7th annual gathering of AI, agentic, and ML practitioners who learn, share, and advance AI excellence in production. Nov 17-18 | Austin, TX
Joined September 2020
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402 Photos and videos
MLOps World | GenAI Summit retweeted
17 May 2021
LinkedIn Open-sources Greykite, a library for time-series forecasting ๐๐๐ฎ
#mlopsworld2021
github.com/linkedin/greykite
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MLOps World | GenAI Summit retweeted
From promise to practice: Dr. Mamdani's team at Unity Health Toronto deployed dozens of AI solutions in production to improve patient outcomes and hospital efficiency.
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MLOps World | GenAI Summit retweeted
Scaling LLMs is fundamentally a systems problem.
Not a hardware problem. Not a modeling problem. A systems problem, and that changes what you debug first when moving from prototype to production.
Deepkamal Gill, The Vanguard Group. TMLS 2026 โ torontomachinelearning.com/
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MLOps World | GenAI Summit retweeted
@netflix 's Globalization team removes language barriers across 300 million members in 190 countries and 30 languages.
@cwolferesearch will walk through how his team is evaluating show synopses with LLM-as-a-Judge at TMLS 2026 โ torontomachinelearning.com/
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MLOps World | GenAI Summit retweeted
Your agent eval says accuracy improved.
But did latency spike? Does your LLM-based metric even agree with human judgment? And is that 5% gain real or noise?
Abhimanyu Anand, @elastic
TMLS 2026 โtorontomachinelearning.com/
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MLOps World | GenAI Summit retweeted
30 Apr 2021
How you can go about hiring your VP of Engineering?
A nicely laid out framework here (from @Shopify 's VP of Engineering)
Great share, @fnthawar review.firstround.com/hiringโฆ #mlopsworld2021
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MLOps World | GenAI Summit retweeted
21 Apr 2021
AI/Data Landscape Credit: @LFAIDataFdn
Open Source AI & Tools Demo week at #MLOpsWorld2021 mlopsworld.com/tools/
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MLOps World | GenAI Summit retweeted
An agent has a flow, and getting the flow right is critical.
You can trust the result only if the path the agent took aligns with your architectural intent. Agents are processes.
Michael Havey, OpsGuru.
TMLS 2026 โtorontomachinelearning.com/
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MLOps World | GenAI Summit retweeted
Most large AI orgs have a data problem that isn't the one they think.
It isn't missing data. It's data that exists, was built deliberately, and still isn't being reused. It sits in a pipeline nobody else can find.
@Mendelsohn Chan, @Alation
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MLOps World | GenAI Summit retweeted
For long-context workloads, the bottleneck is no longer raw compute.
Memory, bandwidth, and latency become the real limits once sequences get long, and the assumptions that work for shorter workloads stop carrying over.
Mehdi Rezagholizadeh, @AMD.
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MLOps World | GenAI Summit retweeted
Semantic similarity is not the same thing as answer retrieval.
Lean on embeddings as the default for every use case and you get systems that sound convincing while returning weak, incomplete, or confidently incorrect answers.
David vonThenen, @NetApp .
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MLOps World | GenAI Summit retweeted
Every software company claims to be becoming an AI company. Most are re-running the wrong playbook: treating AI like an infrastructure migration instead of a shift in how products are designed, shipped, and operated.
Alet Blanken, @Workday . TMLS 2026.
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MLOps World | GenAI Summit retweeted
The default assumption in AI security is that encryption costs performance. Fully Homomorphic Encryption, especially.
The talk analyzes trade-offs between encryption overhead and latency, using open-source FHE and model optimizations.
Tyson Macaulay, @01quantuminc
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MLOps World | GenAI Summit retweeted
A robot that can recall what it did last week needs memory across three dimensions: spatial, descriptive, and visual.
That is the architecture behind experience recall for temporal question answering in agentic robots.
@stevewaslander , @UofT
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MLOps World | GenAI Summit retweeted
Agent vulnerability is primarily architectural, not a model alignment problem.
Fixing the model without addressing orchestration logic leaves the most exploitable attack surface untouched.
Naga Sujitha Vummaneni, @Ripple
TMLS 2026 โ torontomachinelearning.com/
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MLOps World | GenAI Summit retweeted
$26/month infrastructure. 200 languages served. A core team of three.
That's the Multilingual Climate Chatbot, a production RAG system, open-source and easy to adopt.
Luis Ticas Helena Yu, Sprout Climate
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MLOps World | GenAI Summit retweeted
Real data can be a legitimate option for pre-training tabular foundation models - despite being underutilized in favour of synthetic data.
It captures complex signals critical for downstream generalization.
Anthony Caterini @Layer6AI is exploring that here at TMLS 2026
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MLOps World | GenAI Summit retweeted
Evals in production: the #1 constraint our committee flagged this year.
Korede Adegboye is presenting his framework at TMLS 2026: automated dataset curation, failure-mode detection, and uncertainty-aware decisioning.
โ See All Speakers: torontomachinelearning.com/
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Vino Sangaralingam is automating and productionizing an NLP-based process with a GenAI component in regulated finance and payments, where ROI and measurement need to be built into the pipeline from day one.
Sheโs on the MLOps World Steering Committee.
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MLOps World 2026 ยท Nov 17โ18 ยท Austin, TX
โ See what MLOps World is built around: mlopsworld.com
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