| Mar. 2020 |
Machine Learning That Works: Interview With Pawel Godula |
Kv-5I5v2UGY |
| Mar. 2020 |
Machine Learning That Works: Interview With Vladimir Rybakov |
9vuF01EBmH8 |
| May. 2020 |
Machine Learning That Works: Interview With Arash Azhand |
ULklVy3k8JU |
| Jun. 2020 |
Machine Learning That Works: Interview With Gabriel Preda |
bB1tcoQJ72g |
| Feb. 2022 |
Webinar: Time-series Forecasting With Model Types: ARIMAX, FBProphet, LSTM |
ZshMjxblrKE |
| Feb. 2022 |
Webinar: Computer Vision Projects With Lightning and neptune.ai |
BrrHrJrzSlA |
| Feb. 2022 |
Webinar: From Training to Production. How to Fit neptune.ai in Your ML Model Lifecycle? |
zrvXBVDebIU |
| May. 2022 |
Setting up MLOps at a Reasonable Scale With Jacopo Tagliabue |
YeTjgzllGqw |
| May. 2022 |
Building Visual Search Engine With Kuba Cieslik |
YFEGsm2XAVM |
| May. 2022 |
Why and What to Track in Time-Series Forecasting Projects |
cAWDLaaGSI8 |
| May. 2022 |
How and Why to Centralize Metadata From the MLOps Lifecycle |
bAYLtUOGev4 |
| May. 2022 |
Why and What to Track in Computer Vision Projects |
DqNBYTEKiyk |
| May. 2022 |
Deploying Models on GPU With Kyle Morris |
LchUTiF50xE |
| Jun. 2022 |
Testing Recommender Systems With Federico Bianchi |
gYYZHiWh54I |
| Jun. 2022 |
Navigating ML Observability With Danny Leybzon |
H5blEJovyDI |
| Jul. 2022 |
Data Engineering and MLOps for Neural Search With Jakub Zavrel and Fernando Rejon Barrera |
ZJIyobYE90U |
| Jul. 2022 |
Managing Computer Vision Projects With Michal Tadeusiak |
7aIDgljzTqQ |
| Jul. 2022 |
MLOps Live With Jacopo Tagliabue: How to Scale Reasonable Scale MLOps? |
g94ovT1Kvw4 |
| Jul. 2022 |
MLOps Live With Jacopo Tagliabue: The Culture in Reasonable Scale Companies |
v22Hj7_8spw |
| Jul. 2022 |
MLOps Live With Jacopo Tagliabue: What Is Reasonable Scale MLOps? |
Y8u8UkacIbY |
| Jul. 2022 |
MLOps Live With Jacopo Tagliabue: How to Start Setting Up MLOps at a Reasonable Scale? |
JBK7FbYr1uI |
| Aug. 2022 |
Leveraging Unlabeled Image Data With Self-Supervised Learning or Pseudo Labeling With Mateusz Opala |
XiOXgsVWnUw |
| Aug. 2022 |
MLOps Live With Mateusz Opala: Image Augmentation for SSL Models |
IPijZroew5Q |
| Aug. 2022 |
MLOps Live With Mateusz Opala: Challenges With Self-Supervised Learning and Pseudo Labeling |
S-qezq1wQtA |
| Aug. 2022 |
MLOps Live With Mateusz Opala: Working With Pseudo Labeling |
NDh0QdXyOes |
| Aug. 2022 |
MLOps Live With Mateusz Opala: Pseudo-labeling Applications |
X4Qkjym3ysw |
| Aug. 2022 |
MLOps Live With Mateusz Opala: What is Pseudo Labeling? |
tPtWBQMwyaU |
| Aug. 2022 |
Your First MLOps System: What Does Good Look Like? With Andy McMahon |
fge5I_SZu5Y |
| Aug. 2022 |
Building an MLOps Culture in Your Team With Adam Sroka |
iykUtOagU_8 |
| Sep. 2022 |
Embracing Responsible AI for ML Models in Production With Amber Roberts |
