What is Amazon SageMaker and what are its top alternatives?
Amazon SageMaker is a fully managed service that enables developers and data scientists to build, train, and deploy machine learning models at scale. Its key features include built-in algorithms, automated model tuning, one-click deployment, and scalable training. However, some limitations of Amazon SageMaker include its cost which can be high for small scale projects, limited customization options, and dependency on the AWS infrastructure.
- Google Cloud AI Platform: Google Cloud AI Platform provides a collaborative environment for data science teams to build, train, and deploy machine learning models. Key features include built-in Jupyter notebooks, distributed training, hyperparameter tuning, and deployment flexibility. Pros include integration with other Google Cloud services, while the con is limited support for on-premises deployment.
- Databricks: Databricks is a unified analytics platform that offers capabilities for data engineering, data science, and machine learning. Key features include collaborative workspace, scalable data processing, and integrated machine learning libraries. Pros include seamless integration with Apache Spark, while the con is the pricing model based on usage.
- DataRobot: DataRobot is an automated machine learning platform that enables users to build and deploy machine learning models quickly. Key features include automated model selection, hyperparameter optimization, and model deployment. Pros include user-friendly interface, while the con is the lack of transparency in model building process.
- IBM Watson Studio: IBM Watson Studio is an integrated environment for data scientists, application developers, and business analysts to collaborate and build AI models. Key features include visual modeling tools, built-in deployment options, and data preparation capabilities. Pros include easy integration with IBM Cloud services, while the con is complex pricing structure.
- H2O.ai: H2O.ai offers an open source AI platform that provides machine learning algorithms and tools for data scientists and developers. Key features include automatic feature engineering, ensemble modeling, and scalability. Pros include open source community support, while the con is the learning curve for beginners.
- Azure Machine Learning: Azure Machine Learning is a cloud-based service for building, training, and deploying machine learning models. Key features include drag-and-drop interface, automated machine learning, and integration with Azure services. Pros include seamless integration with Microsoft ecosystem, while the con is limited support for on-premises deployment.
- SAS Viya: SAS Viya is an AI and analytics platform that provides tools for data preparation, modeling, and deployment. Key features include visual interfaces, open source integration, and model interpretability. Pros include rich analytics capabilities, while the con is the steep learning curve.
- BigML: BigML is a machine learning platform that offers tools for creating, evaluating, and deploying machine learning models. Key features include automated model evaluation, anomaly detection, and batch prediction. Pros include user-friendly interface, while the con is limited support for deep learning models.
- RapidMiner: RapidMiner is a data science platform that provides tools for data preparation, machine learning, and model deployment. Key features include visual workflow design, automated machine learning, and model validation. Pros include ease of use for non-technical users, while the con is the limited scalability for large datasets.
- KNIME: KNIME is an open source data analytics platform that allows users to create data science workflows using a visual interface. Key features include drag-and-drop workflow design, integration with various data sources, and machine learning extensions. Pros include open source community support, while the con is the lack of advanced machine learning algorithms compared to other platforms.
Top Alternatives to Amazon SageMaker
- Amazon Machine Learning
This new AWS service helps you to use all of that data you’ve been collecting to improve the quality of your decisions. You can build and fine-tune predictive models using large amounts of data, and then use Amazon Machine Learning to make predictions (in batch mode or in real-time) at scale. You can benefit from machine learning even if you don’t have an advanced degree in statistics or the desire to setup, run, and maintain your own processing and storage infrastructure. ...
- Databricks
Databricks Unified Analytics Platform, from the original creators of Apache Spark™, unifies data science and engineering across the Machine Learning lifecycle from data preparation to experimentation and deployment of ML applications. ...
- Azure Machine Learning
Azure Machine Learning is a fully-managed cloud service that enables data scientists and developers to efficiently embed predictive analytics into their applications, helping organizations use massive data sets and bring all the benefits of the cloud to machine learning. ...
- Kubeflow
The Kubeflow project is dedicated to making Machine Learning on Kubernetes easy, portable and scalable by providing a straightforward way for spinning up best of breed OSS solutions. ...
- TensorFlow
TensorFlow is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API. ...
- IBM Watson
It combines artificial intelligence (AI) and sophisticated analytical software for optimal performance as a "question answering" machine. ...
- H2O
H2O.ai is the maker behind H2O, the leading open source machine learning platform for smarter applications and data products. H2O operationalizes data science by developing and deploying algorithms and models for R, Python and the Sparkling Water API for Spark. ...
- Postman
It is the only complete API development environment, used by nearly five million developers and more than 100,000 companies worldwide. ...
