI0RbQ |
| May. 2022 |
apply() Conference 2022 | Machine Learning in Production: What I learned from monitoring 30+ models |
Ki0es3bLsG8 |
| May. 2022 |
apply() Conference 2022 | Wild Wild Tests: Monitoring Recommender Systems in the Wild |
FfJKFbgdSSo |
| May. 2022 |
apply() Conference 2022 | More ethical machine learning using model cards at Wikimedia |
t4GMq7MC7Js |
| May. 2022 |
apply() Conference 2022 | ralf: Real-time, Accuracy Aware Feature Store Maintenance |
c4mTAMkq0N8 |
| May. 2022 |
apply() Conference 2022 | Streamlining NLP Model Creation and Inference |
jBN_PTKcOmE |
| May. 2022 |
apply() Conference 2022 | Bring Your Models to Production with Ray Serve |
j9sV3HBtaxY |
| May. 2022 |
apply() Conference 2022 | Empowering Small Businesses with the Power of Tech, Data, and ML |
hTR-YSrN60Q |
| May. 2022 |
apply() Conference 2022 | Training Large-Scale Recommendation Models with TPUs |
iBfNfJHr9mE |
| May. 2022 |
apply() Conference 2022 | Key Pillars of ML Observability and How to Apply Them to Your ML Systems |
fdhJiVD3fYI |
| May. 2022 |
apply() Conference 2022 | Panel: Common Patterns of the World’s Most Successful ML Teams |
m-qcjCE7AHU |
| May. 2022 |
apply() Conference 2022 | Are Transformers Becoming the Most Impactful Tech of the Decade |
tWaXLomb-zw |
| May. 2022 |
apply() Conference 2022 | The dbt Semantic Layer |
ezqsNzwN0p4 |
| May. 2022 |
apply() Conference 2022 | Why is Machine Learning Hard |
F3ExAlW62e8 |
| May. 2022 |
apply() Conference 2022 | Streaming is just an Implementation Detail |
iR-iN2Mqi0A |
| May. 2022 |
apply() Conference 2022 | PyTorch’s Next generation of Data Tooling |
pAoV9rls1IY |
| May. 2022 |
apply() Conference 2022 | Panel: What Do Engineers Not Get About Working with Data Scientists? |
0lzTx5yEGRE |
| May. 2022 |
apply() Conference 2022 | Machine Learning Platform for Online Prediction and Continual Learning |
P5uBWGqzogs |
| May. 2022 |
apply() Conference 2022 | Is Production RL at a Tipping Point |
ufdhVCj-tpg |
| May. 2022 |
apply() Conference 2022 | Managing the Flywheel of ML Data |
95p0JR6H-fM |
| May. 2022 |
apply() Conference 2022 | How to Draw an Owl and Build Effective ML Stacks |
wbExGrRBDvI |
| May. 2022 |
apply() Conference 2022 | Enabling Rapid Model Deployment in the Healthcare Setting |
HWD42AHrgZk |
| May. 2022 |
apply() Conference 2022 | Extending Open Source Feature Stores to Fit Adyen |
Yidl2VmrgVs |
| May. 2022 |
apply() Conference 2022 | DIY Feature Store: A Minimalist’s Guide |
q4bZ0ixdUKk |
| May. 2022 |
apply() Conference 2022 | Building a Movie Recommender with Tecton and Snowflake |
8vU9Z6QZVTc |
| May. 2022 |
apply() Conference 2022 | Declarative Machine Learning Systems: Ludwig & Predibase |
74hqlj5k4Zg |
| May. 2022 |
apply() Conference 2022 | Building Real Time ML Features with Feast, Spark, Redis, and Kafka |
yFyFKTtYY-w |
| May. 2022 |
apply() Conference 2022 | Compass: Composable and Scalable Signals Engineering |
nyaZm04qWl8 |
| May. 2022 |
apply() Conference 2022 | Accelerating Model Deployment Velocity |
tClDQk7DqlY |
| May. 2022 |
apply() Conference 2022 | Fireside Chat: Is ML a Subset or a Superset of Programming? |
urx00Wfm4dw |
| May. 2022 |
apply() Conference 2022 | Data Observability for Machine Learning Teams |
GKpflt3fvKw |
| May. 2022 |
apply() Conference 2022 | Feature Engineering at Scale with Dagger and Feast |
9B9qqqJVm4M |
| May. 2022 |
apply() Conference 2022 | Engineering for Applied ML |
F_RXAHsgbRE |
| Jul. 2022 |
More Than a Feature Store - Tecton The Complete Feature Platform for Machine Learning |