ximdIInoVNw |
| Sep. 2022 |
AutoML and MLOps With Adam Becker |
r8BxaPdRRf4 |
| Oct. 2022 |
How Early-Stage Startups and Small Teams Tackle MLOps With Duarte Carmo |
sqv1ydViDgA |
| Oct. 2022 |
What does MLOps at early-stage companies look like? #shorts |
GL-UFm53UW0 |
| Oct. 2022 |
Solving the Model Serving Component of the MLOps Stack With Chaoyu Yang |
mqTw4RYz-pE |
| Nov. 2022 |
Building Well-Architected ML Solutions on AWS With Phil Basford |
OYOEOxx96Vo |
| Nov. 2022 |
Philip Basford explains what architecting ML solutions well is all about in one minute #shorts |
zETF3-rPRqg |
| Nov. 2022 |
Differences Between Shipping Classic Software and Operating ML Models With Simon Stiebellehner |
6TZD49NB0_Q |
| Nov. 2022 |
How is ML engineer different from MLOps engineer? #shorts |
JgTDgdiyYn0 |
| Dec. 2022 |
How Does Data Get Transferred to neptune.ai Servers |
VrdRT-GdzfE |
| Dec. 2022 |
How to Version Datasets or Models Stored in the S3 Compatible Storage |
VwvD5RY_AoQ |
| Dec. 2022 |
Writing Clean, Production-Level ML Code With Laszlo Sragner |
4Gco3dA06Uw |
| Dec. 2022 |
Intersecting DevOps With the ML Lifecycle With Shirsha Ray Chaudhuri |
wQvuUlFfw3g |
| Dec. 2022 |
What does "clean code" actually mean and why should it be a topic of concern? #shorts |
MN_vj0TeDlI |
| Jan. 2023 |
Setting Up MLOps at a Healthcare Startup With Vishnu Rachakonda |
NOOFxMbbWeY |
| Jan. 2023 |
What is the value that ML solutions can bring to healthcare-focused companies? #shorts |
Hmv9kwYJsv0 |
| Jan. 2023 |
Continuous MLOps Pipelines With Itay Ben Haim |
PzSWDNfHa9U |
| Feb. 2023 |
ML Platform Teams, Features Stores, and Where MLOps Extends DevOps With Aurimas Griciunas |
6bfyfZFlT7M |
| Mar. 2023 |
Implementing Vector Search Engines With Kacper Lukawski |
BfMSgFPM2z4 |
| Mar. 2023 |
Deploying Conversational AI Products to Production With Jason Flaks |
oQTxwOPkCqU |
| Mar. 2023 |
Doing MLOps for Clinical Research Studies With Silas Bempong and Abhijit Ramesh |
NvlcNAtHei0 |
| Mar. 2023 |
Leveraging MLOps Technologies and Principles at Non-ML Companies With Andreas Malekos and Ivan Chan |
EzcLeYD2CTU |
| Apr. 2023 |
Managing Data and ML Teams to Deliver Value With Delina Ivanova |
KXaiY-MiZIM |
| Apr. 2023 |
What Does GPT-3 Mean For the Future of MLOps? With David Hershey |
lHaXDKD4q9o |
| May. 2023 |
Tackling MLOps Challenges in Computer Vision With Marcin Tuszyński |
-lVROaYrioE |
| May. 2023 |
Track, compare, and share your models in one place – neptune.ai |
bQzgnqM5J6U |
| May. 2023 |
Navigating Organizational Barriers by Doing MLOps With Leanne Kim Fitzpatrick |
_NNk8iZ1EKs |
| Jun. 2023 |
Live Workshop with Aurimas Griciūnas - Create AzureML Pipeline |
Co-Bq0IYPB8 |
| Jul. 2023 |
Learnings From Building the ML Platform at Stitch Fix With Stefan Krawczyk |