Amazon SageMaker alternatives & related posts
Amazon Machine Learning
related Amazon Machine Learning posts
Which #IaaS / #PaaS to chose? Not all #Cloud providers are created equal. As you start to use one or the other, you'll build around very specific services that don't have their equivalent elsewhere.
Back in 2014/2015, this decision I made for SmartZip was a no-brainer and #AWS won. AWS has been a leader, and over the years demonstrated their capacity to innovate, and reducing toil. Like no other.
Year after year, this kept on being confirmed, as they rolled out new (managed) services, got into Serverless with AWS Lambda / FaaS And allowed domains such as #AI / #MachineLearning to be put into the hands of every developers thanks to Amazon Machine Learning or Amazon SageMaker for instance.
Should you compare with #GCP for instance, it's not quite there yet. Building around these managed services, #AWS allowed me to get my developers on a whole new level. Where they know what's under the hood. Where they know they have these services available and can build around them. Where they care and are responsible for operations and security and deployment of what they've worked on.
- Best Performances on large datasets1
- True lakehouse architecture1
- Scalability1
- Databricks doesn't get access to your data1
- Usage Based Billing1
- Security1
- Data stays in your cloud account1
- Multicloud1
related Databricks posts
From my point of view, both OpenRefine and Apache Hive serve completely different purposes. OpenRefine is intended for interactive cleaning of messy data locally. You could work with their libraries to use some of OpenRefine features as part of your data pipeline (there are pointers in FAQ), but OpenRefine in general is intended for a single-user local operation.
I can't recommend a particular alternative without better understanding of your use case. But if you are looking for an interactive tool to work with big data at scale, take a look at notebook environments like Jupyter, Databricks, or Deepnote. If you are building a data processing pipeline, consider also Apache Spark.
Edit: Fixed references from Hadoop to Hive, which is actually closer to Spark.
related Azure Machine Learning posts
- System designer9
- Google backed3
- Customisation3
- Kfp dsl3
- Azure0
related Kubeflow posts
Can you please advise which one to choose FastText Or Gensim, in terms of:
- Operability with ML Ops tools such as MLflow, Kubeflow, etc.
- Performance
- Customization of Intermediate steps
- FastText and Gensim both have the same underlying libraries
- Use cases each one tries to solve
- Unsupervised Vs Supervised dimensions
- Ease of Use.
Please mention any other points that I may have missed here.
We are trying to standardise DevOps across both ML (model selection and deployment) and regular software. Want to minimise the number of tools we have to learn. Also want a scalable solution which is easy enough to start small - eg. on a powerful laptop and eventually be deployed at scale. MLflow vs Kubernetes (Kubeflow)?
- High Performance32
- Connect Research and Production19
- Deep Flexibility16
- Auto-Differentiation12
- True Portability11
- Easy to use6
- High level abstraction5
- Powerful5
- Hard9
- Hard to debug6
- Documentation not very helpful2
related TensorFlow posts
Google Analytics is a great tool to analyze your traffic. To debug our software and ask questions, we love to use Postman and Stack Overflow. Google Drive helps our team to share documents. We're able to build our great products through the APIs by Google Maps, CloudFlare, Stripe, PayPal, Twilio, Let's Encrypt, and TensorFlow.
Why we built an open source, distributed training framework for TensorFlow , Keras , and PyTorch:
At Uber, we apply deep learning across our business; from self-driving research to trip forecasting and fraud prevention, deep learning enables our engineers and data scientists to create better experiences for our users.
TensorFlow has become a preferred deep learning library at Uber for a variety of reasons. To start, the framework is one of the most widely used open source frameworks for deep learning, which makes it easy to onboard new users. It also combines high performance with an ability to tinker with low-level model details—for instance, we can use both high-level APIs, such as Keras, and implement our own custom operators using NVIDIA’s CUDA toolkit.
