Alternatives

Products that do what OrchestraML does

From English prompt to deployed ML model with human approval

1
OrchestraML▲4
Turn natural-language ML goals into auditable ML pipelines.
27d ago · orchestra-ml.vercel.app
2
ZenML▲84
Create reproducible machine learning pipelines
2020
3
AskCodi▲230
Custom LLMs, without training. Use via openai compatible api
Nov 2025
4
MakeML▲274
Train Neural Networks without a line of code
2019
5
StackML▲252
Machine Learning platform in-browser, for creators
2019
6
automl-docker▲68
Open-source, easily create ready-to-use ML models for NLP
2022
7
Google CLOUD AUTOML▲93
Train custom ML models with minimum effort and expertise
2018
8
Orchestra 1.0▲195
An open-source robotic process automation system 🚀
2019
9
UnionML▲77
The easiest way to build and deploy ML microservices
2022
10
Obviously AI AutoML▲276
The fastest, most precise no-code AI in the world.
2020
11
Label Studio 1.8.0 Release▲185
Open source data labelling platform for AI model tuning
2023
12
Layer AI▲124
Metadata store for production ML
2022
13
ScoopML▲81
An AI assistant for machine learning engineers
2020
14
Pioneer▲113
Fine-tune any LLM in minutes, with one prompt
Apr 2026
15
CRML▲130
CRML is a declaritive language for writing cyberrisk as code
Feb 2026
16
Elemeno AI-Ops▲67
Make machine learning work for you, not the other way around
2022
17EA
Eno – A lightning fast, user-friendly YAML/TOML alternative▲102
eno [1] - A modern plaintext language w/ libraries [2] for JavaScript, Python, Ruby & soon more! We migrated a big relational research database to a file-based solution - requirements were: - Super fast and easy editability for users - Highest performance for parsing/validating >10K documents on every user change. Our trials with YAML/TOML showed us that we wanted something both faster [3][4] and easier [4], something tailored for file-based content management ... and after months of research & development it's now publicly available (under MIT license) for everyone! Last but…
2018
18
Mi▲75
30-line zero-config CLI agent for bug fixes + refactoring
May 2026
19
Taylor AI▲118
Fine-tune open source LLMs in minutes
2023
20
integrate.ai▲80
Extend your product to train ML models on distributed data
2022
21ZE
ZenML, extensible open-source MLOps framework for reproducible ML▲37
2020 · github.com
22
dolv▲47
Your AI operator for content, CRM, and GTM execution
27d ago · dolv.work
23LA
LUML – an open source (Apache 2.0) MLOps/LLMOps platform▲7
Hi HN, We built LUML (https://github.com/luml-ai/luml), an open-source (Apache 2.0) MLOps/LLMOps platform that covers experiments, registry, LLM tracing, deployments and so on. It separates the control plane from your data and compute. Artifacts are self-contained. Each model artifact includes all metadata (including the experiment snapshots, dependencies, etc.), and it stays in your storage (S3-compatible or Azure). File transfers go directly between your machine and storage, and execution happens on compute nodes you host and connect to LUML. We’d love you to try…
Feb 2026 · github.com
24IM
I'm building a simple way to package, collaborate, and deploy ML models▲9
Heya HN, after spending +1 year building an ML-driven analytics product (that didn't pan out unfortunately), I've pivoted to solving a problem my team and I found while building the previous product … why the hell is it so hard to move a model from a Jupyter notebook, to a development server, then to a production pipeline!? To solve this my team and I started the open source KitOps project under the Apache 2 license. KitOps includes the Kit CLI that uses a Kitfile manifest to create ModelKits: 1. The kit CLI packages your model, datasets, code, and configuration into an OCI compliant…
2024 · kitops.ml

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