GYNsIR4QkZc |
| Aug. 2023 |
Learnings From Building the ML Platform at Mailchimp With Mikiko Bazeley |
W-iQoQpgwG4 |
| Aug. 2023 |
The Reality of Building ML Platform: Syncing With Business Objectives |
Cumb75bOoC4 |
| Aug. 2023 |
Inside Internal ML Platforms: Mailchimp's ML Team Structure |
2WguNmXxizc |
| Aug. 2023 |
Understanding the Feature Store: Literal, Physical & Virtual Explained |
hGsTvKnbpjg |
| Sep. 2023 |
Building MLOps Capabilities at GitLab As a One-Person ML Platform Team With Eduardo Bonet |
UfuC8bZVc3A |
| Oct. 2023 |
Code Reviews in the Data Science Job Flow [With Eduardo Bonet From GitLab] |
KUmmBYRj1x8 |
| Oct. 2023 |
GitLab’s Approach to Building an ML Platform Product |
ofKcwzFWp9g |
| Oct. 2023 |
The Role of an Incubation Engineer at GitLab [With Eduardo Bonet] |
M20AHKExSI8 |
| Oct. 2023 |
LLMs and the Future of the MLOps Infrastructure Stack |
bZGiiVa0o_s |
| Oct. 2023 |
MLOps vs DevOps [With Eduardo Bonet from GitLab] |
gPs_QvFnmq4 |
| Dec. 2023 |
Year in Review: LLMs & LLMOps, State of MLOps, and What's Next in 2024 |
G5dzU4Ye4nU |
| Jan. 2024 |
LLMs and Machine Learning Layoffs |
QsRmGRqjXqQ |
| Jan. 2024 |
Merging ML and DevOps Platform Teams |
jvlx6uM7A-0 |
| Jan. 2024 |
MLOps is an Extension of DevOps, Not a Fork (a Year Later) |
M99tP-6YNlY |
| Jan. 2024 |
Understanding ML Model Registry: The 2024 Perspective |
NLE9QaX5kgE |
| Jan. 2024 |
The Rise of Internal ML Platforms in 2023 and Unsolved Debate on End-to-End vs. Single Components |
jdwgHzydgtA |
| Jan. 2024 |
The Impact of AI Regulations in 2024 |
mVB3qHWOiUs |
| Jan. 2024 |
MLOps and LLMOps Predictions for 2024 |
0eh5lE2Zet8 |
| Jan. 2024 |
How to Reproduce Experiments with Neptune |
FRhOu_ETzBY |
| Jan. 2024 |
Have LLMs impacted ML layoffs? #llm #llms #machinelearning #ml #layoffs2023 |
t3jblPov9eQ |
| Jan. 2024 |
End-to-end platform vs. single components #ml #machinelearning #mlops #techstack |
T9n3esikvMA |
| Jan. 2024 |
Will ML platform team composition change soon? #ml #machinelearning |
VE8oEBrnTWs |
| Jan. 2024 |
Where #llms don't align? #2024predictions |
nbd1oObdw9s |
| Jan. 2024 |
MLOps is an extension of DevOps, not a fork (a year later) #mlops #devops |
q7DQ7lDqyY4 |
| Jan. 2024 |
Should ML and DevOps platform teams merge? #ml #machinelearning #devops |
xPtg1W3QOFw |
| Jan. 2024 |
Is MLOps an extension of DevOps? #mlops #devops |
ne4Kcy4Jhng |
| Jan. 2024 |
Integrating security into #dev and #ml toolkits |
6F6ueqtfblI |
| Jan. 2024 |
Exploring a Single Experiment in Neptune |
QVCzE91Lubc |
| Jan. 2024 |
How to Use Neptune for A/B Testing ML Models |
6uD4jLiA0ok |
| Jan. 2024 |
How to Register and Version Models With Neptune |
xK6FqjCBa0k |
| Jan. 2024 |
Overview of Neptune's Architecture |
1Mo4CBXhK2U |
| Jan. 2024 |
How to Integrate Neptune Into Your Code |
A3DduWaro7s |