VhuxiERkAtg |
| Dec. 2022 |
apply(recsys) Conference 2022 | Workshop: Choosing Feast or Tecton for Your RecSys Architecture |
dFc5hzuPwb8 |
| Dec. 2022 |
apply(recsys) Conference 2022 | Lessons Learned: The Journey to Operationalizing Recommender Systems |
cWBrqB4ewrw |
| Dec. 2022 |
apply(recsys) Conference 2022 | Real-Time Recommendation System With Collision-less Embedding Table |
zJfDNOyAJP4 |
| Dec. 2022 |
apply(recsys) Conference 2022 | Recommend API: Slack’s Unified End-to-End ML Infrastructure |
bW9Jt4iYsoI |
| Dec. 2022 |
apply(recsys) Conference 2022 | Crawl, walk, run: a practical intro to applied recommender systems |
YQmL-Khqxvk |
| Jun. 2023 |
apply(risk) 2023 | Access Control in ML Feature Platforms |
SdUEVFVggHs |
| Jun. 2023 |
apply(risk) 2023 | Fraud Prevention Best Practices In ML Observability & Emerging Approaches |
iyhVeP2SEaA |
| Jun. 2023 |
apply(risk) 2023 | Challenges at the Intersection of ML & Real-Time Data: Lessons Learned |
y06fj-Ymd64 |
| Jun. 2023 |
apply(risk) 2023 | Fighting Financial Crime With Machine Learning at Tide |
qDlqPO1RwPg |
| Jun. 2023 |
apply(risk) 2023 | A Decade of Risk Machine Learning: Some Lessons Learned |
2XEeDTB4Aqs |
| Jun. 2023 |
apply(risk) 2023 | Solving Twitter's Bot Problem With Less Than 10 Lines of Code |
lPIcNW-_4K4 |
| Jun. 2023 |
apply(risk) 2023 | Powering ML Fraud Detection Models With Advanced Aggregations |
M3utvzbKSHM |
| Jun. 2023 |
apply(risk) 2023 | How to Build a Real-Time Fraud Detection Application With a Feature Platform |
X0lkG3JWWro |
| Jun. 2023 |
apply(risk) 2023 | Fighting Fraud with Machine Learning at Remitly |
jKg8QiLfRaY |
| Jun. 2023 |
Tecton and Databricks |
HMW-qfkCwVU |
| Jun. 2023 |
Tecton and Snowflake |
JYrIMQbw_8o |
| Aug. 2023 |
Introducing Tecton and Google Cloud Platform (GCP) |
4Txntj5cJz0 |
| Aug. 2023 |
Tecton and Google Cloud Platform (GCP) |
FbM35-mSUSc |
| Aug. 2024 |
The BIGGEST blocker to building AI…why projects get stalled |
xoXUS_raai8 |
| Aug. 2024 |
Making LLMs smarter with better context…a real PITA? |
-x8C-IbHvZU |
| Sep. 2024 |
Tecton 1.0 Launch |
G-JGB6vYG4g |
| Sep. 2024 |
Machine Learning at Atlassian with Tecton |
E5UA1gVmnP0 |
| Sep. 2024 |
Machine Learning at Coinbase with Tecton |
b6e9Exvbz4g |
| Sep. 2024 |
Building a Real Time Fraud Detection System at Signifyd 1 |
Usqf3RqNDjo |
| Sep. 2024 |
Productionize AI ML Faster, and with Less Tech Debt |
_IKEF_ygvIo |
| Sep. 2024 |
Production Embeddings for Business Critical Predictive & Generative AI |
VRczhlGcrws |
| Sep. 2024 |
Powering Millions of Real Time Rankings at GetYourGuide |
_kUi8mTwCVk |
| Sep. 2024 |
Full RAG A Modern Architecture of Hyperpersonalization |
zM_SIIfg9rU |
| Sep. 2024 |
Building a Resilient, Real Time Fraud System at Block |
3sJgCwEMQNo |
| Sep. 2024 |
Tecton 2024 Launch Event What's New and Coming Next |
H7klETCqKEs |
| Sep. 2024 |
Supercharge Production AI with Features as Code |
CQOtFEBU4uo |
| Sep. 2024 |
Real Time ML for Enhanced Customer Experiences at Varo Bank |
LVp1hniCjVY |
| Sep. 2024 |
Building Personalized Insurance Pricing with Real Time AI at Prima |
yeZj8NQxnKg |
| Nov. 2024 |
Building Plaid's ML Fraud Detection Application, Signal |
6g-1bQdgsmU |
| Nov. 2024 |
From Dream to Stream Scaling ML Engineering at Flo Health |
alR8JGsw6KA |
| Nov. 2024 |
Intro to Tecton - The Aggregation Engine |
1P0XwnnXTXw |
| Nov. 2024 |
Intro to Tecton - The Tecton Framework |
v12U4zpPl0c |
| Nov. 2024 |
Intro to Tecton - Materialization |
trDouyaf50A |