Before diving straight into MLOps tools, you need to understand what MLOps is, the workflow involved in a MLOps project, the various algorithms involved, and how one can take their model into deployment.
MLOps is the process of executing a machine learning project successfully. MLOps is not anything like Devops because DevOps is a linear action; it pushes code to production. Whereas MLOps is a loop action that does not end with the deployment but continues towards retraining and redeployment.
The various steps involved in a machine learning project:
The above is the standard steps involved in any machine learning project. We have divided the steps into the following stages, and we will talk in detail about MLOps tools stacks available in each stage.
By definition, MLOps tools are single-use software or end-to-end platform that helps you execute a stage or an entire machine learning project. All the MLOps tools serve a particular purpose, but if you look at the bigger picture, they collectively work towards solving a real-world problem through data science.
Your tool stack should reduce the time you spend figuring it out and increase your time in solving a business problem. Put together tools that help you in each stage of the ML workflow, and you have a MLOps tool stack that streamlines your MLOps pipeline and makes pushing your models into production quicker and easier.
No matter how many demos you sit down to, numerous hours contemplating the right fit won’t be sufficient for you to decide whether the tool will serve its purpose. Fortunately, there are ways you can decide whether the tool will be a right fit for your pipeline.
Some considerations before choosing a MLOps tool, it should not lock you in a single platform; you should be able to expand as necessary or cut down when needed. It must be cloud-agnostic; this is a given no argument in that. Finally, the MLOps tools you’re looking for should include various libraries and support multiple languages.
You can use several MLOps tools in various stages of your machine learning project workflow. We have divided them into six groups based on the workflow stage they cater to.
In this article, we will go through various stages in the MLOps pipeline and the best MLOps tools available in each step of the pipeline. We will also go through the features, screenshots, and what makes the tool stand out from the rest.
The first stage of any machine learning project is deciding on the framework we will use. ML frameworks let data scientists and developers build and deploy models faster. Let us take a look at some of the best MLOps tools available to us in this phase.
Hugging Face is an open-source machine learning framework focusing on AI ethics and easy-to-deploy tools. Clément Delangue and Julien Chaumond founded Hugging Face in 2016 as a chatbot company. Hugging Face is offered in two categories, one as an open-source platform and the other as a subscription-based NLP feature.
Hugging Face is famous for a couple of reasons:
Some of the noteworthy features of Hugging Face are:
PyTorch was created inside the Facebook research lab by Facebook AI Research in 2017. Since then, it has become quite popular with data scientists and machine learning engineers because of its flexibility and speed.
PyTorch is a deep learning tensor library built on Python and Torch. It uses Pythonic, a dynamic computation graph that allows us to run codes in real-time.
Features of PyTorch are:
The Google Brain team developed TensorFlow in 2015. It is an open-source framework for mathematical computation, making machine learning and developing neural networks faster. TensorFlow uses python to provide an API for building applications. For example, TensorFlow allows developers to create graphs that would enable them to see how data moves through the graph, called data graphs.
Features of TensorFlow:
Grid.ai is a framework that lets data scientists train models on the cloud at scale. Founded by William Falcon and Luis Capelo in 2019, it enables people without ML engineering or MLOps experience to develop ML models.
Features of Grid.Ai:
Distributed computing is multiple nodes of machine learning algorithms and systems that improve performance, increase accuracy, and scale with large datasets. First, look at some of the top MLOps tools available in this category.
Anyscale is a fully scalable distributed computing platform that makes it easier for anyone to build, deploy, and manage scalable ML applications on Ray. It is a framework for building machine learning frameworks and has two sets of libraries, one for new workloads and the second for replacing existing libraries.
Features that make anyscale standout:
Coiled lets you quickly scale your Python applications by making them cloud agnostic. Founded in 2020 by Matthew Rocklin, Hugo Bowne-Anderson, and Rami Chowdhury, Coiled has recently announced public availability in the DASK distributed summit. Their DASK projects vary from machine learning pipelines to demand forecasting and modeling.
Prominent features of Coiled:
Dask is an open-source python library for parallel computing that scales python applications from local systems to large distributed clusters in the cloud. Dask also makes it easier to work with Numpy, Pandas, and Scikit-learn. Also, it is a framework used to build distributed applications with systems like XGBoost, PyTorch, Prefect, Airflow, and Rapids.
Critical features of DASK:
Apache Spark is a MLOps tool that is used for data processing that can process large amounts of datasets faster and more efficiently than Hadoop. In addition, it can also distribute data processing to multiple systems on its own or using distributed computing tools.
Founded in AMPLab at U.C. Berkeley in 2009, Apache Spark has been the go-to framework for processing big data. Apache Spark supports SQL and graph processing and binds with Java, Scala, Python, and R programming languages.
Here’s why Apache Spark shines over the rest:
Model evaluation and experiment tracking in machine learning are tracking and saving all the information regarding the experiments you have added to your training. In addition, model evaluation and experiment tracking help us to track the model performance, compare versions and select the ideal version for deployment.
Let us go over some of the top MLOps tools available in this segment.
Weights & Biases, also known as W&B, is an open-source MLOps tool for performance tracking and visualization in a machine learning project. They organize and analyze your experiments and save the model’s hyperparameters and metrics. They also provide training, model comparisons, and accuracy tracking visualization charts.