| Feb. 2024 |
How to Use Custom Dashboards in Neptune |
zSDM3b0_V8k |
| Feb. 2024 |
How to Track ML Model Training: PyTorch + neptune.ai Integration |
jVPrWj7JeKo |
| Feb. 2024 |
How to Track Hyperparameters: Optuna + neptune.ai Integration |
pXkLVpXFZxI |
| Feb. 2024 |
How Does Programmatic CI/CD Work With Neptune’s Model Registry? |
vbKPlw0z4Yw |
| Feb. 2024 |
MLOps at Pinterest with Aayush Mudgal: MLOps World 2023 |
W_j3uqHj-e0 |
| Feb. 2024 |
ZenML in the LLM Space: Adam Probst at MLOps World 2023 |
J0OoDT5uFaE |
| Feb. 2024 |
Canonical | Ubuntu: The Future of Kubernetes and Open Source |
WK6r3d0iLMI |
| Feb. 2024 |
GenAI and LLMs at Digits: Cost of Hosting, Fine-Tuning, Evaluation |
sn9xLpB2i90 |
| Feb. 2024 |
How #mlops started at Pinterest |
idCMkHzFdbs |
| Mar. 2024 |
MLOps World 2023: Interview Series with Aurimas Griciūnas |
D05nMWOnqq0 |
| Mar. 2024 |
ZenML in the #llm space with Adam Probst at #mlops World 2023 |
IgSnSO5Qy34 |
| Mar. 2024 |
The future of #kubernetes |
6zgYyhJraOo |
| Mar. 2024 |
Multi-task learning in #ml |
MpVj9p1o_c8 |
| Mar. 2024 |
#generativeai use cases at Digits |
V-qxs6vQ78I |
| Mar. 2024 |
LLM Evaluation, Cost Considerations, and the Future of Open-Source: Rajiv Shah at MLOps World 2023 |
BD7jhILtl34 |
| Mar. 2024 |
The future of #opensource, #mlops and #llms |
qU-k6JPWYak |
| Mar. 2024 |
#llm based evaluation |
xO0lNrjc3HM |
| Mar. 2024 |
How to Compare Experiments in Neptune |
Xt-_-UQX15c |
| Mar. 2024 |
Challenges of adopting #llms in production on your own infra |
hfqPQW--3c4 |
| Mar. 2024 |
Safeguarding LLMs with Guardrails AI: Shreya Rajpal at MLOps World 2023 |
XWFh602lCEo |
| Mar. 2024 |
Moving from #tensorflow to #pytorch |
DIJEa5BPgUw |
| Mar. 2024 |
Classical #ml models vs. #llms at Digits |
ZA6k5qp-3Ec |
| Mar. 2024 |
ML Pipeline Management Using Open-Source: DAGWorks and Hamilton |
3aRATDPNA_E |
| Mar. 2024 |
Challenges when building #guardrails #ai: Shreya Rajpal at #mlops world 2023 |
a_Jjf60aK9k |
| Mar. 2024 |
Mastering LLMs with AI Makerspace: MLOps World 2023 |
vO4eeKk9zYw |
| Mar. 2024 |
Building ML Platform at Scout 24 [With Olalekan Elesin] |
_6lSD9OcneY |
| Mar. 2024 |
User experience of plugging-in #guardrails #ai when using #chatgpt |
kzj5sbDMwc4 |
| Mar. 2024 |
Evaluating #llm models with Rajiv Shah (#mlops world 2023) |
XzQjGsa5rTA |
| Mar. 2024 |
Why Go Open-Source? The Insight Story of Hamilton at Stitch Fix |
bfAjg1IMC30 |
| Mar. 2024 |
How to Version and Compare Datasets in Neptune |
yzQyUjBiqZw |
| Mar. 2024 |
Tips for working with #llms |
HcCcna_luW4 |
| Mar. 2024 |
Intersection of Neuroscience and LLMs, Agent Systems, and LLM Predictions |
XKRIuIJEhLw |
| Apr. 2024 |
#llmops vs. #mlops |
plZkchQjbn8 |
| Apr. 2024 |
What’s next for #llms with Greg Loughnane |
OwsFBiT1uq0 |
| Apr. 2024 |
