The best of both worlds: Combining Python and Kotlin for Machine Learning
Bridge the gap between data science and engineering. Use Python for ML experimentation and Kotlin for robust, production-ready APIs.
#1about 5 minutes
The production gap in machine learning
Most machine learning models fail to reach production due to the disconnect between data scientists and software engineers, and the complex MLOps lifecycle required.
#2about 8 minutes
Data exploration and analysis with Kotlin in Jupyter
Use the Kotlin kernel in Jupyter notebooks with libraries like DataFrame and Let's Plot to perform type-safe data analysis and visualization.
#3about 3 minutes
Building neural networks with the Kotlin DL library
Define and train a neural network model using the Kotlin DL library, but be aware of current limitations like incompatibility with ARM-based Macs.
#4about 4 minutes
Deploying ML models as a web service with Ktor
Serve a pre-trained ONNX machine learning model with a lightweight web service using the Ktor framework for easy integration into production systems.
#5about 3 minutes
Choosing between Python and Kotlin for ML tasks
Use Python for its mature ecosystem in model development and experimentation, while leveraging Kotlin's type safety and performance for data pipelines and model serving.
#6about 2 minutes
Q&A on Kotlin for machine learning
The speaker answers audience questions about Kotlin DataFrame internals, integration with other frameworks, and the connection between the Kotlin and Python ecosystems.
Related jobs
Jobs that call for the skills explored in this talk.
Matching moments
00:40 MIN
Using multiple languages from Java to Python
Coffee with Developers - CODE100 Manchester challenger Gbenga Oladipupo
02:58 MIN
Introduction to Kotlin and its Java interoperability
Route from Java to Kotlin
02:18 MIN
Using data management and open source tools for MLOps
MLOps - What’s the deal behind it?
03:35 MIN
The challenge of moving machine learning to production
The state of MLOps - machine learning in production at enterprise scale
01:29 MIN
Overview of the data and machine learning tech stack
Empowering Retail Through Applied Machine Learning
10:46 MIN
Navigating the machine learning project lifecycle
Intelligent Automation using Machine Learning
01:33 MIN
The convergence of ML and DevOps in MLOps
AI Model Management Life Circles: ML Ops For Generative AI Models From Research to Deployment
06:17 MIN
Running LLMs in production with Kubeflow and KServe
DevOps for AI: running LLMs in production with Kubernetes and KubeFlow
All the videos of Halfstack London 2024!Last month was Halfstack London, a conference about the web, JavaScript and half a dozen other things. We were there to deliver a talk, but also to record all the sessions and we're happy to share them with you. It took a bit as we had to wait for th...
Benedikt Bischof
MLops – Deploying, Maintaining And Evolving Machine Learning Models in ProductionWelcome to this issue of the WeAreDevelopers Live Talk series. This article recaps an interesting talk by Bas Geerdink who gave advice on MLOps.About the speaker:Bas is a programmer, scientist, and IT manager. At ING, he is responsible for the Fast...
Benedikt Bischof
MLOps – What’s the deal behind it?Welcome to this issue of the WeAreDevelopers Live Talk series. This article recaps an interesting talk by Nico Axtmann who introduced us to MLOpsAbout the speaker:Nico Axtmann is a seasoned machine learning veteran. Starting back in 2014 he observed ...
Benedikt Bischof
MLOps And AI Driven DevelopmentWelcome to this issue of the WeAreDevelopers Dev Talk Recap series. This article recaps an interesting talk by Natalie Pistunovic who spoke about the development of AI and MLOps. What you will learn:How the concept of AI became an academic field and ...
From learning to earning
Jobs that call for the skills explored in this talk.
28:22
34:21
52:37
29:24
44:40
45:45
22:07
21:01
.gif?w=240&auto=compress,format)
.gif?w=240&auto=compress,format)
.gif?w=240&auto=compress,format)
