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What are machine learning models and how can they help?

There are 3 main machine learning models that can help accelerate your business in a variety of ways:

1. Automated machine learning models

The use of traditional machine learning methods to solve real-world business problems is time-consuming, resource-intensive, and challenging. Automated machine learning is an incremental shift in the way organizations approach machine learning and data science. The core objective is to make machine learning accessible and easy by generating a data analysis pipeline that includes pre-processing of data, a selection of features, and engineering methodologies along with machine learning methods and parameter settings that are optimized for your data sets.

2. Managed to compute

Just imagine an infrastructure that delivers secure, seamless and flexible support that your workloads require. Yes, with managed compute, you can experience a scalable infrastructure that balances on-and off-premise, enabling you to run at peak performance. This model helps you transform your IT operations through a cohesive set of services that could span cloud, network, and management. You can choose scalable, hybrid, private, or public cloud solutions and managed services that include managed hosting and multi-cloud management that helps you increase your business agility.

3. DevOps for machine learning models

Normally, there are tremendous synergies between DevOps and Machine Learning Analytics. DevOps is the process of bringing together business, development, release, and operational expertise to deliver a solution. Implementing DevOps for machine learning helps to solve both data science and data engineering problems. Allowing DevOps automation in machine learning projects accelerates the process of going from development to production. Integrating DevOps will help in achieving the following objectives:

• The time it takes to train the data will reduce with large data sets and increased accuracy rate. • The inference would be faster with the ability to rapidly retrain • Deployments would be faster • Increased capability to observe model behavior • Delivery would be more secure with an intense ability to identify anomalies • Better tracking of application delivery by making use of activity data from DevOps tools like Jira, Git, Jenkins, SonarQube, Puppet, Ansible

How can I incorporate machine learning into my business?

As time goes on it’s going to get harder and harder to be successful without using some sort of machine learning or artificial intelligence tool. It can be used for just about anything from managing the water level in your swimming pool to helping doctors diagnose diseases. Machine learning is advancing our everyday life at lightning speed. However, getting started with machine learning can be daunting and requires deep insights. Here is a 4-step process that can help you get started with Machine learning:

Step 1: Having a Clear Mindset Machine learning is a huge and exciting field of study where you can achieve impressive results and find solutions to very challenging real-time problems. First and foremost to get started with anything, you should prepare your mind. Do intensive homework and practice sample machine learning programs which are available online. If you understand what it does and your options then you will have a better understanding of how it can help your particular business.

Step 2: Picking up a Process The process we follow defines the quality of output. When it comes to machine learning, following a systematic process will help you achieve better results.

Define the problem: In this phase, you have to list down the problems along with the related assumptions. Also, validate the points on why you need to solve that problem and how you are going to do that.

Prepare data: Data preparation, analysis and summarizing plays a major role here. During this phase, you have to start collecting the data with which you have to preprocess the same and transform the processed data using features like using scaling, attribute decomposition and attribute aggregation.

Spot check algorithms: With this spot check method, you could find the best predictive models for a given data set. Following this approach provides a lot of information in a relatively short amount of time.

Improve and present results: After spot checking, it’s time to get the best results and present the findings to the stakeholders.

Step 3: Tool Selection There are numerous machine learning tools available in the tech market. The tools you choose makes the applied machine learning faster, easier and more fun. The best thing to do before choosing a machine learning tool is to segregate them into platforms and libraries. The difference between both is, a platform has and provides everything you need to run a machine learning project whereas with a library you will have discrete capabilities or parts of what you need to complete a project.

Examples of machine learning platforms: ♦ Alteryx ♦ H20.ai ♦ KNIME ♦ RapidMiner ♦ R Platform ♦ Teradata ♦ TensorFlow

Examples of machine learning libraries: ♦ scikit-learn in Python ♦ Keras ♦ JSAT in Java ♦ Accord Framework in .NET

Step 4: Continuous practices on data sets

Holding high-quality, real-world, and easily understood machine learning datasets to practice will keep your efforts going with more accurate and robust results. You will most likely have to spend some time measuring and collecting the data which in turn helps you acquire more accurate results. When it comes to different machine learning models, you need a development company that can properly build, train, and deploy these models. At SDI, we leverage the power of deep learning, natural language processing (NLP) and neural networks that could clone human decision-making abilities and allow for real-time applications of machine learning.

How to get Azure machine learning services for free

SMB’s can visualize viable and exponential growth with machine learning. However, they are unable to integrate this with their business due to cost, special coding skills, and hardware requirements.

Fortunately, Azure offers Azure Machine Learning to provide a robust environment for the development of Machine Learning solutions which allows Small and medium businesses to explore and find reliable solutions for their real-time business problems. You can register for free by following these basic steps:

1. Visit www.microsoft.com and sign in/sign up

2. Once you login to your Microsoft account, go to All Microsoft menu at the top and click the Microsoft Azure option from the available list of services.

3. Click the “Start Free” button and you will again be prompted to Sign in to your Microsoft account.

4. Enter the necessary details required. You will be asked to provide your personal details along with the credit card information where you don’t have to pay anything with your credit unless you upgrade your subscription.

5. Once you are signed in, “Go to the portal” page and you will be redirected to the Azure ML dashboard.

6. To access the Azure Machine Learning Studio, go here.

7. Once you do that you can start your Machine Learning Project.

Building an advanced analytics solution with an Azure Machine Learning Tool is more approachable and simple which does not require in-house expertise. It will also help you connect the data sets, algorithms, and modules. Need more insights and help with incorporating machine learning into your business? Contact Rob Lapointe at 408-802-2885 or rob@sdi.la.

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