End-to-end ML Platform Team Structure at Stitch Fix |
SSCWy8ecEBw |
| Apr. 2024 |
Create and Save Custom Table Views in Neptune |
tu9SGTUsWxw |
| Apr. 2024 |
Experiment tracking and LLMs |
p8GiJmhg1os |
| Apr. 2024 |
Getting closer to the business as an #mlops engineer |
tu0QKyFVDjA |
| Apr. 2024 |
The future of #opensource with Rajiv Shah (ML Engineer at #huggingface) |
e6QQchq1N9c |
| Apr. 2024 |
Project success metrics at #gitlab |
kieKk4F1Czk |
| Apr. 2024 |
The Crucial Role of Program Managers [ML Platform Team at Stitch Fix] |
2mKxORsoSfY |
| Apr. 2024 |
How to Control Access to ML Models in Neptune? |
4GFevjFhPsU |
| Apr. 2024 |
Predictions for #llms with Charles Frye (#mlops #generativeai world 2023) |
g7vg3o9gIvo |
| Apr. 2024 |
Learnings From Building the ML Platform at Uber (Michelangelo) |
B5ABVupqi1U |
| Apr. 2024 |
How is DAGWorks different from its competitors? #mlops |
9c20WdtYJdE |
| Apr. 2024 |
Selecting the first #ml platform projects |
WGY6KfADe6k |
| Apr. 2024 |
The Story Behind Michelangelo | ML Platform at Uber |
6NDPAbDWqak |
| Apr. 2024 |
Methods for evaluating #llms with Hennes Hapke |
2T80mQCPi4A |
| Apr. 2024 |
The future of MLOps with LLMs | Mike Del Balso |
CcZ0wo4tEN8 |
| Apr. 2024 |
#llm evaluation challenges with Greg Loughnane (#mlops world 2023) |
EGcW6AHTiic |
| Apr. 2024 |
#llm cost considerations: self-hosting vs. proprietary API |
-A-DnkY0nrg |
| Apr. 2024 |
Vector Databases and Feature Platforms [With Mike Del Balso From Tecton] |
SCfq1WYCRfQ |
| Apr. 2024 |
Future plans for Canonical’s #ai #ml projects with Maciej Mazur (Principal ML Engineer) |
iyVgjBo51NI |
| Apr. 2024 |
Building the ML Platform at Scout24 | How to Ship Features People Actually Need |
2tVUwrgT0Bs |
| Apr. 2024 |
The motivation behind internal #ml platforms |
XbNtVRA9Qqg |
| Apr. 2024 |
Future applications of #llms |
0DvA5T9pM3s |
| Apr. 2024 |
What is neptune.ai? – Product demo |
d7hG3v1K8LU |
| May. 2024 |
Using LLMs to Drive Product Vision [With Olalekan Elesin] |
h64zfmnMmoA |
| May. 2024 |
Importance of long-term memory in #agentsystems |
FETnOJS6eI0 |
| May. 2024 |
Michelangelo's Feature Platform [With Co-Creator, Mike Del Balso] |
8tppIDRvyVw |
| May. 2024 |
Building #ml platform: lessons learned |
WsjstX8ogSI |
| May. 2024 |
Challenges with #llms |
tILrQ9auM9s |
| May. 2024 |
End-to-end Platforms vs. Point Solutions [With Olalekan Elesin] |
MOpaCn4iOWc |
| May. 2024 |
#llm deployment considerations: #gpu vs. #cpu |
MSt20E457-I |
| May. 2024 |
Going Deep On Model Serving, Deploying LLMs and Anything Production-Deployment |
oREWPxJQUEE |
| May. 2024 |
Reviving and improving Kubeflow: #mlops world 2023 |
urnmzcMtTyo |
| May. 2024 |
#llms and feature stores with Mike Del Balso |
kVJZ3dGZKqA |
| May. 2024 |
Real-time Machine Learning | Mike Del Balso |
gBW0MLB4zKQ |