We spend a lot of time focused on giving data scientists the best experience for deploying their machine learning models. We think they should not only use the best tools for the job, they should also be able to integrate their work easily with other tools. Today we’ll highlight one such integration: Jupyter Notebook.
When we work in Jupyter Notebook—an open-source project tool used for creating and sharing documents that contain live code snippets, visualizations, and markdown text—we are reminded how easy it is to use our API to deploy a machine learning model from within a notebook.
About Jupyter Notebook
These notebooks are popular among data scientists and are used both internally and externally to share information about data exploration, model training, and model deployment. Jupyter Notebook supports running both Python and R, two of the most common languages used by data scientists today.
How We Use It
We don’t think you should be limited to creating algorithms/models solely through our UI. Instead, to give data scientists a better and more comprehensive experience, we built an API endpoint that gives more control over your algorithms. You can create, update, and publish algorithms using our Python client, which lets data scientists deploy their models directly from their existing Jupyter Notebook workflows.
We put together the following walkthrough to help guide users through the process to deploy from within a notebook. The first few steps are shown below:
In this example, after setting up credentials, we download and prep a dataset and build and deploy a text classifier model. You can see the full example notebook here. And for more information about our API, please visit our guide.
More of a Visual Learner?
Watch this short demo that goes through the full workflow.
Two of the most interesting things potentially ever are happening in our lifetime: the rise of machine learning and the blockchain revolution.
Machine Learning (ML) systems have been able to surpass humans in many problem domains. These systems are now better at lip reading, speech recognition, location tagging, playing Go, image classification, and more.
With the invention of the blockchain and bitcoin, we’ve seen a wave of new cryptocurrencies and distributed applications built on these new blockchains.
The DanKu protocol is an overlap between the blockchain and Machine Learning. It helps facilitate exchanging ML models on the Ethereum blockchain. We even published a whitepaper about it here. You can read more about the DanKu protocol in our previous blog post.
Machine Learning algorithms are being developed and improved at an incredible rate, but are not necessarily getting more accessible to the broader community. That’s why today Algorithmia is announcing DanKu, a new blockchain-based protocol for evaluating and purchasing ML models on a public blockchain such as Ethereum. DanKu enables anyone to get access to high quality, objectively measured machine learning models. At Algorithmia, we believe that widespread access to algorithms and deployment solutions is going to be a fundamental building block of a balanced future for AI, and DanKu is a step towards that vision.
The DanKu protocol utilizes blockchain technology via smart contracts. The contract allows anyone to post a data set, an evaluation function, and a monetary reward for anyone who can provide the best trained machine learning model for the data. Participants train deep neural networks to model the data, and submit their trained networks to the blockchain. The blockchain executes these neural network models to evaluate submissions, and ensure that payment goes to the best model.
The contract allows for the creation of a decentralized and trustless marketplace for exchanging ML models. This gives ML practitioners an opportunity to monetize their skills directly. It also allows any participant or organization to solicit machine learning models from all over the world. This will incentivize the creation of better machine learning models, and make AI more accessible to companies and software agents. Anyone with a dataset, including software agents can create DanKu contracts.
We’re also launching the first DanKu competition for a machine learning problem. Read More…
We host more than 4000 algorithms for over 50k developers. Here is a list of best practices we’ve identified for designing advanced algorithms. We hope this can help you and your team. Read More…
We often get asked about if we’re planning on adding any non-English NLP algorithms. As much as we would love to train NLP models on other languages, there aren’t many usable training datasets in these languages. And, due to the linguistic structure of these languages, training with pre-existing approaches doesn’t always give the best results.
Until better training sets can be generated, one passable solution is to translate the text to English before sending it to the algorithm. Read More…