MLOps Conference

MLOps Conference

On DataOps, productionizing ML models, and running experiments at scale.

Make a submission

Accepting submissions till 14 Jul 2021, 11:00 PM

Machine Learning (ML) is at the helm of products. As products evolve with time, so is the necessity for ML to evolve. In 2010s, we saw DevOps culture take the forefront for engineering teams. 2020s will be all about MLOps.

MLOps stands for Machine Learning Operations. MLOps mainly focuses on workflows, thought processes and tools that are used in creating ML models, and their evolution over time. The workflows for ML at organizations are different as the problem space, maturity of teams and experience with ML tools are widely different.

MLOps relies on DataOps. DataOps is about Data operations, and helps define data and SLOs for data - how they are stored, managed and mutate over time - thereby providing the foundations for sound ML. The success and failure of ML models depends heavily on DataOps, where data is well-managed and brought into the system in a well thought out manner. ML and data processes have to evolve to provide insights into the reasons as to why certain models are not behaving as before.

Productionizing ML models is a challenge, but so is running experiments at scale. MLOps caters not only to scaling ML models in production, but also helps in providing guidelines and thought processes to support rapid prototyping and research for ML teams.

MLOps Conference 2021 edition

The 2021 edition is curated by Nischal HP, Director of Data at Scoutbee.

The conference covers the following themes:

  1. Machine Learning Operations
  2. Machine Learning in Production
  3. Privacy and Security in Machine Learning
  4. Tooling and frameworks in Machine Learning
  5. Economies of Machine Learning

Speakers from Doordash, Twilio, Scribble Data, Microsoft Research Labs India, Freshworks, Aampe, Myntra, Farfetch and other organizations will share their experiences and insights on the above topics.

Schedule: https://hasgeek.com/fifthelephant/mlops-conference/schedule

Who should participate in MLOps conference?

  1. Data/MLOps engineers who want to learn about state-of-the-art tools and techniques.
  2. Data scientists who want a deeper understanding of model deployment/governance.
  3. Architects who are building ML workflows that scale.
  4. Tech founders who are building products that require ML or building developer productivity products for ML.
  5. Product managers, who are seeking to learn about the process of building ML products.
  6. Directors, VPs and senior tech leadership who are building ML teams.

Contact information: Join The Fifth Elephant Telegram group on https://t.me/fifthel or follow @fifthel on Twitter. For inquiries, contact The Fifth Elephant on fifthelephant.editorial@hasgeek.com or call 7676332020.

Hosted by

The Fifth Elephant - known as one the best #datascience and #machinelearning conference in Asia - is transitioning into a year-round forum for conversations about data and ML engineering; data science in production; data security and privacy practices. more

Supported by

Scribble Data builds feature stores for data science teams that are serious about putting models (ML, or even sub-ML) into production. The ability to systematically transform data is the single biggest determinant of how well these models do. Scribble Data streamlines the feature engineering proces… more

Promoted

Deep dives into privacy and security, and understanding needs of the Indian tech ecosystem through guides, research, collaboration, events and conferences. Sponsors: Privacy Mode’s programmes are sponsored by: more
Venkata Pingali

Venkata Pingali

@pingali

Past and Future of Feature Stores

Submitted May 11, 2021

Audience Level: Intermediate
Nature: Conceptual

https://drive.google.com/file/d/1WpKUXeC0i93f72vC8yXtUR6unPEaB4ma/view?usp=sharing

Scribble has built and operate feature stores for companies for the
past few years. This is a perspective talk on why feature stores came
about, what is being built today, and what we foresee over the next
few years.

  1. Feature store introduction and history

  2. Understanding existing feature stores
    (a) Architecture: Integrated/standalone
    (b) Scale: Peta/Tera
    (c) Core abstraction: SQL-like/program
    (d) Application scope: ML/Non-ML
    (e) Programming Iterface: Tight/Open
    (f) Data classes: Streaming/Timeseries, document, transactions

  3. Classes of decisions
    (a) What, Shallow why, Deep why, Why not
    (b) How these are addressed today & gaps

  4. Feature Stores 1.0: Passive, robust, scalable
    (a) Focused on ML usecases
    (b) Focus on scale & abstractions
    (c) Passive but robust

  5. Feature Stores 2.0: Intelligent, trusted, end-to-end
    (a) Context-aware - Integrates with upstream and downstream
    About data, nature of processing, risks involved
    Changes operations, resources, observation levels
    (b) Knowledge management - Help ip creation
    Better and efficient processes
    (c) Risk management - Trust and safety as first class goal
    Reduce risks from insecure, poor/changing code & data
    Change handling, impact assessment
    (d) Proactive - Actively observes and recommends
    Suggests features, impact assessment
    (e) Scope - Expanded classes of decisions and users
    All classes of advanced data needs (shallow why…)
    (f) Distributed - Handle constraints (time, volume etc)
    Data cannot/should not flow to centralized
    Distributed discovery

  6. Some niche contexts where new classes of
    stores might emerge:
    (a) Constrained devices (handhelds)
    (b) Classes of data (geospatial)
    (c) Computational complexity (1000s of models)

  7. Key Takeaways
    (a) Feature stores are now a standard component
    (b) Understanding the journey will help future-proof your implementation
    (c) Feature stores 2.0 will be different from 1.0

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Make a submission

Accepting submissions till 14 Jul 2021, 11:00 PM

Hosted by

The Fifth Elephant - known as one the best #datascience and #machinelearning conference in Asia - is transitioning into a year-round forum for conversations about data and ML engineering; data science in production; data security and privacy practices. more

Supported by

Scribble Data builds feature stores for data science teams that are serious about putting models (ML, or even sub-ML) into production. The ability to systematically transform data is the single biggest determinant of how well these models do. Scribble Data streamlines the feature engineering proces… more

Promoted

Deep dives into privacy and security, and understanding needs of the Indian tech ecosystem through guides, research, collaboration, events and conferences. Sponsors: Privacy Mode’s programmes are sponsored by: more