Machine Learning Tools to Facilitate ML Model Development

Ranging from business intelligence to the implementation of autonomous IoT systems across industrial processes, machine learning has enabled tremendous advancements in the way businesses operate. Organizations have ramped up their preparedness to assimilate ML-enabled solutions across their processes.

However, there is still some concern surrounding the development of machine learning models in an effective, streamlined way. A report by IBM highlighted that 25% of enterprises believe the lack of tools or platforms to develop high-quality ML models is one of the key barriers to the adoption of AI implements in the organization.

Let’s look at some high-efficiency machine learning tools you can use to expedite the ML model development process at your enterprise.

Machine Learning Development Tools

Model deployment tools or machine learning tools aid the development of performant ML models and increase the efficiency by a manifold. Let's have a look at a few of them in detail. 

1. Integrated Development Environments (IDEs)

Integrated Development Environments, or IDEs for machine learning, are a package of tools that enable developers to edit, build, test, and package ML code into one application. IDEs come bundled with tools and features like code editors, compilers (or interpreters), debuggers, build automation tools, etc.

Some popular multilanguage IDEs for machine learning are Eclipse, Komodo, Aptana, and Geany. Professionals have the option to either download IDEs to local hardware or access the development environment hosted on the cloud through their web browsers.

2. Version Control Systems (VCS)

The development of machine learning models requires several revisions and do-overs to achieve models that perform desirably. This necessitates the use of Version Control Systems in ML that enable professionals to keep track of changes made to a code or a set of files over a period of time.

Version Control Systems maintain an official record of every single change made to the source code, including authorship, timestamps, and other change details. Version Control Systems in ML make it simpler for developers to collaborate with each other more transparently.

3. Data Preparation Tools

A recent IBM report highlighted that data complexity was voted as one of the most daunting barriers to AI adoption by 24% of businesses. It is thus necessary to implement tools to facilitate efficient data preparation for machine learning development processes.

Data preparation tools enable ML professionals to explore, combine, clean, transform, enrich, and share curated datasets while creating machine learning models. It ensures that the training datasets that go into ML algorithms are consistently of high quality and accurate.

Some of the most popular data preparation tools are Tableau Prep, Talend, and Alteryx.

4. Machine Learning Libraries and Frameworks

ML libraries and frameworks are resources for machine learning developers that provide a wide range of functionalities and capabilities for ML development.

Machine learning libraries are a set of algorithms that enable programmers to train and test their ML models, and to make predictions and data-driven decisions through these models. Some popular ML libraries are MLib, FANN, and TensorFlow.

Similarly, machine learning frameworks are tools that enable professionals to build ML models without needing to understand the algorithms that go into them. The developers don’t need to get down with math and stats to build ML models as they are already present in the framework and ready to be used. Some popular frameworks are Scikit-learn, Apache SINGA, and TensorFlow.

5. Model Deployment Tools

The process of model deployment involves putting a developed, tested machine-learning model into production. Through model deployment, an organization makes the outputs available and accessible to users and systems across the enterprise. The right model deployment tools enable enterprises to track the use of the ML model and its performance with respect to set tolerances and benchmarks.

Some popular model deployment tools are Cortex, SageMaker, Gradio, and Azure ML.

6. AutoML (Automated Machine Learning) Platforms

The AutoML platform is a library of a wide range of tools and functionalities that enable ML professionals to perform diverse tasks with their ML models. The platform helps automate the development of machine learning models, helping make it more accessible to people with limited expertise and experience in the field.

AutoML platform helps save time and resources when developing and deploying ML models.

7. Explainability and Interpretability Tools

Explainability and interpretability in ML refer to the process of enhancing the comprehensibility of the predictions and results generated by an ML model in human terms. These tools, therefore, are implemented to help understand the backstage processes and computations that an ML model uses to arrive at a prediction or conclusion.

Using explainability and interpretability tools enables programmers to better finetune the ML models to perform desirably. Explainable AI is one popular tool by Google in this niche.

8. Collaboration and Documentation Tools

Collaboration tools for data science facilitate team-based project execution, allowing all the involved members to work on the code or different aspects of ML model development together. They enable resource sharing, version tracking, sharing, and annotation features, helping teams to stay on the same page.

Documentation tools are primarily used to understand the usage, assembly, collection, automation, and lifecycle of datasets in a machine learning system.

9. Model Monitoring and Management Tools

One of the most important tools that machine learning ecosystems use is model monitoring and management tools. Model monitoring in machine learning is an automatable process that consistently monitors model performance for parameters like data drifts, data quality, outliers, model performance, concept drift, etc.

ML model management tools can then be leveraged by professionals to redevelop or tweak their models through validating and retraining the ML models.

10. Cloud Platforms for ML

If your aim is to steadily scale your ML operations as you develop more and more models, it is best to use cloud platforms for ML development. They enable businesses to set up an experimentation sandbox to test for scaling needs, production, and demand for growing capabilities. Server loads can be variably adjusted based on need, reducing development and deployment costs.

Some examples are Amazon SageMaker and Azure Machine Learning.

11. Security and Ethical AI Tools

Training machine learning models is fraught with risks that stem from feeding them real-world data. The model could end up learning biases and prejudices, risking output accuracy. It is essential to employ tools like Audit-AI for bias testing of every model that your organization develops.

Additionally, you also need to consider implementing AI security tools to protect the deployment or model-sharing process. Tools like Rebuff help detect PI attacks that attempt to manipulate model outputs.

Conclusion

Machine learning development processes can benefit tremendously from selecting the right tools to augment tasking. Right from data collection, model development and training, testing, deployment, and security, every step can be streamlined using a variety of machine learning tools.

Businesses can leverage the power of MarkovML's AI powered platform to design, build, and test ML-based applications with high efficiencies. The platform provides extensive toolsets and features to help professionals work with data, intelligence, analytics, monitoring, and collaboration.

MarkovML's state-of-the-art workflow automation empowers businesses to save time, effort, and manpower on exhaustive, repetitive tasks. Visit MarkvoML to understand how the platform can help your organization's machine-learning ventures.