Features of W&B:
Comet ML is a MLOps tool founded by Gideon Mendels in 2017, used to track, organize, compare, and visualize the performance of experiments in a machine learning project. They also help us keep track of performance history, code changes, and production models. Comet ML is also moving towards an automated ML approach by adding predictive early stopping and neural architecture search.
Promising features of Comet ML:
Iterative.ai is a git-based MLOps tool for data scientists and ML engineers with DVC (data version control) and CML (continuous machine learning). Iterative.ai was created by Dmitry Petrov while working as a Microsoft data scientist, aiming to bring engineering practices to data science and machine learning.
Key features of iterative.ai are:
MLflow is an open source platform built on an open interface philosophy helping us manage certain aspects of the machine learning workflow. So, any data scientists working with any framework, supported or unsupported, can use the open interface, integrate with the platform, and start working.
There are four main features in MLflow:
Model deployment in machine learning is the stage where we deploy our trained models into production. This enables the model to serve its purpose of predicting results as it was intended to do so. For a complete guide to model deployment, you can read our blog here.
Now, we look into various MLOps tools for model deployment in machine learning.
Creators of Apache TVM spun out of the University of Washington and created OctoML to help companies to develop and deploy deep learning models in specific hardware as needed. OctoML supports a variety of machine learning frameworks, such as PyTorch and TensorFlow.
Features of OctoML are:
BentoML is an end-to-end platform solution for model serving. It gives the data scientist the power to develop production-ready models, with best practices from DevOps and optimization at each stage. Furthermore, its standard and easy architecture simplify the building of production-ready models.
Key features of BentoML are:
Seldon is an open-source platform that helps data scientists and ML engineers to solve problems faster and effectively through audit trails, advanced experiments, CI/CD, scaling, model updates, and more. In addition, Seldon converts ML models or language wrappers into containerized product microservices.
Prominent features of Seldon:
Wallaroo is a MLOps tool that helps in model deployment. The platform consists of four main components, MLOps, process engine, data connectors, and audit and performance reports. Wallaroo allows data scientists to deploy models against live data to testing, staging, and production using machine learning frameworks.
Four main features of Wallaroo:
Post-deployment, model monitoring, and management play a vital role in the MLOps pipeline. However, the reality from test data to actual data is a vast difference, and data drifts and performance degradation are common. This is where MLOps differs from DevOps; model monitoring is a huge task, and fortunately, MLOps tools are available to solve this problem.
Arize is leading the model performance monitoring space. It’s a full-stack platform designed to solve daily pain points and bottlenecks faced by data scientists and ML engineers. Arize detects errors and data drifts when they appear, analyzes why the error occurred, and improves the model’s overall performance.
Features of Arize are:
Arthur AI is a machine learning performance platform that monitors model performance, bias detection, and explainability. The platform enables data scientists, ML engineers, and developers to detect errors, data drift, and anomalies.
Features of Arthur AI:
Setup and implementation with just a few lines of code. Platform agnostic fits right into your workflow and has a unified dashboard. Monitor models at one instance, collaborate with stakeholders on the platform and set up alerts to detect data drifts as it happens.
Fiddler AI is a model performance management platform that gives us a common language, centralized controls, and actionable insights into the performance of the models. The platform also auto-generates valuable real-time insights into incidents and enables users to perform a complete analysis, including bias and fairness.
Features of Fiddler AI:
WhyLabs is the conjunction of data observability and MLOps into a single platform. WhyLabs aims to reduce the time spent on error identification and solving. “The goal of the company is to first build a data observability platform for data and machine learning,” says Andy Dang, head of engineering and co-founder of Whylabs.
Key features of WhyLabs are:
These platforms offer a comprehensive solution covering the entire machine learning pipeline spectrum. In addition, these platforms provide a one-stop solution for all, from data and pipeline, model experimentations, and hyperparameter tuning, to deployment and monitoring.
NimbleBox.ai is a complete MLOps platform that enables data scientists and ML engineers to build, deploy, and allocate jobs to their machine learning projects. The four core components of NimbleBox are their Build, Jobs, Deploy and Manage components. These features let anyone start their machine learning project with just a few lines of code and push their model deployment in the easiest ways.
Features of NimbleBox.ai are:
Domino Data Lab is a fully-fledged MLOps platform that enables data scientists and ML engineers to develop and deploy machine learning models focusing on data governance and collaboration.
Features of Domino Data Lab are:
dataBricks is a big data processing platform that integrates data science, engineering, and business across the machine learning project lifecycle. The creators of Apache Spark founded it as an alternative to the MapReduce system. DataBricks accelerates development by unifying the pipelines involved in the development process.
Features of DataBricks are:
DataRobot is a MLOps platform used to build and deploy machine learning models. They also provide a built-in library of algorithms and prebuilt prototypes for feature extraction and data prep. It also offers automation in feature selection, algorithms, and parameter values.
Features of DataRobot are:
ZenML is an open-source MLOps framework tool that reproduces machine learning pipelines and gives us a production-ready MLOps tool. Two things make ZenML stand out from the rest, a very good Python library and third-party integrations. ZenML’s python library helps data scientists to kickstart their MLOps faster, and their integration lets them do everything from anywhere.
Features of ZenML:
No matter your requirement in MLOps, there are tools for MLOps for every need. These tools enable data scientists and engineers to develop, train, deploy, and monitor models from machine learning frameworks to entire platforms.
In an era where we still debate the importance of developer tools and whether we can build our ML pipeline in-house, these tools certainly give us an advantage in delivering what is asked of us. These indeed are exciting times to be a data scientist.