Uber has introduced Michelangelo (https://eng.uber.com/michelangelo/), an internal ML-as-a-service platform that democratizes machine learning and makes it easy to build and deploy these systems at scale. In this article, we pull back the curtain on Horovod, an open source component of Michelangelo’s deep learning toolkit which makes it easier to start—and speed up—distributed deep learning projects with TensorFlow:
(Direct GitHub repo: https://github.com/uber/horovod)
IBM Watson
- Api4
- Prebuilt front-end GUI1
- Intent auto-generation1
- Custom webhooks1
- Disambiguation1
- Multi-lingual1
related IBM Watson posts
- Highly customizable2
- Very fast and powerful2
- Auto ML is amazing2
- Super easy to use2
- Not very popular1
related H2O posts
- Easy to use490
- Great tool369
- Makes developing rest api's easy peasy276
- Easy setup, looks good156
- The best api workflow out there144
- It's the best53
- History feature53
- Adds real value to my workflow44
- Great interface that magically predicts your needs43
- The best in class app35
- Can save and share script12
- Fully featured without looking cluttered10
- Collections8
- Option to run scrips8
- Global/Environment Variables8
- Shareable Collections7
- Dead simple and useful. Excellent7
- Dark theme easy on the eyes7
- Awesome customer support6
- Great integration with newman6
- Documentation5
- Simple5
- The test script is useful5
- Saves responses4
- This has simplified my testing significantly4
- Makes testing API's as easy as 1,2,34
- Easy as pie4
- API-network3
- I'd recommend it to everyone who works with apis3
- Mocking API calls with predefined response3
- Now supports GraphQL2
- Postman Runner CI Integration2
- Easy to setup, test and provides test storage2
- Continuous integration using newman2
- Pre-request Script and Test attributes are invaluable2
- Runner2
- Graph2
- <a href="http://fixbit.com/">useful tool</a>1
- Stores credentials in HTTP10
- Bloated features and UI9
- Cumbersome to switch authentication tokens8
- Poor GraphQL support7
- Expensive5
- Not free after 5 users3
- Can't prompt for per-request variables3
- Import swagger1
- Support websocket1
- Import curl1
related Postman posts
We just launched the Segment Config API (try it out for yourself here) — a set of public REST APIs that enable you to manage your Segment configuration. A public API is only as good as its #documentation. For the API reference doc we are using Postman.
Postman is an “API development environment”. You download the desktop app, and build API requests by URL and payload. Over time you can build up a set of requests and organize them into a “Postman Collection”. You can generalize a collection with “collection variables”. This allows you to parameterize things like username
, password
and workspace_name
so a user can fill their own values in before making an API call. This makes it possible to use Postman for one-off API tasks instead of writing code.
Then you can add Markdown content to the entire collection, a folder of related methods, and/or every API method to explain how the APIs work. You can publish a collection and easily share it with a URL.
This turns Postman from a personal #API utility to full-blown public interactive API documentation. The result is a great looking web page with all the API calls, docs and sample requests and responses in one place. Check out the results here.
Postman’s powers don’t end here. You can automate Postman with “test scripts” and have it periodically run a collection scripts as “monitors”. We now have #QA around all the APIs in public docs to make sure they are always correct
Along the way we tried other techniques for documenting APIs like ReadMe.io or Swagger UI. These required a lot of effort to customize.
Writing and maintaining a Postman collection takes some work, but the resulting documentation site, interactivity and API testing tools are well worth it.
Our whole Node.js backend stack consists of the following tools:
- Lerna as a tool for multi package and multi repository management
- npm as package manager
- NestJS as Node.js framework
- TypeScript as programming language
- ExpressJS as web server
- Swagger UI for visualizing and interacting with the API’s resources
- Postman as a tool for API development
- TypeORM as object relational mapping layer
- JSON Web Token for access token management
The main reason we have chosen Node.js over PHP is related to the following artifacts:
- Made for the web and widely in use: Node.js is a software platform for developing server-side network services. Well-known projects that rely on Node.js include the blogging software Ghost, the project management tool Trello and the operating system WebOS. Node.js requires the JavaScript runtime environment V8, which was specially developed by Google for the popular Chrome browser. This guarantees a very resource-saving architecture, which qualifies Node.js especially for the operation of a web server. Ryan Dahl, the developer of Node.js, released the first stable version on May 27, 2009. He developed Node.js out of dissatisfaction with the possibilities that JavaScript offered at the time. The basic functionality of Node.js has been mapped with JavaScript since the first version, which can be expanded with a large number of different modules. The current package managers (npm or Yarn) for Node.js know more than 1,000,000 of these modules.
- Fast server-side solutions: Node.js adopts the JavaScript "event-loop" to create non-blocking I/O applications that conveniently serve simultaneous events. With the standard available asynchronous processing within JavaScript/TypeScript, highly scalable, server-side solutions can be realized. The efficient use of the CPU and the RAM is maximized and more simultaneous requests can be processed than with conventional multi-thread servers.
- A language along the entire stack: Widely used frameworks such as React or AngularJS or Vue.js, which we prefer, are written in JavaScript/TypeScript. If Node.js is now used on the server side, you can use all the advantages of a uniform script language throughout the entire application development. The same language in the back- and frontend simplifies the maintenance of the application and also the coordination within the development team.
- Flexibility: Node.js sets very few strict dependencies, rules and guidelines and thus grants a high degree of flexibility in application development. There are no strict conventions so that the appropriate architecture, design structures, modules and features can be freely selected for the development.